likelihood methods in statistics

Likelihood Methods in Survival Analysis：With R Examples

作者：Jun (Department of Statistics Ma Faculty of Science and Engineering Macquarie University); Malcolm Hudson; Annabel (Macquarie University Webb Australia) 出版社：Taylor & Francis Inc 出版日：2024/10/01 裝訂：精裝

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2.

Likelihood Methods in Statistics

作者：Thomas A. Severini 出版社：Oxford Univ Pr on Demand 出版日：2001/01/18 裝訂：精裝

This book is suitable for students and statisticians with a knowledge of graduate-level statistical theory. At least some background in classical likelihood theory is recommended, but the opening cha

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3.

Bayesian Likelihood Methods in Ecology And Biology

作者：Michael Brimacombe 出版社：Taylor & Francis 出版日：2014/02/26 裝訂：精裝

Through an integrated and comparative approach, Bayesian Likelihood Methods in Ecology and Biology provides a clear guide to the development, application, and interpretation of Bayesian statistical me

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4.

Likelihood Methods in Sample Surveys

作者：Alan Welsh; Suojin Wang 出版社：CRC Press UK 出版日：2012/05/15 裝訂：精裝

Sample surveys provide data used by researchers in a large range of disciplines to analyze important relationships using well-established and widely used likelihood methods. The methods used to select

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Econometric Applications of Maximum Likelihood Methods

90折

5.

Econometric Applications of Maximum Likelihood Methods

作者：Jan Salomon Cramer 出版社：Cambridge Univ Pr 出版日：1989/07/01 裝訂：平裝

The advent of electronic computing permits the empirical analysis of economic models of far greater subtlety and rigour than before, when many interesting ideas were not followed up because the calculations involved made this impracticable. The estimation and testing of these more intricate models is usually based on the method of Maximum Likelihood, which is a well-established branch of mathematical statistics. Its use in econometrics has led to the development of a number of special techniques; the specific conditions of econometric research moreover demand certain changes in the interpretation of the basic argument. This book is a self-contained introduction to this field. It consists of three parts. The first deals with general features of Maximum Likelihood methods; the second with linear and nonlinear regression; and the third with discrete choice and related micro-economic models. Readers should already be familiar with elementary statistical theory, with applied econometric res

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定價：1689 元，優惠價：9 1520

Likelihood Methods in Biology and Ecology: A Modern Approach to Statistics

90折

6.

Likelihood Methods in Biology and Ecology: A Modern Approach to Statistics

作者：Michael Brimacombe 出版社：CRC PR INC 出版日：2020/12/20 裝訂：平裝

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定價：2790 元，優惠價：9 2511

7.

Likelihood, Bayesian, and McMc Methods in Quantitative Genetics

作者：Daniel Sorensen; Daniel Gianola 出版社：Springer Verlag 出版日：2002/10/01 裝訂：精裝

This book provides the foundations of likelihood, Bayesian and MCMC methods in the context of genetic analysis of quantitative traits. Considerably more detail is offered than what may be warranted f

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8.

Numerical Methods of Statistics

作者：John F. Monahan 出版社：Cambridge Univ Pr 出版日：2011/04/18 裝訂：精裝

This book explains how computer software is designed to perform the tasks required for sophisticated statistical analysis. For statisticians, it examines the nitty-gritty computational problems behind statistical methods. For mathematicians and computer scientists, it looks at the application of mathematical tools to statistical problems. The first half of the book offers a basic background in numerical analysis that emphasizes issues important to statisticians. The next several chapters cover a broad array of statistical tools, such as maximum likelihood and nonlinear regression. The author also treats the application of numerical tools; numerical integration and random number generation are explained in a unified manner reflecting complementary views of Monte Carlo methods. Each chapter contains exercises that range from simple questions to research problems. Most of the examples are accompanied by demonstration and source code available from the author's website. New in this second

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9.

Empirical Likelihood and Quantile Methods for Time Series ― Efficiency, Robustness, Optimality, and Prediction

作者：Yan Liu; Fumiya Akashi; Masanobu Taniguchi 出版社：Springer Nature 出版日：2018/12/17 裝訂：平裝

This book integrates the fundamentals of asymptotic theory of statistical inference for time series under nonstandard settings, e.g., infinite variance processes, not only from the point of view of ef

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定價：3499 元，優惠價：1 3499

10.

Core Statistics

作者：Simon N. Wood 出版社：Cambridge Univ Pr 出版日：2015/04/30 裝訂：精裝

Based on a starter course for beginning graduate students, Core Statistics provides concise coverage of the fundamentals of inference for parametric statistical models, including both theory and practical numerical computation. The book considers both frequentist maximum likelihood and Bayesian stochastic simulation while focusing on general methods applicable to a wide range of models and emphasizing the common questions addressed by the two approaches. This compact package serves as a lively introduction to the theory and tools that a beginning graduate student needs in order to make the transition to serious statistical analysis: inference; modeling; computation, including some numerics; and the R language. Aimed also at any quantitative scientist who uses statistical methods, this book will deepen readers' understanding of why and when methods work and explain how to develop suitable methods for non-standard situations, such as in ecology, big data and genomics.

