Models For Probability And Statistical Inference

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Probability And Statistical Inference

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Author : Robert Bartoszynski
language : en
Publisher: John Wiley & Sons
Release Date : 2007-11-16

Probability And Statistical Inference written by Robert Bartoszynski 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 2007-11-16 with Mathematics categories.

Now updated in a valuable new edition—this user-friendly book focuses on understanding the "why" of mathematical statistics Probability and Statistical Inference, Second Edition introduces key probability and statis-tical concepts through non-trivial, real-world examples and promotes the developmentof intuition rather than simple application. With its coverage of the recent advancements in computer-intensive methods, this update successfully provides the comp-rehensive tools needed to develop a broad understanding of the theory of statisticsand its probabilistic foundations. This outstanding new edition continues to encouragereaders to recognize and fully understand the why, not just the how, behind the concepts,theorems, and methods of statistics. Clear explanations are presented and appliedto various examples that help to impart a deeper understanding of theorems and methods—from fundamental statistical concepts to computational details. Additional features of this Second Edition include: A new chapter on random samples Coverage of computer-intensive techniques in statistical inference featuring Monte Carlo and resampling methods, such as bootstrap and permutation tests, bootstrap confidence intervals with supporting R codes, and additional examples available via the book's FTP site Treatment of survival and hazard function, methods of obtaining estimators, and Bayes estimating Real-world examples that illuminate presented concepts Exercises at the end of each section Providing a straightforward, contemporary approach to modern-day statistical applications, Probability and Statistical Inference, Second Edition is an ideal text for advanced undergraduate- and graduate-level courses in probability and statistical inference. It also serves as a valuable reference for practitioners in any discipline who wish to gain further insight into the latest statistical tools.

Probability Theory And Statistical Inference

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Author : Aris Spanos
language : en
Publisher: Cambridge University Press
Release Date : 2019-09-19

Probability Theory And Statistical Inference written by Aris Spanos and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-19 with Business & Economics categories.

This empirical research methods course enables informed implementation of statistical procedures, giving rise to trustworthy evidence.

Modelling Inference And Data Analysis

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Author : Miltiadis C. Mavrakakis
language : en
Publisher: Chapman and Hall/CRC
Release Date : 2014-12-15

Modelling Inference And Data Analysis written by Miltiadis C. Mavrakakis and has been published by Chapman and Hall/CRC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-15 with Mathematics categories.

Modelling, Inference and Data Analysis brings together key topics in mathematical statistics and presents them in a rigorous yet accessible manner. It covers aspects of probability, distribution theory and random processes that are fundamental to a proper understanding of inference. The book also discusses the properties of estimators constructed from a random sample of ends, with sections on methods for estimating parameters in time series models and computationally intensive inferential techniques. The text challenges and excites the more mathematically able students while providing an approachable explanation of advanced statistical concepts for students who struggle with existing texts.

An Introduction To Probability And Statistical Inference

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Author : George G. Roussas
language : en
Publisher: Elsevier
Release Date : 2003-02-13

An Introduction To Probability And Statistical Inference written by George G. Roussas and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-02-13 with Social Science categories.

Roussas introduces readers with no prior knowledge in probability or statistics, to a thinking process to guide them toward the best solution to a posed question or situation. An Introduction to Probability and Statistical Inference provides a plethora of examples for each topic discussed, giving the reader more experience in applying statistical methods to different situations. "The text is wonderfully written and has the mostcomprehensive range of exercise problems that I have ever seen." — Tapas K. Das, University of South Florida"The exposition is great; a mixture between conversational tones and formal mathematics; the appropriate combination for a math text at [this] level. In my examination I could find no instance where I could improve the book." — H. Pat Goeters, Auburn, University, Alabama* Contains more than 200 illustrative examples discussed in detail, plus scores of numerical examples and applications* Chapters 1-8 can be used independently for an introductory course in probability* Provides a substantial number of proofs

Introduction To Linear Models And Statistical Inference

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Author : Steven J. Janke
language : en
Publisher: John Wiley & Sons
Release Date : 2005-09-15

Introduction To Linear Models And Statistical Inference written by Steven J. Janke 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 2005-09-15 with Mathematics categories.

A multidisciplinary approach that emphasizes learning by analyzing real-world data sets This book is the result of the authors' hands-on classroom experience and is tailored to reflect how students best learn to analyze linear relationships. The text begins with the introduction of four simple examples of actual data sets. These examples are developed and analyzed throughout the text, and more complicated examples of data sets are introduced along the way. Taking a multidisciplinary approach, the book traces the conclusion of the analyses of data sets taken from geology, biology, economics, psychology, education, sociology, and environmental science. As students learn to analyze the data sets, they master increasingly sophisticated linear modeling techniques, including: * Simple linear models * Multivariate models * Model building * Analysis of variance (ANOVA) * Analysis of covariance (ANCOVA) * Logistic regression * Total least squares The basics of statistical analysis are developed and emphasized, particularly in testing the assumptions and drawing inferences from linear models. Exercises are included at the end of each chapter to test students' skills before moving on to more advanced techniques and models. These exercises are marked to indicate whether calculus, linear algebra, or computer skills are needed. Unlike other texts in the field, the mathematics underlying the models is carefully explained and accessible to students who may not have any background in calculus or linear algebra. Most chapters include an optional final section on linear algebra for students interested in developing a deeper understanding. The many data sets that appear in the text are available on the book's Web site. The MINITAB(r) software program is used to illustrate many of the examples. For students unfamiliar with MINITAB(r), an appendix introduces the key features needed to study linear models. With its multidisciplinary approach and use of real-world data sets that bring the subject alive, this is an excellent introduction to linear models for students in any of the natural or social sciences.

