Parametric Statistical Models And Likelihood

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Parametric Statistical Models And Likelihood

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Author : Ole E Barndorff-Nielsen
language : en
Publisher:
Release Date : 1988-12-01

Parametric Statistical Models And Likelihood written by Ole E Barndorff-Nielsen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988-12-01 with categories.

A Parametric Approach To Nonparametric Statistics

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Author : Mayer Alvo
language : en
Publisher: Springer
Release Date : 2018-10-12

A Parametric Approach To Nonparametric Statistics written by Mayer Alvo and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-12 with Mathematics categories.

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.

Parametric Statistical Inference

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Author : James K. Lindsey
language : en
Publisher: Oxford University Press
Release Date : 1996

Parametric Statistical Inference written by James K. Lindsey and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Mathematics categories.

Inference involves drawing conclusions about some general phenomenon from limited empirical observations in the face of random variability. Two central unifying components of statistics are the likelihood function and the exponential family. These are here brought together for the first time as the central themes of a book on statistical inference. This book is appropriate as an advanced undergraduate or graduate text in mathematical statistics.

Probability Models And Statistical Analyses For Ranking Data

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Author : Michael A. Fligner
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

Probability Models And Statistical Analyses For Ranking Data written by Michael A. Fligner and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.

In June of 1990, a conference was held on Probablity Models and Statisti cal Analyses for Ranking Data, under the joint auspices of the American Mathematical Society, the Institute for Mathematical Statistics, and the Society of Industrial and Applied Mathematicians. The conference took place at the University of Massachusetts, Amherst, and was attended by 36 participants, including statisticians, mathematicians, psychologists and sociologists from the United States, Canada, Israel, Italy, and The Nether lands. There were 18 presentations on a wide variety of topics involving ranking data. This volume is a collection of 14 of these presentations, as well as 5 miscellaneous papers that were contributed by conference participants. We would like to thank Carole Kohanski, summer program coordinator for the American Mathematical Society, for her assistance in arranging the conference; M. Steigerwald for preparing the manuscripts for publication; Martin Gilchrist at Springer-Verlag for editorial advice; and Persi Diaconis for contributing the Foreword. Special thanks go to the anonymous referees for their careful readings and constructive comments. Finally, we thank the National Science Foundation for their sponsorship of the AMS-IMS-SIAM Joint Summer Programs. Contents Preface vii Conference Participants xiii Foreword xvii 1 Ranking Models with Item Covariates 1 D. E. Critchlow and M. A. Fligner 1. 1 Introduction. . . . . . . . . . . . . . . 1 1. 2 Basic Ranking Models and Their Parameters 2 1. 3 Ranking Models with Covariates 8 1. 4 Estimation 9 1. 5 Example. 11 1. 6 Discussion. 14 1. 7 Appendix . 15 1. 8 References.

Mathematical Statistics

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Author : Richard J. Rossi
language : en
Publisher: John Wiley & Sons
Release Date : 2018-10-02

Mathematical Statistics written by Richard J. Rossi and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-02 with Mathematics categories.

Presents a unified approach to parametric estimation, confidence intervals, hypothesis testing, and statistical modeling, which are uniquely based on the likelihood function This book addresses mathematical statistics for upper-undergraduates and first year graduate students, tying chapters on estimation, confidence intervals, hypothesis testing, and statistical models together to present a unifying focus on the likelihood function. It also emphasizes the important ideas in statistical modeling, such as sufficiency, exponential family distributions, and large sample properties. Mathematical Statistics: An Introduction to Likelihood Based Inference makes advanced topics accessible and understandable and covers many topics in more depth than typical mathematical statistics textbooks. It includes numerous examples, case studies, a large number of exercises ranging from drill and skill to extremely difficult problems, and many of the important theorems of mathematical statistics along with their proofs. In addition to the connected chapters mentioned above, Mathematical Statistics covers likelihood-based estimation, with emphasis on multidimensional parameter spaces and range dependent support. It also includes a chapter on confidence intervals, which contains examples of exact confidence intervals along with the standard large sample confidence intervals based on the MLE's and bootstrap confidence intervals. There’s also a chapter on parametric statistical models featuring sections on non-iid observations, linear regression, logistic regression, Poisson regression, and linear models. Prepares students with the tools needed to be successful in their future work in statistics data science Includes practical case studies including real-life data collected from Yellowstone National Park, the Donner party, and the Titanic voyage Emphasizes the important ideas to statistical modeling, such as sufficiency, exponential family distributions, and large sample properties Includes sections on Bayesian estimation and credible intervals Features examples, problems, and solutions Mathematical Statistics: An Introduction to Likelihood Based Inference is an ideal textbook for upper-undergraduate and graduate courses in probability, mathematical statistics, and/or statistical inference.

Likelihood Methods In Biology And Ecology

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Author : Michael Brimacombe
language : en
Publisher: CRC Press
Release Date : 2018-12-18

Likelihood Methods In Biology And Ecology written by Michael Brimacombe and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-18 with Mathematics categories.

This book emphasizes the importance of the likelihood function in statistical theory and applications and discusses it in the context of biology and ecology. Bayesian and frequentist methods both use the likelihood function and provide differing but related insights. This is examined here both through review of basic methodology and also the integr

Applied Statistical Modelling For Ecologists

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Author : Marc Kéry
language : en
Publisher: Elsevier
Release Date : 2024-07-18

Applied Statistical Modelling For Ecologists written by Marc Kéry and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-18 with Science categories.