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滿額折

11.

Core Statistics

作者：Simon N. Wood 出版社：Cambridge Univ Pr 出版日：2015/04/30 裝訂：平裝

Based on a starter course for beginning graduate students, Core Statistics provides concise coverage of the fundamentals of inference for parametric statistical models, including both theory and practical numerical computation. The book considers both frequentist maximum likelihood and Bayesian stochastic simulation while focusing on general methods applicable to a wide range of models and emphasizing the common questions addressed by the two approaches. This compact package serves as a lively introduction to the theory and tools that a beginning graduate student needs in order to make the transition to serious statistical analysis: inference; modeling; computation, including some numerics; and the R language. Aimed also at any quantitative scientist who uses statistical methods, this book will deepen readers' understanding of why and when methods work and explain how to develop suitable methods for non-standard situations, such as in ecology, big data and genomics.

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定價：1819 元，優惠價：9 1637

12.

Maximum Likelihood Estimation And Inference - With Examples In R, Sas, And Admb

作者：Millar 出版社：John Wiley & Sons Inc 出版日：2011/09/02 裝訂：精裝

Applied Likelihood Methods provides an accessible and practical introduction to likelihood modeling, supported by examples and software. The book features applications from a range of disciplines, inc

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13.

Applied Asymptotics：Case Studies in Small-Sample Statistics

作者：A. R. Brazzale 出版社：Cambridge Univ Pr 出版日：2007/05/31 裝訂：精裝

In fields such as biology, medical sciences, sociology, and economics researchers often face the situation where the number of available observations, or the amount of available information, is sufficiently small that approximations based on the normal distribution may be unreliable. Theoretical work over the last quarter-century has led to new likelihood-based methods that lead to very accurate approximations in finite samples, but this work has had limited impact on statistical practice. This book illustrates by means of realistic examples and case studies how to use the new theory, and investigates how and when it makes a difference to the resulting inference. The treatment is oriented towards practice and comes with code in the R language (available from the web) which enables the methods to be applied in a range of situations of interest to practitioners. The analysis includes some comparisons of higher order likelihood inference with bootstrap or Bayesian methods.

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Introduction to Probability and Statistics from a Bayesian Viewpoint, Part 2 ─ Inference

90折

14.

Introduction to Probability and Statistics from a Bayesian Viewpoint, Part 2 ─ Inference

作者：D. V. Lindley 出版社：Cambridge Univ Pr 出版日：1980/03/20 裝訂：平裝

The two parts of this book treat probability and statistics as mathematical disciplines and with the same degree of rigour as is adopted for other branches of applied mathematics at the level of a British honours degree. They contain the minimum information about these subjects that any honours graduate in mathematics ought to know. They are written primarily for general mathematicians, rather than for statistical specialists or for natural scientists who need to use statistics in their work. No previous knowledge of probability or statistics is assumed, though familiarity with calculus and linear algebra is required. The first volume takes the theory of probability sufficiently far to be able to discuss the simpler random processes, for example, queueing theory and random walks. The second volume deals with statistics, the theory of making valid inferences from experimental data, and includes an account of the methods of least squares and maximum likelihood; it uses the results of the

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定價：2014 元，優惠價：9 1813

Introduction to Probability and Statistics from a Bayesian Viewpoint, Part 1 ─ Probability

90折

15.

Introduction to Probability and Statistics from a Bayesian Viewpoint, Part 1 ─ Probability

作者：D. V. Lindley 出版社：Cambridge Univ Pr 出版日：1980/03/20 裝訂：平裝

The two parts of this book treat probability and statistics as mathematical disciplines and with the same degree of rigour as is adopted for other branches of applied mathematics at the level of a British honours degree. They contain the minimum information about these subjects that any honours graduate in mathematics ought to know. They are written primarily for general mathematicians, rather than for statistical specialists or for natural scientists who need to use statistics in their work. No previous knowledge of probability or statistics is assumed, though familiarity with calculus and linear algebra is required. The first volume takes the theory of probability sufficiently far to be able to discuss the simpler random processes, for example, queueing theory and random walks. The second volume deals with statistics, the theory of making valid inferences from experimental data, and includes an account of the methods of least squares and maximum likelihood; it uses the results of the

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定價：2599 元，優惠價：9 2339

90折

16.

Empirical Processes in M-Estimation

作者：Sara A. van de Geer 出版社：Cambridge Univ Pr 出版日：2009/11/19 裝訂：平裝

The theory of empirical processes provides valuable tools for the development of asymptotic theory in (nonparametric) statistical models, and makes possible the unified treatment of a number of them. This book reveals the relation between the asymptotic behaviour of M-estimators and the complexity of parameter space. Virtually all results are proved using only elementary ideas developed within the book; there is minimal recourse to abstract theoretical results. To make the results concrete, a detailed treatment is presented for two important examples of M-estimation, namely maximum likelihood and least squares. The theory also covers estimation methods using penalties and sieves. Many illustrative examples are given, including the Grenander estimator, estimation of functions of bounded variation, smoothing splines, partially linear models, mixture models and image analysis. Graduate students and professionals in statistics as well as those with an interest in applications, to such area

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定價：2729 元，優惠價：9 2456

17.

