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| textbook of parametric and nonparametric statistics: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Edition David J. Sheskin, 2020-06-09 Following in the footsteps of its bestselling predecessors, the Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Edition provides researchers, teachers, and students with an all-inclusive reference on univariate, bivariate, and multivariate statistical procedures.New in the Fifth Edition:Substantial updates and new material th |
| textbook of parametric and nonparametric statistics: A Parametric Approach to Nonparametric Statistics Mayer Alvo, Philip L. H. Yu, 2018-10-12 This book demonstrates that nonparametric statistics can be taught from a parametric point of view. As a result, one can exploit various parametric tools such as the use of the likelihood function, penalized likelihood and score functions to not only derive well-known tests but to also go beyond and make use of Bayesian methods to analyze ranking data. The book bridges the gap between parametric and nonparametric statistics and presents the best practices of the former while enjoying the robustness properties of the latter. This book can be used in a graduate course in nonparametrics, with parts being accessible to senior undergraduates. In addition, the book will be of wide interest to statisticians and researchers in applied fields. |
| textbook of parametric and nonparametric statistics: All of Nonparametric Statistics Larry Wasserman, 2006-09-10 There are many books on various aspects of nonparametric inference such as density estimation, nonparametric regression, bootstrapping, and wavelets methods. But it is hard to ?nd all these topics covered in one place. The goal of this text is to provide readers with a single book where they can ?nd a brief account of many of the modern topics in nonparametric inference. The book is aimed at master’s-level or Ph. D. -level statistics and computer science students. It is also suitable for researchersin statistics, machine lea- ing and data mining who want to get up to speed quickly on modern n- parametric methods. My goal is to quickly acquaint the reader with the basic concepts in many areas rather than tackling any one topic in great detail. In the interest of covering a wide range of topics, while keeping the book short, I have opted to omit most proofs. Bibliographic remarks point the reader to references that contain further details. Of course, I have had to choose topics to include andto omit,the title notwithstanding. For the mostpart,I decided to omit topics that are too big to cover in one chapter. For example, I do not cover classi?cation or nonparametric Bayesian inference. The book developed from my lecture notes for a half-semester (20 hours) course populated mainly by master’s-level students. For Ph. D. |
| textbook of parametric and nonparametric statistics: Nonparametric Statistics for Non-Statisticians Gregory W. Corder, Dale I. Foreman, 2011-09-20 A practical and understandable approach to nonparametric statistics for researchers across diverse areas of study As the importance of nonparametric methods in modern statistics continues to grow, these techniques are being increasingly applied to experimental designs across various fields of study. However, researchers are not always properly equipped with the knowledge to correctly apply these methods. Nonparametric Statistics for Non-Statisticians: A Step-by-Step Approach fills a void in the current literature by addressing nonparametric statistics in a manner that is easily accessible for readers with a background in the social, behavioral, biological, and physical sciences. Each chapter follows the same comprehensive format, beginning with a general introduction to the particular topic and a list of main learning objectives. A nonparametric procedure is then presented and accompanied by context-based examples that are outlined in a step-by-step fashion. Next, SPSS® screen captures are used to demonstrate how to perform and recognize the steps in the various procedures. Finally, the authors identify and briefly describe actual examples of corresponding nonparametric tests from diverse fields. Using this organized structure, the book outlines essential skills for the application of nonparametric statistical methods, including how to: Test data for normality and randomness Use the Wilcoxon signed rank test to compare two related samples Apply the Mann-Whitney U test to compare two unrelated samples Compare more than two related samples using the Friedman test Employ the Kruskal-Wallis H test to compare more than two unrelated samples Compare variables of ordinal or dichotomous scales Test for nominal scale data A detailed appendix provides guidance on inputting and analyzing the presented data using SPSS®, and supplemental tables of critical values are provided. In addition, the book's FTP site houses supplemental data sets and solutions for further practice. Extensively classroom tested, Nonparametric Statistics for Non-Statisticians is an ideal book for courses on nonparametric statistics at the upper-undergraduate and graduate levels. It is also an excellent reference for professionals and researchers in the social, behavioral, and health sciences who seek a review of nonparametric methods and relevant applications. |
| textbook of parametric and nonparametric statistics: Textbook of Parametric and Nonparametric Statistics Vimala Veeraraghavan, Suhas Shetgovekar, 2019-01-17 This unique textbook guides students and researchers of social sciences to successfully apply the knowledge of parametric and nonparametric statistics in the collection and analysis of data. This book comprehensively covers all the methods of parametric and nonparametric statistics such as correlation and regression, analysis of variance, test construction, one-sample test to k-sample tests, etc. The two methods of statistics are presented simultaneously, with indication of their use in data analysis. Through adequate knowledge of both techniques, readers can select the appropriate method of testing as well as the graphical method for representing the data. Key Features • Provides a comparative account of the two statistical methodologies: why, when and how to use either of the two methods • Enables the readers to consider and identify the nature of data and apply the most suitable test • Exclusive chapter on SPSS and Excel-based statistical analysis of data • Special coverage of the use of statistics in psychology and psychological test construction • Contains worked-out problems to help students and scholars for better understanding of the concepts and further practice |
