A Course In The Large Sample Theory Of Statistical Inference
DOWNLOAD
READ ONLINE
Download A Course In The Large Sample Theory Of Statistical Inference PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get A Course In The Large Sample Theory Of Statistical Inference book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
A Course In The Large Sample Theory Of Statistical Inference
DOWNLOAD
READ ONLINE
Author : W. Jackson Hall
language : en
Publisher: CRC Press
Release Date : 2023-12-14
A Course In The Large Sample Theory Of Statistical Inference written by W. Jackson Hall and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-14 with Mathematics categories.
Provides accessible introduction to large sample theory with moving alternatives Elucidates mathematical concepts using simple practical examples Includes problem sets and solutions for each chapter Uses the moving alternative formulation developed by LeCam but requires a minimum of mathematical prerequisites
A Course In The Large Sample Theory Of Statistical Inference
DOWNLOAD
READ ONLINE
Author : William Jackson Hall
language : en
Publisher:
Release Date : 2023-12
A Course In The Large Sample Theory Of Statistical Inference written by William Jackson Hall and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12 with Statistical hypothesis testing categories.
"This book provides an accessible but rigorous introduction to asymptotic theory in parametric statistical models. Asymptotic results for estimation and testing are derived using the "moving alternative" formulation due to R. A. Fisher and L. Le Cam. Later chapters include discussions of linear rank statistics and of chi-squared tests for contingency table analysis, including situations where parameters are estimated from the complete ungrouped data. The book is based on lecture notes prepared by the first author, subsequently edited, expanded and updated by the second author. Some facility with linear algebra and with real analysis including "epsilon-delta" arguments is required. Concepts and results from measure theory are explained when used. Familiarity with undergraduate probability and statistics including basic concepts of estimation and hypothesis testing is necessary, and experience with applying these concepts to data analysis would be very helpful"--
A Course In Large Sample Theory
DOWNLOAD
READ ONLINE
Author : Thomas S. Ferguson
language : en
Publisher: Routledge
Release Date : 2017-09-06
A Course In Large Sample Theory written by Thomas S. Ferguson and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-06 with Mathematics categories.
A Course in Large Sample Theory is presented in four parts. The first treats basic probabilistic notions, the second features the basic statistical tools for expanding the theory, the third contains special topics as applications of the general theory, and the fourth covers more standard statistical topics. Nearly all topics are covered in their multivariate setting.The book is intended as a first year graduate course in large sample theory for statisticians. It has been used by graduate students in statistics, biostatistics, mathematics, and related fields. Throughout the book there are many examples and exercises with solutions. It is an ideal text for self study.
Asymptotic Statistical Inference
DOWNLOAD
READ ONLINE
Author : Shailaja Deshmukh
language : en
Publisher: Springer Nature
Release Date : 2021-07-05
Asymptotic Statistical Inference written by Shailaja Deshmukh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-05 with Mathematics categories.
The book presents the fundamental concepts from asymptotic statistical inference theory, elaborating on some basic large sample optimality properties of estimators and some test procedures. The most desirable property of consistency of an estimator and its large sample distribution, with suitable normalization, are discussed, the focus being on the consistent and asymptotically normal (CAN) estimators. It is shown that for the probability models belonging to an exponential family and a Cramer family, the maximum likelihood estimators of the indexing parameters are CAN. The book describes some large sample test procedures, in particular, the most frequently used likelihood ratio test procedure. Various applications of the likelihood ratio test procedure are addressed, when the underlying probability model is a multinomial distribution. These include tests for the goodness of fit and tests for contingency tables. The book also discusses a score test and Wald’s test, their relationship with the likelihood ratio test and Karl Pearson’s chi-square test. An important finding is that, while testing any hypothesis about the parameters of a multinomial distribution, a score test statistic and Karl Pearson’s chi-square test statistic are identical. Numerous illustrative examples of differing difficulty level are incorporated to clarify the concepts. For better assimilation of the notions, various exercises are included in each chapter. Solutions to almost all the exercises are given in the last chapter, to motivate students towards solving these exercises and to enable digestion of the underlying concepts. The concepts from asymptotic inference are crucial in modern statistics, but are difficult to grasp in view of their abstract nature. To overcome this difficulty, keeping up with the recent trend of using R software for statistical computations, the book uses it extensively, for illustrating the concepts, verifying the properties of estimators and carrying out various test procedures. The last section of the chapters presents R codes to reveal and visually demonstrate the hidden aspects of different concepts and procedures. Augmenting the theory with R software is a novel and a unique feature of the book. The book is designed primarily to serve as a text book for a one semester introductory course in asymptotic statistical inference, in a post-graduate program, such as Statistics, Bio-statistics or Econometrics. It will also provide sufficient background information for studying inference in stochastic processes. The book will cater to the need of a concise but clear and student-friendly book introducing, conceptually and computationally, basics of asymptotic inference.
Large Sample Inference For Long Memory Processes
DOWNLOAD
READ ONLINE
Author : Donatas Surgailis
language : en
Publisher: World Scientific Publishing Company
Release Date : 2012-04-27
Large Sample Inference For Long Memory Processes written by Donatas Surgailis 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 2012-04-27 with Mathematics categories.
