Advertisement
| parameter of interest definition: Introductory Statistics 2e Barbara Illowsky, Susan Dean, 2023-12-13 Introductory Statistics 2e provides an engaging, practical, and thorough overview of the core concepts and skills taught in most one-semester statistics courses. The text focuses on diverse applications from a variety of fields and societal contexts, including business, healthcare, sciences, sociology, political science, computing, and several others. The material supports students with conceptual narratives, detailed step-by-step examples, and a wealth of illustrations, as well as collaborative exercises, technology integration problems, and statistics labs. The text assumes some knowledge of intermediate algebra, and includes thousands of problems and exercises that offer instructors and students ample opportunity to explore and reinforce useful statistical skills. This is an adaptation of Introductory Statistics 2e by OpenStax. You can access the textbook as pdf for free at openstax.org. Minor editorial changes were made to ensure a better ebook reading experience. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution 4.0 International License. |
| parameter of interest definition: Multiple Testing Procedures with Applications to Genomics Sandrine Dudoit, Mark J. van der Laan, 2007-12-18 This book establishes the theoretical foundations of a general methodology for multiple hypothesis testing and discusses its software implementation in R and SAS. These are applied to a range of problems in biomedical and genomic research, including identification of differentially expressed and co-expressed genes in high-throughput gene expression experiments; tests of association between gene expression measures and biological annotation metadata; sequence analysis; and genetic mapping of complex traits using single nucleotide polymorphisms. The procedures are based on a test statistics joint null distribution and provide Type I error control in testing problems involving general data generating distributions, null hypotheses, and test statistics. |
| parameter of interest definition: Field Sampling Methods for Remedial Investigations Mark Edward Byrnes, 2008-08-25 Originally published in 1994, the first edition of Field Sampling Methods for Remedial Investigations soon became a premier resource in the field. ThePrinceton Groundwater course designated it as one of the top books on the market that address strategies for groundwater well installation, well completion, and groundwater sampling. This long-awai |
| parameter of interest definition: MODFLOW-2000 Edward R. Banta, 2000 |
| parameter of interest definition: Experimental Design and Data Analysis for Biologists Gerald Peter Quinn, Michael J. Keough, 2002-03-21 An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data. The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models. Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models. Multivariate techniques, including classification and ordination, are then introduced. Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results. The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature. The book is supported by a website that provides all data sets, questions for each chapter and links to software. |
| parameter of interest definition: Statistical Modeling for Biological Systems Anthony Almudevar, David Oakes, Jack Hall, 2020-03-11 This book commemorates the scientific contributions of distinguished statistician, Andrei Yakovlev. It reflects upon Dr. Yakovlev’s many research interests including stochastic modeling and the analysis of micro-array data, and throughout the book it emphasizes applications of the theory in biology, medicine and public health. The contributions to this volume are divided into two parts. Part A consists of original research articles, which can be roughly grouped into four thematic areas: (i) branching processes, especially as models for cell kinetics, (ii) multiple testing issues as they arise in the analysis of biologic data, (iii) applications of mathematical models and of new inferential techniques in epidemiology, and (iv) contributions to statistical methodology, with an emphasis on the modeling and analysis of survival time data. Part B consists of methodological research reported as a short communication, ending with some personal reflections on research fields associated with Andrei and on his approach to science. The Appendix contains an abbreviated vitae and a list of Andrei’s publications, complete as far as we know. The contributions in this book are written by Dr. Yakovlev’s collaborators and notable statisticians including former presidents of the Institute of Mathematical Statistics and of the Statistics Section of the AAAS. Dr. Yakovlev’s research appeared in four books and almost 200 scientific papers, in mathematics, statistics, biomathematics and biology journals. Ultimately this book offers a tribute to Dr. Yakovlev’s work and recognizes the legacy of his contributions in the biostatistics community. |
| parameter of interest definition: In All Likelihood Yudi Pawitan, 2013-01-17 This book introduces likelihood as a unifying concept in statistical modelling and inference. The complete range of concepts and applications are covered, from very simple to very complex studies. It relies on realistic examples, and presents the main results using heuristic rather than formal mathematical arguments. |