| textbook of parametric and nonparametric statistics: Handbook of Parametric and Nonparametric Statistical Procedures David J. Sheskin, 2000-02-24 Called the bible of applied statistics, the first edition of the bestselling Handbook of Parametric and Nonparametric Statistical Procedures was unsurpassed in its scope. The Second Edition goes even further - more tests, more examples, more than 250 pages of new material. Thorough - Up-To-Date With details of more than 100 statistical procedures, the Handbook offers unparalleled coverage of modern statistical methods. You get in-depth discussion of both practical and theoretical issues, many of which are not addressed in conventional statistics books. Practical - User-Friendly Accessible to novices but valuable to seasoned researchers, the Handbook emphasizes application over theory and presents the procedures in a standardized format that makes it easy to access the information you need. If you have to Ø Decide what method of analysis to use Ø Use a particular test for the first time Ø Distinguish acceptable from unacceptable research Ø Interpret the results of published studies the Handbook of Parametric and Nonparametric Statistical Procedures has the background, the answers, and the guidelines to get the job done. |
| textbook of parametric and nonparametric statistics: Introduction to Nonparametric Statistics for the Biological Sciences Using R Thomas W. MacFarland, Jan M. Yates, 2016-07-06 This book contains a rich set of tools for nonparametric analyses, and the purpose of this text is to provide guidance to students and professional researchers on how R is used for nonparametric data analysis in the biological sciences: To introduce when nonparametric approaches to data analysis are appropriate To introduce the leading nonparametric tests commonly used in biostatistics and how R is used to generate appropriate statistics for each test To introduce common figures typically associated with nonparametric data analysis and how R is used to generate appropriate figures in support of each data set The book focuses on how R is used to distinguish between data that could be classified as nonparametric as opposed to data that could be classified as parametric, with both approaches to data classification covered extensively. Following an introductory lesson on nonparametric statistics for the biological sciences, the book is organized into eight self-contained lessons on various analyses and tests using R to broadly compare differences between data sets and statistical approach. |
| textbook of parametric and nonparametric statistics: Nonparametric Statistics Gregory W. Corder, Dale I. Foreman, 2014-04-14 “...a very useful resource for courses in nonparametric statistics in which the emphasis is on applications rather than on theory. It also deserves a place in libraries of all institutions where introductory statistics courses are taught. –CHOICE This Second Edition presents a practical and understandable approach that enhances and expands the statistical toolset for readers. This book includes: New coverage of the sign test and the Kolmogorov-Smirnov two-sample test in an effort to offer a logical and natural progression to statistical power SPSS® (Version 21) software and updated screen captures to demonstrate how to perform and recognize the steps in the various procedures Data sets and odd-numbered solutions provided in an appendix, and tables of critical values Supplementary material to aid in reader comprehension, which includes: narrated videos and screen animations with step-by-step instructions on how to follow the tests using SPSS; online decision trees to help users determine the needed type of statistical test; and additional solutions not found within the book. |
| textbook of parametric and nonparametric statistics: Nonparametric Statistical Methods Using R John Kloke, Joseph W. McKean, 2014-10-09 A Practical Guide to Implementing Nonparametric and Rank-Based Procedures Nonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses, including estimation and inference for models ranging from simple location models to general linear and nonlinear models for uncorrelated and correlated responses. The authors emphasize applications and statistical computation. They illustrate the methods with many real and simulated data examples using R, including the packages Rfit and npsm. The book first gives an overview of the R language and basic statistical concepts before discussing nonparametrics. It presents rank-based methods for one- and two-sample problems, procedures for regression models, computation for general fixed-effects ANOVA and ANCOVA models, and time-to-event analyses. The last two chapters cover more advanced material, including high breakdown fits for general regression models and rank-based inference for cluster correlated data. The book can be used as a primary text or supplement in a course on applied nonparametric or robust procedures and as a reference for researchers who need to implement nonparametric and rank-based methods in practice. Through numerous examples, it shows readers how to apply these methods using R. |
| textbook of parametric and nonparametric statistics: Bayesian Nonparametrics J.K. Ghosh, R.V. Ramamoorthi, 2006-05-11 This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. It will also appeal to statisticians in general. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian non-parametrics. |