Analysis of Survival Data With Dependent Censoring ― Copula-based Approaches

作者：Takeshi Emura; Yi-hau Chen; Shigeyuki Matsui; Virginie Rondeau 出版社：Springer Verlag 出版日：2018/04/13 裝訂：平裝

This book introduces readers to copula-based statistical methods for analyzing survival data involving dependent censoring. Primarily focusing on likelihood-based methods performed under copula models

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Bayesian Methods：An Analysis for Statisticians and Interdisciplinary Researchers

90折

18.

Bayesian Methods：An Analysis for Statisticians and Interdisciplinary Researchers

作者：Thomas Leonard 出版社：Cambridge Univ Pr 出版日：2001/08/06 裝訂：平裝

This book describes the Bayesian approach to statistics at a level suitable for final year undergraduate and Masters students. It is unusual in presenting Bayesian statistics with a practical flavor and an emphasis on mainstream statistics, showing how to infer scientific, medical, and social conclusions from numerical data. The authors draw on many years of experience with practical and research programs and describe many statistical methods, not readily available elsewhere. A first chapter on Fisherian methods, together with a strong overall emphasis on likelihood, makes the text suitable for mainstream statistics courses whose instructors wish to follow mixed or comparative philosophies. The other chapters contain important sections relating to many areas of statistics such as the linear model, categorical data analysis, time series and forecasting, mixture models, survival analysis, Bayesian smoothing, and non-linear random effects models. The text includes a large number of practi

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定價：2729 元，優惠價：9 2456

Statistical Inference for Spatial Processes

90折

19.

Statistical Inference for Spatial Processes

作者：B. D. Ripley 出版社：Cambridge Univ Pr 出版日：1991/07/18 裝訂：平裝

The study of spatial processes and their applications is an important topic in statistics and finds wide application particularly in computer vision and image processing. This book is devoted to statistical inference in spatial statistics and is intended for specialists needing an introduction to the subject and to its applications. One of the themes of the book is the demonstration of how these techniques give new insights into classical procedures (including new examples in likelihood theory) and newer statistical paradigms such as Monte-Carlo inference and pseudo-likelihood. Professor Ripley also stresses the importance of edge effects and of lack of a unique asymptotic setting in spatial problems. Throughout, the author discusses the foundational issues posed and the difficulties, both computational and philosophical, which arise. The final chapters consider image restoration and segmentation methods and the averaging and summarising of images. Thus, the book will find wide appeal

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定價：2794 元，優惠價：9 2515

20.

Bayesian Methods：An Analysis for Statisticians and Interdisciplinary Researchers

作者：Thomas Leonard 出版社：Cambridge Univ Pr 出版日：1999/09/16 裝訂：精裝

This book describes the Bayesian approach to statistics at a level suitable for final year undergraduate and Masters students. It is unusual in presenting Bayesian statistics with a practical flavor and an emphasis on mainstream statistics, showing how to infer scientific, medical, and social conclusions from numerical data. The authors draw on many years of experience with practical and research programs and describe many statistical methods, not readily available elsewhere. A first chapter on Fisherian methods, together with a strong overall emphasis on likelihood, makes the text suitable for mainstream statistics courses whose instructors wish to follow mixed or comparative philosophies. The other chapters contain important sections relating to many areas of statistics such as the linear model, categorical data analysis, time series and forecasting, mixture models, survival analysis, Bayesian smoothing, and non-linear random effects models. The text includes a large number of practi

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Likelihood Methods in Survival Analysis：With R Examples

Likelihood Methods in Statistics

Bayesian Likelihood Methods in Ecology And Biology

Likelihood Methods in Sample Surveys

Econometric Applications of Maximum Likelihood Methods

Likelihood Methods in Biology and Ecology: A Modern Approach to Statistics

Likelihood, Bayesian, and McMc Methods in Quantitative Genetics

Numerical Methods of Statistics

Empirical Likelihood and Quantile Methods for Time Series ― Efficiency, Robustness, Optimality, and Prediction

Core Statistics

Core Statistics

Maximum Likelihood Estimation And Inference - With Examples In R, Sas, And Admb

Applied Asymptotics：Case Studies in Small-Sample Statistics

Introduction to Probability and Statistics from a Bayesian Viewpoint, Part 2 ─ Inference

Introduction to Probability and Statistics from a Bayesian Viewpoint, Part 1 ─ Probability

Empirical Processes in M-Estimation

Analysis of Survival Data With Dependent Censoring ― Copula-based Approaches

Bayesian Methods：An Analysis for Statisticians and Interdisciplinary Researchers

Statistical Inference for Spatial Processes

Bayesian Methods：An Analysis for Statisticians and Interdisciplinary Researchers