| parameter of interest definition: Introductory Statistics Douglas S. Shafer, 2022 |
| parameter of interest definition: Structural Equation Modeling David Kaplan, 2008-07-23 Using detailed, empirical examples, Structural Equation Modeling, Second Edition, presents a thorough and sophisticated treatment of the foundations of structural equation modeling (SEM). It also demonstrates how SEM can provide a unique lens on the problems social and behavioral scientists face. Intended Audience While the book assumes some knowledge and background in statistics, it guides readers through the foundations and critical assumptions of SEM in an easy-to-understand manner. |
| parameter of interest definition: Generalized Linear Models with Random Effects Youngjo Lee, John A. Nelder, Yudi Pawitan, 2018-07-11 This is the second edition of a monograph on generalized linear models with random effects that extends the classic work of McCullagh and Nelder. It has been thoroughly updated, with around 80 pages added, including new material on the extended likelihood approach that strengthens the theoretical basis of the methodology, new developments in variable selection and multiple testing, and new examples and applications. It includes an R package for all the methods and examples that supplement the book. |
| parameter of interest definition: Estimation, Inference and Specification Analysis Halbert White, 1996-06-28 This book examines the consequences of misspecifications for the interpretation of likelihood-based methods of statistical estimation and interference. The analysis concludes with an examination of methods by which the possibility of misspecification can be empirically investigated. |
| parameter of interest definition: Survey Sampling Theory and Applications Raghunath Arnab, 2017-03-08 Survey Sampling Theory and Applications offers a comprehensive overview of survey sampling, including the basics of sampling theory and practice, as well as research-based topics and examples of emerging trends. The text is useful for basic and advanced survey sampling courses. Many other books available for graduate students do not contain material on recent developments in the area of survey sampling. The book covers a wide spectrum of topics on the subject, including repetitive sampling over two occasions with varying probabilities, ranked set sampling, Fays method for balanced repeated replications, mirror-match bootstrap, and controlled sampling procedures. Many topics discussed here are not available in other text books. In each section, theories are illustrated with numerical examples. At the end of each chapter theoretical as well as numerical exercises are given which can help graduate students. - Covers a wide spectrum of topics on survey sampling and statistics - Serves as an ideal text for graduate students and researchers in survey sampling theory and applications - Contains material on recent developments in survey sampling not covered in other books - Illustrates theories using numerical examples and exercises |
| parameter of interest definition: The SAGE Handbook of Quantitative Methodology for the Social Sciences David Kaplan, 2004-06-21 The SAGE Handbook of Quantitative Methodology for the Social Sciences is the definitive reference for teachers, students, and researchers of quantitative methods in the social sciences, as it provides a comprehensive overview of the major techniques used in the field. The contributors, top methodologists and researchers, have written about their areas of expertise in ways that convey the utility of their respective techniques, but, where appropriate, they also offer a fair critique of these techniques. Relevance to real-world problems in the social sciences is an essential ingredient of each chapter and makes this an invaluable resource. |
| parameter of interest definition: Methods in Social Epidemiology J. Michael Oakes, Jay S. Kaufman, 2017-02-22 A thorough, practical reference on the social patterns behind health outcomes Methods in Social Epidemiology provides students and professionals with a comprehensive reference for studying the social distribution and social determinants of health. Covering the theory, models, and methods used to measure and analyze these phenomena, this book serves as both an introduction to the field and a practical manual for data collection and analysis. This new second edition has been updated to reflect the field's tremendous growth in recent years, including advancements in statistical modeling and study designs. New chapters delve into genetic methods, structural cofounding, selection bias, network methods, and more, including new discussion on qualitative data collection with disadvantaged populations. Social epidemiology studies the way society's innumerable social interactions, both past and present, yields different exposures and health outcomes between individuals within populations. This book provides a thorough, detailed overview of the field, with expert guidance toward the real-world methods that fuel the latest advances. Identify, measure, and track health patterns in the population Discover how poverty, race, and socioeconomic factors become risk factors for disease Learn qualitative data collection techniques and methods of statistical analysis Examine up-to-date models, theory, and frameworks in the social epidemiology sphere As the field continues to evolve, researchers continue to identify new disease-specific risk factors and learn more about how the social system promotes and maintains well-known exposure disparities. New technology in data science and genomics allows for more rigorous investigation and analysis, while the general thinking in the field has become more targeted and attentive to causal inference and core assumptions behind effect identification. It's an exciting time to be a part of the field, and Methods in Social Epidemiology provides a solid reference for any student, researcher, or faculty in public health. |