| textbook of parametric and nonparametric statistics: Theory of Nonparametric Tests Thorsten Dickhaus, 2018-03-27 This textbook provides a self-contained presentation of the main concepts and methods of nonparametric statistical testing, with a particular focus on the theoretical foundations of goodness-of-fit tests, rank tests, resampling tests, and projection tests. The substitution principle is employed as a unified approach to the nonparametric test problems discussed. In addition to mathematical theory, it also includes numerous examples and computer implementations. The book is intended for advanced undergraduate, graduate, and postdoc students as well as young researchers. Readers should be familiar with the basic concepts of mathematical statistics typically covered in introductory statistics courses. |
| textbook of parametric and nonparametric statistics: All of Statistics Larry Wasserman, 2004-09-17 This book is for people who want to learn probability and statistics quickly. It brings together many of the main ideas in modern statistics in one place. The book is suitable for students and researchers in statistics, computer science, data mining and machine learning. This book covers a much wider range of topics than a typical introductory text on mathematical statistics. It includes modern topics like nonparametric curve estimation, bootstrapping and classification, topics that are usually relegated to follow-up courses. The reader is assumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. The text can be used at the advanced undergraduate and graduate level. Larry Wasserman is Professor of Statistics at Carnegie Mellon University. He is also a member of the Center for Automated Learning and Discovery in the School of Computer Science. His research areas include nonparametric inference, asymptotic theory, causality, and applications to astrophysics, bioinformatics, and genetics. He is the 1999 winner of the Committee of Presidents of Statistical Societies Presidents' Award and the 2002 winner of the Centre de recherches mathematiques de Montreal–Statistical Society of Canada Prize in Statistics. He is Associate Editor of The Journal of the American Statistical Association and The Annals of Statistics. He is a fellow of the American Statistical Association and of the Institute of Mathematical Statistics. |
| textbook of parametric and nonparametric statistics: Robust Nonparametric Statistical Methods Thomas P. Hettmansperger, Joseph W. McKean, 1998 Offering an alternative to traditional statistical procedures which are based on least squares fitting, the authors cover such topics as one and two sample location models, linear models, and multivariate models. Both theory and applications are examined. |
| textbook of parametric and nonparametric statistics: Nonparametric and Semiparametric Models Wolfgang Karl Härdle, Marlene Müller, Stefan Sperlich, Axel Werwatz, 2012-08-27 The statistical and mathematical principles of smoothing with a focus on applicable techniques are presented in this book. It naturally splits into two parts: The first part is intended for undergraduate students majoring in mathematics, statistics, econometrics or biometrics whereas the second part is intended to be used by master and PhD students or researchers. The material is easy to accomplish since the e-book character of the text gives a maximum of flexibility in learning (and teaching) intensity. |
| textbook of parametric and nonparametric statistics: Nonparametric Econometrics Qi Li, Jeffrey Scott Racine, 2023-07-18 A comprehensive, up-to-date textbook on nonparametric methods for students and researchers Until now, students and researchers in nonparametric and semiparametric statistics and econometrics have had to turn to the latest journal articles to keep pace with these emerging methods of economic analysis. Nonparametric Econometrics fills a major gap by gathering together the most up-to-date theory and techniques and presenting them in a remarkably straightforward and accessible format. The empirical tests, data, and exercises included in this textbook help make it the ideal introduction for graduate students and an indispensable resource for researchers. Nonparametric and semiparametric methods have attracted a great deal of attention from statisticians in recent decades. While the majority of existing books on the subject operate from the presumption that the underlying data is strictly continuous in nature, more often than not social scientists deal with categorical data—nominal and ordinal—in applied settings. The conventional nonparametric approach to dealing with the presence of discrete variables is acknowledged to be unsatisfactory. This book is tailored to the needs of applied econometricians and social scientists. Qi Li and Jeffrey Racine emphasize nonparametric techniques suited to the rich array of data types—continuous, nominal, and ordinal—within one coherent framework. They also emphasize the properties of nonparametric estimators in the presence of potentially irrelevant variables. Nonparametric Econometrics covers all the material necessary to understand and apply nonparametric methods for real-world problems. |
| textbook of parametric and nonparametric statistics: Nonparametric Statistics with Applications to Science and Engineering Paul Kvam, Brani Vidakovic, 2007-07-23 A thorough and definitive book that fully addresses traditional and modern-day topics of nonparametric statistics This book presents a practical approach to nonparametric statistical analysis and provides comprehensive coverage of both established and newly developed methods. With the use of MATLAB, the authors present information on theorems and rank tests in an applied fashion, with an emphasis on modern methods in regression and curve fitting, bootstrap confidence intervals, splines, wavelets, empirical likelihood, and goodness-of-fit testing. Nonparametric Statistics with Applications to Science and Engineering begins with succinct coverage of basic results for order statistics, methods of categorical data analysis, nonparametric regression, and curve fitting methods. The authors then focus on nonparametric procedures that are becoming more relevant to engineering researchers and practitioners. The important fundamental materials needed to effectively learn and apply the discussed methods are also provided throughout the book. Complete with exercise sets, chapter reviews, and a related Web site that features downloadable MATLAB applications, this book is an essential textbook for graduate courses in engineering and the physical sciences and also serves as a valuable reference for researchers who seek a more comprehensive understanding of modern nonparametric statistical methods. |