Statistical Inference

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Author : Murray Aitkin
language : en
Publisher: CRC Press
Release Date : 2010-06-02

Statistical Inference written by Murray Aitkin and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-06-02 with Mathematics categories.

Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian treatment of parameter inference and model comparisons that can be used with simple diffuse prior specifications. This novel approach provides new solutions to difficult model comparison problems and offers direct

Stochastic Epidemic Models With Inference

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Author : Tom Britton
language : en
Publisher: Springer Nature
Release Date : 2019-11-30

Stochastic Epidemic Models With Inference written by Tom Britton and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-30 with Mathematics categories.

Focussing on stochastic models for the spread of infectious diseases in a human population, this book is the outcome of a two-week ICPAM/CIMPA school on "Stochastic models of epidemics" which took place in Ziguinchor, Senegal, December 5–16, 2015. The text is divided into four parts, each based on one of the courses given at the school: homogeneous models (Tom Britton and Etienne Pardoux), two-level mixing models (David Sirl and Frank Ball), epidemics on graphs (Viet Chi Tran), and statistics for epidemic models (Catherine Larédo). The CIMPA school was aimed at PhD students and Post Docs in the mathematical sciences. Parts (or all) of this book can be used as the basis for traditional or individual reading courses on the topic. For this reason, examples and exercises (some with solutions) are provided throughout.

Stochastic Modeling And Mathematical Statistics

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Author : Francisco J. Samaniego
language : en
Publisher: CRC Press
Release Date : 2014-01-14

Stochastic Modeling And Mathematical Statistics written by Francisco J. Samaniego and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-14 with Mathematics categories.

Provides a Solid Foundation for Statistical Modeling and Inference and Demonstrates Its Breadth of Applicability Stochastic Modeling and Mathematical Statistics: A Text for Statisticians and Quantitative Scientists addresses core issues in post-calculus probability and statistics in a way that is useful for statistics and mathematics majors as well

Model Based Inference In The Life Sciences

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Author : David R. Anderson
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-12-22

Model Based Inference In The Life Sciences written by David R. Anderson 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 2007-12-22 with Science categories.

This textbook introduces a science philosophy called "information theoretic" based on Kullback-Leibler information theory. It focuses on a science philosophy based on "multiple working hypotheses" and statistical models to represent them. The text is written for people new to the information-theoretic approaches to statistical inference, whether graduate students, post-docs, or professionals. Readers are however expected to have a background in general statistical principles, regression analysis, and some exposure to likelihood methods. This is not an elementary text as it assumes reasonable competence in modeling and parameter estimation.

Models For Probability And Statistical Inference

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Author : James H. Stapleton
language : en
Publisher: John Wiley & Sons
Release Date : 2007-12-14

Models For Probability And Statistical Inference written by James H. Stapleton 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 2007-12-14 with Mathematics categories.

This concise, yet thorough, book is enhanced with simulations and graphs to build the intuition of readers Models for Probability and Statistical Inference was written over a five-year period and serves as a comprehensive treatment of the fundamentals of probability and statistical inference. With detailed theoretical coverage found throughout the book, readers acquire the fundamentals needed to advance to more specialized topics, such as sampling, linear models, design of experiments, statistical computing, survival analysis, and bootstrapping. Ideal as a textbook for a two-semester sequence on probability and statistical inference, early chapters provide coverage on probability and include discussions of: discrete models and random variables; discrete distributions including binomial, hypergeometric, geometric, and Poisson; continuous, normal, gamma, and conditional distributions; and limit theory. Since limit theory is usually the most difficult topic for readers to master, the author thoroughly discusses modes of convergence of sequences of random variables, with special attention to convergence in distribution. The second half of the book addresses statistical inference, beginning with a discussion on point estimation and followed by coverage of consistency and confidence intervals. Further areas of exploration include: distributions defined in terms of the multivariate normal, chi-square, t, and F (central and non-central); the one- and two-sample Wilcoxon test, together with methods of estimation based on both; linear models with a linear space-projection approach; and logistic regression. Each section contains a set of problems ranging in difficulty from simple to more complex, and selected answers as well as proofs to almost all statements are provided. An abundant amount of figures in addition to helpful simulations and graphs produced by the statistical package S-Plus(r) are included to help build the intuition of readers.