| parameter of interest definition: Intermediate Statistics and Econometrics Dale J. Poirier, 1995 The standard introductory texts to mathematical statistics leave the Bayesian approach to be taught later in advanced topics courses-giving students the impression that Bayesian statistics provide but a few techniques appropriate in only special circumstances. Nothing could be further from the truth, argues Dale Poirier, who has developed a course for teaching comparatively both the classical and the Bayesian approaches to econometrics. Poirier's text provides a thoroughly modern, self-contained, comprehensive, and accessible treatment of the probability and statistical foundations of econometrics with special emphasis on the linear regression model. Written primarily for advanced undergraduate and graduate students who are pursuing research careers in economics, Intermediate Statistics and Econometrics offers a broad perspective, bringing together a great deal of diverse material. Its comparative approach, emphasis on regression and prediction, and numerous exercises and references provide a solid foundation for subsequent courses in econometrics and will prove a valuable resource to many nonspecialists who want to update their quantitative skills. The introduction closes with an example of a real-world data set-the Challengerspace shuttle disaster-that motivates much of the text's theoretical discussion. The ten chapters that follow cover basic concepts, special distributions, distributions of functions of random variables, sampling theory, estimation, hypothesis testing, prediction, and the linear regression model. Appendixes contain a review of matrix algebra, computation, and statistical tables. |
| parameter of interest definition: Targeted Learning Mark J. van der Laan, Sherri Rose, 2011-06-17 The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjective choices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the target parameter representing the scientific question of interest. This book is aimed at both statisticians and applied researchers interested in causal inference and general effect estimation for observational and experimental data. Part I is an accessible introduction to super learning and the targeted maximum likelihood estimator, including related concepts necessary to understand and apply these methods. Parts II-IX handle complex data structures and topics applied researchers will immediately recognize from their own research, including time-to-event outcomes, direct and indirect effects, positivity violations, case-control studies, censored data, longitudinal data, and genomic studies. |
| parameter of interest definition: Uncertainty in Artificial Intelligence 5 R.D. Shachter, L.N. Kanal, M. Henrion, J.F. Lemmer, 2017-03-20 This volume, like its predecessors, reflects the cutting edge of research on the automation of reasoning under uncertainty.A more pragmatic emphasis is evident, for although some papers address fundamental issues, the majority address practical issues. Topics include the relations between alternative formalisms (including possibilistic reasoning), Dempster-Shafer belief functions, non-monotonic reasoning, Bayesian and decision theoretic schemes, and new inference techniques for belief nets. New techniques are applied to important problems in medicine, vision, robotics, and natural language understanding. |
| parameter of interest definition: Models, Measurement, and Metrology Extending the SI William P. Fisher, Jr., Leslie Pendrill, 2024-09-23 The book focuses on the extension of quality-assured measurement and metrology into psychological and social domains. This is not only feasible and achievable, but also a pressing concern. Significant progress in developing a common conceptual system for measurement across the sciences has been made in recent collaborations between metrologists and psychometricians, as reported in the chapters of this book. Modeling, estimation, and interpretation of objectively reproducible unit quantities that support both general comparability and adaptation to unique local circumstances are demonstrated in fields as diverse as artificial intelligence, justice, and beauty perception. |
| parameter of interest definition: Statistics for Engineering and the Sciences William M. Mendenhall, Terry L. Sincich, 2016-04-05 Prepare Your Students for Statistical Work in the Real WorldStatistics for Engineering and the Sciences, Sixth Edition is designed for a two-semester introductory course on statistics for students majoring in engineering or any of the physical sciences. This popular text continues to teach students the basic concepts of data description and statist |