| textbook of parametric and nonparametric statistics: An Introduction to Modern Nonparametric Statistics James J. Higgins, 2004 Guided by problems that frequently arise in actual practice, James Higgins' book presents a wide array of nonparametric methods of data analysis that researchers will find useful. It discusses a variety of nonparametric methods and, wherever possible, stresses the connection between methods. For instance, rank tests are introduced as special cases of permutation tests applied to ranks. The author provides coverage of topics not often found in nonparametric textbooks, including procedures for multivariate data, multiple regression, multi-factor analysis of variance, survival data, and curve smoothing. This truly modern approach teaches non-majors how to analyze and interpret data with nonparametric procedures using today's computing technology. |
| textbook of parametric and nonparametric statistics: INTRODUCTION TO NONPARAMETRIC STATISTICS. JOHN E. KOLASSA, 2022 |
| textbook of parametric and nonparametric statistics: Life Distributions Albert W. Marshall, Ingram Olkin, 2007-10-13 This book is devoted to the study of univariate distributions appropriate for the analyses of data known to be nonnegative. The book includes much material from reliability theory in engineering and survival analysis in medicine. |
| textbook of parametric and nonparametric statistics: Applied Nonparametric Statistics Wayne W. Daniel, 2000-06-30 This book covers the most commonly used nonparametric statistical techniques by emphasizing applications rather than theory. Exercises and examples are drawn from various disciplines including agriculture, biology, sociology, education, psychology, medicine, business, geology, and anthropology. The applications of techniques are presented in a step-by-step format that is repeated for all illustrative examples. Concepts are reinforced with many references to statistical literature to show the relevance to real-world problems. Chapters contain references of available computer programs and software packages that apply to methods presented in the book. |
| textbook of parametric and nonparametric statistics: Nonlinear Time Series Jianqing Fan, Qiwei Yao, 2008-09-11 This is the first book that integrates useful parametric and nonparametric techniques with time series modeling and prediction, the two important goals of time series analysis. Such a book will benefit researchers and practitioners in various fields such as econometricians, meteorologists, biologists, among others who wish to learn useful time series methods within a short period of time. The book also intends to serve as a reference or text book for graduate students in statistics and econometrics. |
| textbook of parametric and nonparametric statistics: Nonparametric Density Estimation Luc Devroye, László Györfi, 1985-01-18 This book gives a rigorous, systematic treatment of density estimates, their construction, use and analysis with full proofs. It develops L1 theory, rather than the classical L2, showing how L1 exposes fundamental properties of density estimates masked by L2. |
| textbook of parametric and nonparametric statistics: Nonparametric Statistical Inference Jean Dickinson Gibbons, Subhabrata Chakraborti, 2010-07-26 Proven Material for a Course on the Introduction to the Theory and/or on the Applications of Classical Nonparametric Methods Since its first publication in 1971, Nonparametric Statistical Inference has been widely regarded as the source for learning about nonparametric statistics. The fifth edition carries on this tradition while thoroughly revising at least 50 percent of the material. New to the Fifth Edition Updated and revised contents based on recent journal articles in the literature A new section in the chapter on goodness-of-fit tests A new chapter that offers practical guidance on how to choose among the various nonparametric procedures covered Additional problems and examples Improved computer figures This classic, best-selling statistics book continues to cover the most commonly used nonparametric procedures. The authors carefully state the assumptions, develop the theory behind the procedures, and illustrate the techniques using realistic research examples from the social, behavioral, and life sciences. For most procedures, they present the tests of hypotheses, confidence interval estimation, sample size determination, power, and comparisons of other relevant procedures. The text also gives examples of computer applications based on Minitab, SAS, and StatXact and compares these examples with corresponding hand calculations. The appendix includes a collection of tables required for solving the data-oriented problems. Nonparametric Statistical Inference, Fifth Edition provides in-depth yet accessible coverage of the theory and methods of nonparametric statistical inference procedures. It takes a practical approach that draws on scores of examples and problems and minimizes the theorem-proof format. Jean Dickinson Gibbons was recently interviewed regarding her generous pledge to Virginia Tech. |
| textbook of parametric and nonparametric statistics: Bayesian Nonparametric Data Analysis Peter Müller, Fernando Andres Quintana, Alejandro Jara, Tim Hanson, 2015-06-17 This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in online software pages. |