**2025 PROSE Award Finalist in Environmental Science**Applied Statistical Modelling for Ecologists provides a gentle introduction to the essential models of applied statistics: linear models, generalized linear models, mixed and hierarchical models. All models are fit with both a likelihood and a Bayesian approach, using several powerful software packages widely used in research publications: JAGS, NIMBLE, Stan, and TMB. In addition, the foundational method of maximum likelihood is explained in a manner that ecologists can really understand.This book is the successor of the widely used Introduction to WinBUGS for Ecologists (Kéry, Academic Press, 2010). Like its parent, it is extremely effective for both classroom use and self-study, allowing students and researchers alike to quickly learn, understand, and carry out a very wide range of statistical modelling tasks.The examples in Applied Statistical Modelling for Ecologists come from ecology and the environmental sciences, but the underlying statistical models are very widely used by scientists across many disciplines. This book will be useful for anybody who needs to learn and quickly become proficient in statistical modelling, with either a likelihood or a Bayesian focus, and in the model-fitting engines covered, including the three latest packages NIMBLE, Stan, and TMB. - Contains a concise and gentle introduction to probability and applied statistics as needed in ecology and the environmental sciences - Covers the foundations of modern applied statistical modelling - Gives a comprehensive, applied introduction to what currently are the most widely used and most exciting, cutting-edge model fitting software packages: JAGS, NIMBLE, Stan, and TMB - Provides a highly accessible applied introduction to the two dominant methods of fitting parametric statistical models: maximum likelihood and Bayesian posterior inference - Details the principles of model building, model checking and model selection - Adopts a "Rosetta Stone" approach, wherein understanding of one software, and of its associated language, will be greatly enhanced by seeing the analogous code in other engines - Provides all code available for download for students, at https://www.elsevier.com/books-and-journals/book-companion/9780443137150

Modes Of Parametric Statistical Inference

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Author : Seymour Geisser
language : en
Publisher: John Wiley & Sons
Release Date : 2006-01-27

Modes Of Parametric Statistical Inference written by Seymour Geisser and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-01-27 with Mathematics categories.

A fascinating investigation into the foundations of statistical inference This publication examines the distinct philosophical foundations of different statistical modes of parametric inference. Unlike many other texts that focus on methodology and applications, this book focuses on a rather unique combination of theoretical and foundational aspects that underlie the field of statistical inference. Readers gain a deeper understanding of the evolution and underlying logic of each mode as well as each mode's strengths and weaknesses. The book begins with fascinating highlights from the history of statistical inference. Readers are given historical examples of statistical reasoning used to address practical problems that arose throughout the centuries. Next, the book goes on to scrutinize four major modes of statistical inference: * Frequentist * Likelihood * Fiducial * Bayesian The author provides readers with specific examples and counterexamples of situations and datasets where the modes yield both similar and dissimilar results, including a violation of the likelihood principle in which Bayesian and likelihood methods differ from frequentist methods. Each example is followed by a detailed discussion of why the results may have varied from one mode to another, helping the reader to gain a greater understanding of each mode and how it works. Moreover, the author provides considerable mathematical detail on certain points to highlight key aspects of theoretical development. The author's writing style and use of examples make the text clear and engaging. This book is fundamental reading for graduate-level students in statistics as well as anyone with an interest in the foundations of statistics and the principles underlying statistical inference, including students in mathematics and the philosophy of science. Readers with a background in theoretical statistics will find the text both accessible and absorbing.

Stevens Handbook Of Experimental Psychology And Cognitive Neuroscience Methodology

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Author :
language : en
Publisher: John Wiley & Sons
Release Date : 2018-02-12

Stevens Handbook Of Experimental Psychology And Cognitive Neuroscience Methodology written by and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-12 with Psychology categories.

V. Methodology: E. J. Wagenmakers (Volume Editor) Topics covered include methods and models in categorization; cultural consensus theory; network models for clinical psychology; response time modeling; analyzing neural time series data; models and methods for reinforcement learning; convergent methods of memory research; theories for discriminating signal from noise; bayesian cognitive modeling; mathematical modeling in cognition and cognitive neuroscience; the stop-signal paradigm; hypothesis testing and statistical inference; model comparison in psychology; fmri; neural recordings; open science; neural networks and neurocomputational modeling; serial versus parallel processing; methods in psychophysics.

Principles Of Statistical Inference From A Neo Fisherian Perspective

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Author : Luigi Pace
language : en
Publisher: World Scientific Publishing Company
Release Date : 1997-08-05

Principles Of Statistical Inference From A Neo Fisherian Perspective written by Luigi Pace and has been published by World Scientific Publishing Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997-08-05 with Mathematics categories.

In this book, an integrated introduction to statistical inference is provided from a frequentist likelihood-based viewpoint. Classical results are presented together with recent developments, largely built upon ideas due to R.A. Fisher. The term “neo-Fisherian” highlights this.After a unified review of background material (statistical models, likelihood, data and model reduction, first-order asymptotics) and inference in the presence of nuisance parameters (including pseudo-likelihoods), a self-contained introduction is given to exponential families, exponential dispersion models, generalized linear models, and group families. Finally, basic results of higher-order asymptotics are introduced (index notation, asymptotic expansions for statistics and distributions, and major applications to likelihood inference).The emphasis is more on general concepts and methods than on regularity conditions. Many examples are given for specific statistical models. Each chapter is supplemented with problems and bibliographic notes. This volume can serve as a textbook in intermediate-level undergraduate and postgraduate courses in statistical inference.