| parameter of interest definition: Bayesian Inference in Dynamic Econometric Models Luc Bauwens, Michel Lubrano, Jean-François Richard, 2000-01-06 This book contains an up-to-date coverage of the last twenty years advances in Bayesian inference in econometrics, with an emphasis on dynamic models. It shows how to treat Bayesian inference in non linear models, by integrating the useful developments of numerical integration techniques based on simulations (such as Markov Chain Monte Carlo methods), and the long available analytical results of Bayesian inference for linear regression models. It thus covers a broad range of rather recent models for economic time series, such as non linear models, autoregressive conditional heteroskedastic regressions, and cointegrated vector autoregressive models. It contains also an extensive chapter on unit root inference from the Bayesian viewpoint. Several examples illustrate the methods. |
| parameter of interest definition: The Econometrics of Panel Data László Mátyás, Patrick Sevestre, 2013-12-01 The aim of this volume is to provide a general overview of the econometrics of panel data, both from a theoretical and from an applied viewpoint. Since the pioneering papers by Edwin Kuh (1959), Yair Mundlak (1961), Irving Hoch (1962), and Pietro Balestra and Marc Nerlove (1966), the pooling of cross sections and time series data has become an increasingly popular way of quantifying economic relationships. Each series provides information lacking in the other, so a combination of both leads to more accurate and reliable results than would be achievable by one type of series alone. Over the last 30 years much work has been done: investigation of the properties of the applied estimators and test statistics, analysis of dynamic models and the effects of eventual measurement errors, etc. These are just some of the problems addressed by this work. In addition, some specific diffi culties associated with the use of panel data, such as attrition, heterogeneity, selectivity bias, pseudo panels etc., have also been explored. The first objective of this book, which takes up Parts I and II, is to give as complete and up-to-date a presentation of these theoretical developments as possible. Part I is concerned with classical linear models and their extensions; Part II deals with nonlinear models and related issues: logit and pro bit models, latent variable models, duration and count data models, incomplete panels and selectivity bias, point processes, and simulation techniques. |
| parameter of interest definition: Statistical Analysis Techniques in Particle Physics Ilya Narsky, Frank C. Porter, 2013-10-24 Modern analysis of HEP data needs advanced statistical tools to separate signal from background. This is the first book which focuses on machine learning techniques. It will be of interest to almost every high energy physicist, and, due to its coverage, suitable for students. |
| parameter of interest definition: Introduction to Statistics Jim Frost, 2020-07-21 Learn statistics without fear! Build a solid foundation in data analysis. Be confident that you understand what your data are telling you and that you can explain the results to others! I'll help you intuitively understand statistics by using simple language and deemphasizing formulas. This guide starts with an overview of statistics and why it is so important. We proceed to essential statistical skills and knowledge about different types of data, relationships, and distributions. Then we move to using inferential statistics to expand human knowledge, how it fits into the scientific method, and how to design and critique experiments. Learn the fundamentals of statistics. Why is the field of statistics so vital in our data-driven society? Interpret graphs and summary statistics. Find relationships between different types of variables. Understand the properties of data distributions. Use measures of central tendency and variability. Interpret correlations and percentiles. Use probability distributions to calculate probabilities. Learn about the normal distribution and the binomial distributions in depth. Grasp the differences between descriptive and inferential statistics. Use data collection methodologies properly and understand sample size considerations. Critique scientific experiments-whether it's your own or another researcher's. |
| parameter of interest definition: Classical Methods of Statistics Otto J.W.F. Kardaun, 2005-09-16 Classical Methods of Statistics is a guidebook combining theory and practical methods. It is especially conceived for graduate students and scientists who are interested in the applications of statistical methods to plasma physics. Thus it provides also concise information on experimental aspects of fusion-oriented plasma physics. In view of the first three basic chapters it can be fruitfully used by students majoring in probability theory and statistics. The first part deals with the mathematical foundation and framework of the subject. Some attention is given to the historical background. Exercises are added to help readers understand the underlying concepts. In the second part, two major case studies are presented which exemplify the areas of discriminant analysis and multivariate profile analysis, respectively. To introduce these case studies, an outline is provided of the context of magnetic plasma fusion research. In the third part an overview is given of statistical software; separate attention is devoted to SAS and S-PLUS. The final chapter presents several datasets and gives a description of their physical setting. Most of these datasets were assembled at the ASDEX Upgrade Tokamak. All of them are accompanied by exercises in form of guided (minor) case studies. The book concludes with translations of key concepts into several languages. |