| textbook of parametric and nonparametric statistics: Introduction to Statistics Gottfried E. Noether, 2012-09-27 The introductory statistics course presents serious pedagogical problems to the instructor. For the great majority of students, the course represents the only formal contact with statistical thinking that he or she will have in college. Students come from many different fields of study, and a large number suffer from math anxiety. Thus, an instructor who is willing to settle for some limited objectives will have a much better chance of success than an instructor who aims for a broad exposure to statistics. Many statisticians agree that the primary objective of the introductory statistics course is to introduce students to variability and uncertainty and how to cope with them when drawing inferences from observed data. Addi tionally, the introductory COurse should enable students to handle a limited number of useful statistical techniques. The present text, which is the successor to the author's Introduction to Statistics: A Nonparametric Approach (Houghton Mifflin Company, Boston, 1976), tries to meet these objectives by introducing the student to the ba sic ideas of estimation and hypothesis testing early in the course after a rather brief introduction to data organization and some simple ideas about probability. Estimation and hypothesis testing are discussed in terms of the two-sample problem, which is both conceptually simpler and more realistic than the one-sample problem that customarily serves as the basis for the discussion of statistical inference. |
| textbook of parametric and nonparametric statistics: Combinatorial Methods in Density Estimation Luc Devroye, Gabor Lugosi, 2012-12-06 Density estimation has evolved enormously since the days of bar plots and histograms, but researchers and users are still struggling with the problem of the selection of the bin widths. This text explores a new paradigm for the data-based or automatic selection of the free parameters of density estimates in general so that the expected error is within a given constant multiple of the best possible error. The paradigm can be used in nearly all density estimates and for most model selection problems, both parametric and nonparametric. It is the first book on this topic. The text is intended for first-year graduate students in statistics and learning theory, and offers a host of opportunities for further research and thesis topics. Each chapter corresponds roughly to one lecture, and is supplemented with many classroom exercises. A one year course in probability theory at the level of Feller's Volume 1 should be more than adequate preparation. Gabor Lugosi is Professor at Universitat Pompeu Fabra in Barcelona, and Luc Debroye is Professor at McGill University in Montreal. In 1996, the authors, together with Lászlo Györfi, published the successful text, A Probabilistic Theory of Pattern Recognition with Springer-Verlag. Both authors have made many contributions in the area of nonparametric estimation. |
| textbook of parametric and nonparametric statistics: Introduction to Nonparametric Estimation Alexandre B. Tsybakov, 2010-11-29 Developed from lecture notes and ready to be used for a course on the graduate level, this concise text aims to introduce the fundamental concepts of nonparametric estimation theory while maintaining the exposition suitable for a first approach in the field. |
| textbook of parametric and nonparametric statistics: Nonparametric Statistical Process Control Subhabrata Chakraborti, Marien Graham, 2019-04-29 A unique approach to understanding the foundations of statistical quality control with a focus on the latest developments in nonparametric control charting methodologies Statistical Process Control (SPC) methods have a long and successful history and have revolutionized many facets of industrial production around the world. This book addresses recent developments in statistical process control bringing the modern use of computers and simulations along with theory within the reach of both the researchers and practitioners. The emphasis is on the burgeoning field of nonparametric SPC (NSPC) and the many new methodologies developed by researchers worldwide that are revolutionizing SPC. Over the last several years research in SPC, particularly on control charts, has seen phenomenal growth. Control charts are no longer confined to manufacturing and are now applied for process control and monitoring in a wide array of applications, from education, to environmental monitoring, to disease mapping, to crime prevention. This book addresses quality control methodology, especially control charts, from a statistician’s viewpoint, striking a careful balance between theory and practice. Although the focus is on the newer nonparametric control charts, the reader is first introduced to the main classes of the parametric control charts and the associated theory, so that the proper foundational background can be laid. Reviews basic SPC theory and terminology, the different types of control charts, control chart design, sample size, sampling frequency, control limits, and more Focuses on the distribution-free (nonparametric) charts for the cases in which the underlying process distribution is unknown Provides guidance on control chart selection, choosing control limits and other quality related matters, along with all relevant formulas and tables Uses computer simulations and graphics to illustrate concepts and explore the latest research in SPC Offering a uniquely balanced presentation of both theory and practice, Nonparametric Methods for Statistical Quality Control is a vital resource for students, interested practitioners, researchers, and anyone with an appropriate background in statistics interested in learning about the foundations of SPC and latest developments in NSPC. |