Box and Jenkins (1970) made the idea of obtaining a stationary time series by differencing the given, possibly nonstationary, time series popular. Numerous time series in economics are found to have this property. Subsequently, Granger and Joyeux (1980) and Hosking (1981) found examples of time series whose fractional difference becomes a short memory process, in particular, a white noise, while the initial series has unbounded spectral density at the origin, i.e. exhibits long memory.Further examples of data following long memory were found in hydrology and in network traffic data while in finance the phenomenon of strong dependence was established by dramatic empirical success of long memory processes in modeling the volatility of the asset prices and power transforms of stock market returns.At present there is a need for a text from where an interested reader can methodically learn about some basic asymptotic theory and techniques found useful in the analysis of statistical inference procedures for long memory processes. This text makes an attempt in this direction. The authors provide in a concise style a text at the graduate level summarizing theoretical developments both for short and long memory processes and their applications to statistics. The book also contains some real data applications and mentions some unsolved inference problems for interested researchers in the field./a
A Course In Mathematical Statistics And Large Sample Theory
DOWNLOAD
READ ONLINE
Author : Rabi Bhattacharya
language : en
Publisher: Springer
Release Date : 2016-08-13
A Course In Mathematical Statistics And Large Sample Theory written by Rabi Bhattacharya and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-13 with Mathematics categories.
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.
Essential Statistical Inference
DOWNLOAD
READ ONLINE
Author : Dennis D. Boos
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-02-06
Essential Statistical Inference written by Dennis D. Boos 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 2013-02-06 with Mathematics categories.
This book is for students and researchers who have had a first year graduate level mathematical statistics course. It covers classical likelihood, Bayesian, and permutation inference; an introduction to basic asymptotic distribution theory; and modern topics like M-estimation, the jackknife, and the bootstrap. R code is woven throughout the text, and there are a large number of examples and problems. An important goal has been to make the topics accessible to a wide audience, with little overt reliance on measure theory. A typical semester course consists of Chapters 1-6 (likelihood-based estimation and testing, Bayesian inference, basic asymptotic results) plus selections from M-estimation and related testing and resampling methodology. Dennis Boos and Len Stefanski are professors in the Department of Statistics at North Carolina State. Their research has been eclectic, often with a robustness angle, although Stefanski is also known for research concentrated on measurement error, including a co-authored book on non-linear measurement error models. In recent years the authors have jointly worked on variable selection methods.
Statistical Inference
DOWNLOAD
READ ONLINE
Author : S.D. Silvey
language : en
Publisher: CRC Press
Release Date : 1975-03-01
Statistical Inference written by S.D. Silvey and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1975-03-01 with Mathematics categories.
Minimum-variance unbiased estimation; The method of least squares; The method of maximum likelihood; Confidence sets; Hypothesis testing; The likelihood-ratio test and alternative 'large-sample' equivalents of it 108; Sequential tests; Non-parametric methods; The bayesian approach; An introduction to decision theory.
Introductory Statistical Inference
DOWNLOAD
READ ONLINE
Author : Nitis Mukhopadhyay
language : en
Publisher: CRC Press
Release Date : 2006-02-07
Introductory Statistical Inference written by Nitis Mukhopadhyay and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-02-07 with Mathematics categories.
This gracefully organized text reveals the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises, figures, tables, and computer simulations to develop and illustrate concepts. Drills and boxed summaries emphasize and reinforce important ideas and special techniques. Beginning with a review of the basic concepts and methods in probability theory, moments, and moment generating functions, the author moves to more intricate topics. Introductory Statistical Inference studies multivariate random variables, exponential families of distributions, and standard probability inequalities. It develops the Helmert transformation for normal distributions, introduces the notions of convergence, and spotlights the central limit theorems. Coverage highlights sampling distributions, Basu's theorem, Rao-Blackwellization and the Cramér-Rao inequality. The text also provides in-depth coverage of Lehmann-Scheffé theorems, focuses on tests of hypotheses, describes Bayesian methods and the Bayes' estimator, and develops large-sample inference. The author provides a historical context for statistics and statistical discoveries and answers to a majority of the end-of-chapter exercises. Designed primarily for a one-semester, first-year graduate course in probability and statistical inference, this text serves readers from varied backgrounds, ranging from engineering, economics, agriculture, and bioscience to finance, financial mathematics, operations and information management, and psychology.
Theory Of Statistics
DOWNLOAD
READ ONLINE
Author : Mark J. Schervish
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Theory Of Statistics written by Mark J. Schervish 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.
The aim of this graduate textbook is to provide a comprehensive advanced course in the theory of statistics covering those topics in estimation, testing, and large sample theory which a graduate student might typically need to learn as preparation for work on a Ph.D. An important strength of this book is that it provides a mathematically rigorous and even-handed account of both Classical and Bayesian inference in order to give readers a broad perspective. For example, the "uniformly most powerful" approach to testing is contrasted with available decision-theoretic approaches.