| parameter of interest definition: Microeconometrics A. Colin Cameron, Pravin K. Trivedi, 2005-05-09 The book is oriented to the practitioner. |
| parameter of interest definition: Statistical Methods for Data Analysis in Particle Physics Luca Lista, 2015-07-24 This concise set of course-based notes provides the reader with the main concepts and tools to perform statistical analysis of experimental data, in particular in the field of high-energy physics (HEP). First, an introduction to probability theory and basic statistics is given, mainly as reminder from advanced undergraduate studies, yet also in view to clearly distinguish the Frequentist versus Bayesian approaches and interpretations in subsequent applications. More advanced concepts and applications are gradually introduced, culminating in the chapter on upper limits as many applications in HEP concern hypothesis testing, where often the main goal is to provide better and better limits so as to be able to distinguish eventually between competing hypotheses or to rule out some of them altogether. Many worked examples will help newcomers to the field and graduate students to understand the pitfalls in applying theoretical concepts to actual data. |
| parameter of interest definition: Statistics Using Technology, Second Edition Kathryn Kozak, 2015-12-12 Statistics With Technology, Second Edition, is an introductory statistics textbook. It uses the TI-83/84 calculator and R, an open source statistical software, for all calculations. Other technology can also be used besides the TI-83/84 calculator and the software R, but these are the ones that are presented in the text. This book presents probability and statistics from a more conceptual approach, and focuses less on computation. Analysis and interpretation of data is more important than how to compute basic statistical values. |
| parameter of interest definition: Data Base Analyzer and Parameter Estimator (DBAPE) Interactive Computer Program User's Manual John C. Imhoff, 1990 |
| parameter of interest definition: Principles of Clinical Pharmacology Shiew-Mei Huang, Arthur J. Atkinson Jr., Juan J.L. Lertora, 2012-09-18 Focusing on the fundamentals that underlie the clinical use and contemporary development of pharmaceuticals, this text includes examples to demonstrate the central role of pharmokinetic principles in both clinical practice and drug development. |
| parameter of interest definition: Encyclopedia of Environmental Health , 2019-08-22 Encyclopedia of Environmental Health, Second Edition, Six Volume Set presents the newest release in this fundamental reference that updates and broadens the umbrella of environmental health, especially social and environmental health for its readers. There is ongoing revolution in governance, policies and intervention strategies aimed at evolving changes in health disparities, disease burden, trans-boundary transport and health hazards. This new edition reflects these realities, mapping new directions in the field that include how to minimize threats and develop new scientific paradigms that address emerging local, national and global environmental concerns. Represents a one-stop resource for scientifically reliable information on environmental health Fills a critical gap, with information on one of the most rapidly growing scientific fields of our time Provides comparative approaches to environmental health practice and research in different countries and regions of the world Covers issues behind specific questions and describes the best available scientific methods for environmental risk assessment |
| parameter of interest definition: Theoretical Statistics D.R. Cox, 1979-09-06 A text that stresses the general concepts of the theory of statistics Theoretical Statistics provides a systematic statement of the theory of statistics, emphasizing general concepts rather than mathematical rigor. Chapters 1 through 3 provide an overview of statistics and discuss some of the basic philosophical ideas and problems behind statistica |
| parameter of interest definition: General Technical Report PNW-GTR , 2005 |