| textbook of parametric and nonparametric statistics: Applied Nonparametric Econometrics Daniel J. Henderson, Christopher F. Parmeter, 2015-01-12 The majority of empirical research in economics ignores the potential benefits of nonparametric methods, while the majority of advances in nonparametric theory ignores the problems faced in applied econometrics. This book helps bridge this gap between applied economists and theoretical nonparametric econometricians. It discusses in depth, and in terms that someone with only one year of graduate econometrics can understand, basic to advanced nonparametric methods. The analysis starts with density estimation and motivates the procedures through methods that should be familiar to the reader. It then moves on to kernel regression, estimation with discrete data, and advanced methods such as estimation with panel data and instrumental variables models. The book pays close attention to the issues that arise with programming, computing speed, and application. In each chapter, the methods discussed are applied to actual data, paying attention to presentation of results and potential pitfalls. |
| textbook of parametric and nonparametric statistics: Modern Statistical Methods for Astronomy Eric D. Feigelson, G. Jogesh Babu, 2012-07-12 Modern astronomical research is beset with a vast range of statistical challenges, ranging from reducing data from megadatasets to characterizing an amazing variety of variable celestial objects or testing astrophysical theory. Linking astronomy to the world of modern statistics, this volume is a unique resource, introducing astronomers to advanced statistics through ready-to-use code in the public domain R statistical software environment. The book presents fundamental results of probability theory and statistical inference, before exploring several fields of applied statistics, such as data smoothing, regression, multivariate analysis and classification, treatment of nondetections, time series analysis, and spatial point processes. It applies the methods discussed to contemporary astronomical research datasets using the R statistical software, making it invaluable for graduate students and researchers facing complex data analysis tasks. A link to the author's website for this book can be found at www.cambridge.org/msma. Material available on their website includes datasets, R code and errata. |
| textbook of parametric and nonparametric statistics: Bayesian Nonparametrics Nils Lid Hjort, Chris Holmes, Peter Müller, Stephen G. Walker, 2010-04-12 Bayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: this intelligent book is the perfect guide to what can seem a forbidding landscape. Tutorial chapters by Ghosal, Lijoi and Prünster, Teh and Jordan, and Dunson advance from theory, to basic models and hierarchical modeling, to applications and implementation, particularly in computer science and biostatistics. These are complemented by companion chapters by the editors and Griffin and Quintana, providing additional models, examining computational issues, identifying future growth areas, and giving links to related topics. This coherent text gives ready access both to underlying principles and to state-of-the-art practice. Specific examples are drawn from information retrieval, NLP, machine vision, computational biology, biostatistics, and bioinformatics. |
| textbook of parametric and nonparametric statistics: High-Dimensional Statistics Martin J. Wainwright, 2019-02-21 Recent years have witnessed an explosion in the volume and variety of data collected in all scientific disciplines and industrial settings. Such massive data sets present a number of challenges to researchers in statistics and machine learning. This book provides a self-contained introduction to the area of high-dimensional statistics, aimed at the first-year graduate level. It includes chapters that are focused on core methodology and theory - including tail bounds, concentration inequalities, uniform laws and empirical process, and random matrices - as well as chapters devoted to in-depth exploration of particular model classes - including sparse linear models, matrix models with rank constraints, graphical models, and various types of non-parametric models. With hundreds of worked examples and exercises, this text is intended both for courses and for self-study by graduate students and researchers in statistics, machine learning, and related fields who must understand, apply, and adapt modern statistical methods suited to large-scale data. |
| textbook of parametric and nonparametric statistics: A Conceptual Guide to Statistics Using SPSS Elliot T. Berkman, Steven P. Reise, 2011-04-12 Bridging an understanding of Statistics and SPSS. The text is written in a user-friendly language and illustrates concepts that would otherwise be confusing to beginning students and those with limited computer skills. -Justice Mbizo, University of West Florida This unique text helps students develop a conceptual understanding of a variety of statistical tests by linking the ideas learned in a statistics class from a traditional statistics textbook with the computational steps and output from SPSS. Each chapter begins with a student-friendly explanation of the concept behind each statistical test and how the test relates to that concept. The authors then walk through the steps to compute the test in SPSS and the output, clearly linking how the SPSS procedure and output connect back to the conceptual underpinnings of the test. By drawing clear connections between the theoretical and computational aspects of statistics, this engaging text aids students′ understanding of theoretical concepts by teaching them in a practical context. |
| textbook of parametric and nonparametric statistics: Nonparametric Kernel Density Estimation and Its Computational Aspects Artur Gramacki, 2017-12-21 This book describes computational problems related to kernel density estimation (KDE) – one of the most important and widely used data smoothing techniques. A very detailed description of novel FFT-based algorithms for both KDE computations and bandwidth selection are presented. The theory of KDE appears to have matured and is now well developed and understood. However, there is not much progress observed in terms of performance improvements. This book is an attempt to remedy this. The book primarily addresses researchers and advanced graduate or postgraduate students who are interested in KDE and its computational aspects. The book contains both some background and much more sophisticated material, hence also more experienced researchers in the KDE area may find it interesting. The presented material is richly illustrated with many numerical examples using both artificial and real datasets. Also, a number of practical applications related to KDE are presented. |
| textbook of parametric and nonparametric statistics: Practical Statistics for Students Louis Cohen, Michael Holliday, 1996-09-28 This bestselling textbook is designed to help students understand parametric and nonparametric statistical methods so that they can tackle research problems successfully. By working through this book carefully and systematically, those who may not have a strong background in mathematics will gain a thorough grasp of the most widely used statistical methods in the social sciences. |
| textbook of parametric and nonparametric statistics: Understanding Statistics Bruce J. Chalmer, 2020-01-29 Introducing undergraduates to the vital concepts of statistics, this superb textbook allows instructors to include as much—or as little—mathematical detail as may be suitable for their students. Featuring Statpal statistical software for the IBM PC®, the book contains study questions that help solidify students’ understanding of the material and prepare them for the next group of concepts. Many of the exercises, labeled “Statpal exercises,” are especially written for the Statpal statistical package. Understanding Statistics begins with the basic concepts of statistical inference ... presents normal and binomial distributions, general techniques of interval estimation and hypothesis testing, and applications of these techniques to inferences about a single population mean and proportions ... and covers inferences about group differences, including parametric and nonparametric approaches to the two-group case, and the one-way ANOVA and its nonparametric analogue. In addition, this volume considers relationships between two variables, including the correlation co-efficient, Spearman’s rho, and Kendall’s tau ... surveys basic regression methods, including simple, multiple, and stepwise ... and discusses the analysis of variance of factorial designs, the concept of interaction, and the analysis of categorical data using the chi-square test. Complete with tables and drawings plus appendices that furnish instructions for using Statpal software, information on advanced topics, and much more, Understanding Statistics is an ideal text for undergraduate survey courses on statistical methods as well as for courses in economics, psychology, sociology, education, business administration, and others that require basic statistics. |
| textbook of parametric and nonparametric statistics: Distribution-free Statistical Tests James Vandiver Bradley, 1960 |
| textbook of parametric and nonparametric statistics: A Course in Mathematical Statistics and Large Sample Theory Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru, 2016-08-13 This graduate-level textbook is primarily aimed at graduate students of statistics, mathematics, science, and engineering who have had an undergraduate course in statistics, an upper division course in analysis, and some acquaintance with measure theoretic probability. It provides a rigorous presentation of the core of mathematical statistics. Part I of this book constitutes a one-semester course on basic parametric mathematical statistics. Part II deals with the large sample theory of statistics - parametric and nonparametric, and its contents may be covered in one semester as well. Part III provides brief accounts of a number of topics of current interest for practitioners and other disciplines whose work involves statistical methods. |
| textbook of parametric and nonparametric statistics: Applied Nonparametric Regression Wolfgang Härdle, 1990 This is the first book to bring together in one place the techniques for regression curve smoothing involving more than one variable. |
| textbook of parametric and nonparametric statistics: Mathematical Statistics Jun Shao, 2008-02-03 This graduate textbook covers topics in statistical theory essential for graduate students preparing for work on a Ph.D. degree in statistics. The first chapter provides a quick overview of concepts and results in measure-theoretic probability theory that are useful in statistics. The second chapter introduces some fundamental concepts in statistical decision theory and inference. Chapters 3-7 contain detailed studies on some important topics: unbiased estimation, parametric estimation, nonparametric estimation, hypothesis testing, and confidence sets. A large number of exercises in each chapter provide not only practice problems for students, but also many additional results. In addition to improving the presentation, the new edition makes Chapter 1 a self-contained chapter for probability theory with emphasis in statistics. Added topics include useful moment inequalities, more discussions of moment generating and characteristic functions, conditional independence, Markov chains, martingales, Edgeworth and Cornish-Fisher expansions, and proofs to many key theorems such as the dominated convergence theorem, monotone convergence theorem, uniqueness theorem, continuity theorem, law of large numbers, and central limit theorem. A new section in Chapter 5 introduces semiparametric models, and a number of new exercises were added to each chapter. |
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As I want to write a short "book" about Analysis I would like to work with a template which is good enough for readability, looks good and furthermore can handle dozens of my theorems, axioms,
What are all the font styles I can use in math mode? - TeX
Jun 1, 2012 · As can be seen in above picture, there are lots of fonts or font styles that can be used in math mode. What are all the fonts or font styles I can use in math mode, and which packages …
What are the available "documentclass" types and their uses?