| parameter of interest definition: Econometrics: Alchemy or Science? David F. Hendry, 2000-10-26 Econometrics: Alchemy or Science? analyses the effectiveness and validity of applying econometric methods to economic time series. The methodological dispute is long-standing, and no claim can be made for a single valid method, but recent results on the theory and practice of model selection bid fair to resolve many of the contentious issues. The book presents criticisms and evaluations of competing approaches, based on theoretical economic and econometric analyses, empirical applications, and Monte Carlo simulations, which interact to determine best practice. It explains the evolution of an approach to econometric modelling founded in careful statistical analyses of the available data, using economic theory to guide the general model specification. From a strong foundation in the theory of reduction, via a range of applied and simulation studies, it demonstrates that general-to-specific procedures have excellent properties. The book is divided into four Parts: Routes and Route Maps; Empirical Modelling Strategies; Formalization; and Retrospect and Prospect. A short preamble to each chapter sketches the salient themes, links to earlier and later developments, and the lessons learnt or missed at the time. A sequence of detailed empirical studies of consumers' expenditure and money demand illustrate most facets of the approach. Material new to this revised edition describes recent major advances in computer-automated model selection, embodied in the powerful new software program PcGets, which establish the operational success of the modelling strategy. |
| parameter of interest definition: Journal of the American Statistical Association , 2004 A scientific and educational journal not only for professional statisticians but also for economists, business executives, research directors, government officials, university professors, and others who are seriously interested in the application of statistical methods to practical problems, in the development of more useful methods, and in the improvement of basic statistical data. |
| parameter of interest definition: BIAN Data Architecture & Design Specialist Courseware Laleh Rafati, René De Vleeschauwer, Martine Alaerts, Patrick Derde, 2022-10-25 This is the official courseware for the accredited BIAN Data Architecture & Design Specialist training. It is intended for professionals that aim to pass the BIAN Data Architecture & Design Specialist Certification,via the online exam. The BIAN Data Architecture & Design Specialist exam leads to the official BIAN Data Architecture & Design Specialist Certification by the Banking Industry Architecture Network. It is carried out by Van Haren Learning Solutions. The BIAN Data Architecture & Design Specialist Certification exam tests the training participant’s knowledge of the BIAN Data Reference Architecture for the Financial Industry. By successfully passing the BIAN Data Architecture & Design Specialist exam, participants will achieve the BIAN Data Architecture & Design Specialist level certification which assures they have been audited and have successfully mastered the required BIAN Data Architecture & Design Specialist level. The BIAN Data Architecture & Design Specialist level includes the ability to describe the added value that the BIAN Object Model seeks to provide to the financial industry and its service providers. It includes the knowledge and understanding of the general design principles and elements of BIAN’s Reference Architecture for the Financial Industry. But the main objective is to understand the underlying ArchiMate® and UML concepts used by BIAN to model the Object Model and to learn and apply the BIAN Modeling Patterns. The BIAN certification exam is intended for professionals in the financial services industry such as: data architects and data modelers at both enterprise and solution levels, as well as consultants and service providers that operate in the financial services industry. Key benefits of the BIAN Data Architecture & Design Specialist Certification are: • It enables data professionals to leverage the benefits of BIAN and the BIAN BOM. • It increases the knowledge and general skills of professionals regarding the BIAN BOM and Control Records and enables the creation of more transparent ICT systems. • It provides professionals and their organizations with a competitive advantage. • It is a hallmark for the professionalism of banking professionals and banking architects who are active with data. Number of questions: 60 Duration(minute) of exam: 60 min. Pass Mark: 70% Open/Closed book: Closed Language: English Paper based & online availability: Online availability |
| parameter of interest definition: Handbook of Bayesian, Fiducial, and Frequentist Inference James Berger, Xiao-Li Meng, Nancy Reid, Min-ge Xie, 2024-02-26 The emergence of data science, in recent decades, has magnified the need for efficient methodology for analyzing data and highlighted the importance of statistical inference. Despite the tremendous progress that has been made, statistical science is still a young discipline and continues to have several different and competing paths in its approaches and its foundations. While the emergence of competing approaches is a natural progression of any scientific discipline, differences in the foundations of statistical inference can sometimes lead to different interpretations and conclusions from the same dataset. The increased interest in the foundations of statistical inference has led to many publications, and recent vibrant research activities in statistics, applied mathematics, philosophy and other fields of science reflect the importance of this development. The BFF approaches not only bridge foundations and scientific learning, but also facilitate objective and replicable scientific research, and provide scalable computing methodologies for the analysis of big data. Most of the published work typically focusses on a single topic or theme, and the body of work is scattered in different journals. This handbook provides a comprehensive introduction and broad overview of the key developments in the BFF schools of inference. It is intended for researchers and students who wish for an overview of foundations of inference from the BFF perspective and provides a general reference for BFF inference. Key Features: Provides a comprehensive introduction to the key developments in the BFF schools of inference Gives an overview of modern inferential methods, allowing scientists in other fields to expand their knowledge Is accessible for readers with different perspectives and backgrounds |