Aug 2, 2010 · The standalone class actually simply loads a real class but uses the preview package to reduce the page size to the content. It is supposed to be used for subfiles holding only picture …
A better notation to denote arcs for an American high school textbook
Jun 9, 2017 · A better notation to denote arcs for an American high school textbook Ask Question Asked 12 years, 4 months ago Modified 4 years, 7 months ago
How to present a Python code snippet efficiently in LaTeX?
Oct 24, 2019 · I am quite new to LaTeX and do not know how to present a simple Python code like this in LaTeX in a professional way. Having your solutions and examples will help me a lot to learn …
Blocks in Beamer - TeX - LaTeX Stack Exchange
Apr 29, 2014 · This has already been answered but I should like to add that you can still use whichever theme you want with invisible blocks, and then set the colors to make blocks …
How can I design a book cover? - TeX - LaTeX Stack Exchange
May 6, 2011 · Can anyone share his or her experience on designing a book cover? I just finished writing my e-book and now I am designing the cover. I tried \\maketitle and the result was pretty …
Math book: how to write Exercise and Answers - LaTeX Stack …
May 11, 2017 · EDIT within \\documentclass[12pt]{book}I want to create chapter-wise exercises and put all the solutions (with or without hints) at the end of the book. I want to do this in simple and …
How can I use BibTeX to cite a web page? - LaTeX Stack Exchange
I'm looking to cite a number of web pages using bibtex and I was wondering if there was a specific template of the form @<template name here> for doing that. If you could use the following …
How to write a book in LaTeX - TeX - LaTeX Stack Exchange
Mar 14, 2012 · This will determine the way you write very much. In math you want a textbook with exercises and detailed proofs or more a monograph with extended bibliography, etc. Structure …
Good LaTeX template for book about maths - LaTeX Stack …
As I want to write a short "book" about Analysis I would like to work with a template which is good enough for readability, looks good and furthermore can handle dozens of my theorems, axioms,
What are all the font styles I can use in math mode? - TeX
Jun 1, 2012 · As can be seen in above picture, there are lots of fonts or font styles that can be used in math mode. What are all the fonts or font styles I can use in math mode, and which …
What are the available "documentclass" types and their uses?
Aug 2, 2010 · The standalone class actually simply loads a real class but uses the preview package to reduce the page size to the content. It is supposed to be used for subfiles holding …
A better notation to denote arcs for an American high school …
Jun 9, 2017 · A better notation to denote arcs for an American high school textbook Ask Question Asked 12 years, 4 months ago Modified 4 years, 7 months ago
How to present a Python code snippet efficiently in LaTeX?
Oct 24, 2019 · I am quite new to LaTeX and do not know how to present a simple Python code like this in LaTeX in a professional way. Having your solutions and examples will help me a lot …
Blocks in Beamer - TeX - LaTeX Stack Exchange
Apr 29, 2014 · This has already been answered but I should like to add that you can still use whichever theme you want with invisible blocks, and then set the colors to make blocks …
How can I design a book cover? - TeX - LaTeX Stack Exchange
May 6, 2011 · Can anyone share his or her experience on designing a book cover? I just finished writing my e-book and now I am designing the cover. I tried \\maketitle and the result was …
Math book: how to write Exercise and Answers - LaTeX Stack …
May 11, 2017 · EDIT within \\documentclass[12pt]{book}I want to create chapter-wise exercises and put all the solutions (with or without hints) at the end of the book. I want to do this in …
How can I use BibTeX to cite a web page? - LaTeX Stack Exchange
I'm looking to cite a number of web pages using bibtex and I was wondering if there was a specific template of the form @<template name here> for doing that. If you could use the following …