| parameter of interest definition: Bayesian Thinking, Modeling and Computation , 2005-11-29 This volume describes how to develop Bayesian thinking, modelling and computation both from philosophical, methodological and application point of view. It further describes parametric and nonparametric Bayesian methods for modelling and how to use modern computational methods to summarize inferences using simulation. The book covers wide range of topics including objective and subjective Bayesian inferences with a variety of applications in modelling categorical, survival, spatial, spatiotemporal, Epidemiological, software reliability, small area and micro array data. The book concludes with a chapter on how to teach Bayesian thoughts to nonstatisticians. Critical thinking on causal effects Objective Bayesian philosophy Nonparametric Bayesian methodology Simulation based computing techniques Bioinformatics and Biostatistics |
| parameter of interest definition: Objective Bayesian Inference James O Berger, Jose-miguel Bernardo, Dongchu Sun, 2024-03-06 Bayesian analysis is today understood to be an extremely powerful method of statistical analysis, as well an approach to statistics that is particularly transparent and intuitive. It is thus being extensively and increasingly utilized in virtually every area of science and society that involves analysis of data.A widespread misconception is that Bayesian analysis is a more subjective theory of statistical inference than what is now called classical statistics. This is true neither historically nor in practice. Indeed, objective Bayesian analysis dominated the statistical landscape from roughly 1780 to 1930, long before 'classical' statistics or subjective Bayesian analysis were developed. It has been a subject of intense interest to a multitude of statisticians, mathematicians, philosophers, and scientists. The book, while primarily focusing on the latest and most prominent objective Bayesian methodology, does present much of this fascinating history.The book is written for four different audiences. First, it provides an introduction to objective Bayesian inference for non-statisticians; no previous exposure to Bayesian analysis is needed. Second, the book provides an overview of the development and current state of objective Bayesian analysis and its relationship to other statistical approaches, for those with interest in the philosophy of learning from data. Third, the book presents a careful development of the particular objective Bayesian approach that we recommend, the reference prior approach. Finally, the book presents as much practical objective Bayesian methodology as possible for statisticians and scientists primarily interested in practical applications. |
| parameter of interest definition: Business Process Management Workshops Adela Del Río Ortega, Henrik Leopold, Flávia Maria Santoro, 2021-01-18 This book constitutes revised papers from the International Workshops held at the 18th International Conference on Business Process Management, BPM 2020, during September 13-18, 2020. The conference was planned to take place in Seville, Spain, but changed to an online format due to the COVID-19 pandemic. Papers from the following workshops are included: Workshop on Security and Privacy-Enhanced Business Process Management (SPBP 2020) Workshop on Social and Human Aspects of Business Process Management (BPMS2 2020) Workshop on Business Processes Meet the Internet-of Things (BP-Meet-IoT 2020) Workshop on Artificial Intelligence for Business Process Management (AI4BPM 2020) Workshop BPM in the Era of Digital Innovation and Transformation (BPMinDIT 2020) Workshop on Business Process Intelligence (BPI 2020) Workshop on Declarative, Decision and Hybrid Approaches to Processes (DEC2H 2020) Each of the seven workshops focused on particular aspects of business process management, either from a technical or from a domain perspective. Overall, after a thorough review process there were 28 full and 1 short paper selected from 53 submissions. |
| parameter of interest definition: Handbook of Adaptive Designs in Pharmaceutical and Clinical Development Annpey Pong, Shein-Chung Chow, 2016-04-19 In response to the US FDA's Critical Path Initiative, innovative adaptive designs are being used more and more in clinical trials due to their flexibility and efficiency, especially during early phase development. Handbook of Adaptive Designs in Pharmaceutical and Clinical Development provides a comprehensive and unified presentation of the princip |
编程中，parameter、argument翻译成什么中文最好？ - 知乎
但实际上 Argument 专用于 Actual Argument（实际参数，实参），Parameter 专用于 Formal Parameter（形式参数，形参）。 在上下文没有歧义的情况下，我个人的习惯会将这两个词均 …
What's the difference between an argument and a parameter?
Oct 1, 2008 · In computer programming, two notions of parameter are commonly used, and are referred to as parameters and arguments—or more formally as a formal parameter and an …
How to pass variable as a parameter in Execute SQL Task SSIS?
Sep 30, 2011 · The examples would require parameters that have the following names: The EXCEL and OLED DB connection managers use the parameter names 0 and 1. The ODBC …
How are parameters sent in an HTTP POST request?
parameter=value&also=another When you use a file upload in the form, you use the multipart/form-data encoding instead, which has a different format. It's more complicated, but …
Adding a parameter to the URL with JavaScript - Stack Overflow
Jan 28, 2009 · Here's a vastly simplified version, making tradeoffs for legibility and fewer lines of code instead of micro-optimized performance (and we're talking about a few miliseconds …
What is the "String [] args" parameter in the main method?
May 21, 2009 · The String[] args parameter is an array of Strings passed as parameters when you are running your application through command line in the OS. So, imagine you have compiled …
What is the purpose of the `self` parameter? Why is it needed?
"This works, because the first parameter of length_global, can be re-used as the self parameter in length_new. This would not be possible without an explicit self." - it would work just the same. …
What is "not assignable to parameter of type never" error in …
Sep 20, 2018 · in latest versions of angular, you have to define the type of the variables: if it is a string, you must do like that:
sql - Passing multiple values for a single parameter in Reporting ...
Create a parameter in SSRS that is NOT on the dataset you want to filter. Let's call it Customer; Set Customer param to allow multiple values and set up Available Values tab with the dataset, …
c# - Moq: How to get to a parameter passed to a method of a …
Mar 19, 2015 · Since you use Capture directly in the parameter list, it is far less prone to issues when refactoring a method's parameter list, and therefore makes tests less brittle. With …
编程中，parameter、argument翻译成什么中文最好？ - 知乎
但实际上 Argument 专用于 Actual Argument（实际参数，实参），Parameter 专用于 Formal Parameter（形式参数，形参）。 在上下文没有歧义的情况下，我个人的习惯会将这两个词均翻译为参数，在其他情况下使用实参和形参来对 Argument 和 Parameter 加以区分。
What's the difference between an argument and a parameter?
Oct 1, 2008 · In computer programming, two notions of parameter are commonly used, and are referred to as parameters and arguments—or more formally as a formal parameter and an actual parameter. For example, in the definition of a function such as. y = f(x) = x + 2, x is the formal parameter (the parameter) of the defined function.
How to pass variable as a parameter in Execute SQL Task SSIS?
Sep 30, 2011 · The examples would require parameters that have the following names: The EXCEL and OLED DB connection managers use the parameter names 0 and 1. The ODBC connection type uses 1 and 2. The ADO connection type could use any two parameter names, such as Param1 and Param2, but the parameters must be mapped by their ordinal position in the parameter ...
How are parameters sent in an HTTP POST request?
parameter=value&also=another When you use a file upload in the form, you use the multipart/form-data encoding instead, which has a different format. It's more complicated, but you usually don't need to care what it looks like, so I won't show an example, but it can be good to know that it exists.
Adding a parameter to the URL with JavaScript - Stack Overflow
Jan 28, 2009 · Here's a vastly simplified version, making tradeoffs for legibility and fewer lines of code instead of micro-optimized performance (and we're talking about a few miliseconds difference, realistically... due to the nature of this (operating on the current document's location), this will most likely be ran once on a page).
