Theory of Statistical Inference (Chapman & Hall/CRC Texts in Statistical Science) - Image 1

£44.99

Chapman and Hall/CRC Theory of Statistical Inference (Chapman & Hall/CRC Texts in Statistical Science)

Name: Theory of Statistical Inference (Chapman & Hall/CRC Texts in Statistical Science)
Brand: Chapman and Hall/CRC
Price: 44.99 GBP
Availability: InStock

Probability & Statistics

This is the most expensive it has ever been. Walk away.

£45 today · previous high £45 · all-time low £40

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 489 days • 489 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 489 days • 6 price levels

Days at Price

Current Price

Price Analysis

Most common price: £41 (239 days, 48.9%)

Price range: £40 - £45

Price levels: 6 different prices over 489 days

Description

Theory of Statistical Inference is designed as a reference on statistical inference for researchers and students at the graduate or advanced undergraduate level. It presents a unified treatment of the foundational ideas of modern statistical inference, and would be suitable for a core course in a graduate program in statistics or biostatistics. The emphasis is on the application of mathematical theory to the problem of inference, leading to an optimization theory allowing the choice of those statistical methods yielding the most efficient use of data. The book shows how a small number of key concepts, such as sufficiency, invariance, stochastic ordering, decision theory and vector space algebra play a recurring and unifying role. The volume can be divided into four sections. Part I provides a review of the required distribution theory. Part II introduces the problem of statistical inference. This includes the definitions of the exponential family, invariant and Bayesian models. Basic concepts of estimation, confidence intervals and hypothesis testing are introduced here. Part III constitutes the core of the volume, presenting a formal theory of statistical inference. Beginning with decision theory, this section then covers uniformly minimum variance unbiased (UMVU) estimation, minimum risk equivariant (MRE) estimation and the Neyman-Pearson test. Finally, Part IV introduces large sample theory. This section begins with stochastic limit theorems, the δ-method, the Bahadur representation theorem for sample quantiles, large sample U-estimation, the Cramér-Rao lower bound and asymptotic efficiency. A separate chapter is then devoted to estimating equation methods. The volume ends with a detailed development of large sample hypothesis testing, based on the likelihood ratio test (LRT), Rao score test and the Wald test. Features This volume includes treatment of linear and nonlinear regression models, ANOVA models, generalized linear models (GLM) and generalized estimating equations (GEE). An introduction to decision theory (including risk, admissibility, classification, Bayes and minimax decision rules) is presented. The importance of this sometimes overlooked topic to statistical methodology is emphasized. The volume emphasizes throughout the important role that can be played by group theory and invariance in statistical inference. Nonparametric (rank-based) methods are derived by the same principles used for parametric models and are therefore presented as solutions to well-defined mathematical problems, rather than as robust heuristic alternatives to parametric methods. Each chapter ends with a set of theoretical and applied exercises integrated with the main text. Problems involving R programming are included. Appendices summarize the necessary background in analysis, matrix algebra and group theory.

Product Specifications

Brand: Chapman and Hall/CRC
Format: paperback
ASIN: 0367502801
Category: Books > Subjects > Science, Nature & Maths > Mathematics > Probability & Statistics
Domain: Amazon UK
Release Date: 27 May 2024
Listed Since: 29 March 2024

Barcode

No barcode data available

Chapman and Hall/CRC Theory of Statistical Inference (Chapman & Hall/CRC Texts in Statistical Science)

This is the most expensive it has ever been. Walk away.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Theory of Statistical Inference (Chapman & Hall/CRC Texts in Statistical Science)

SOME RECENT ADVANCES IN MATHEMATICS AND STATISTICS - PROCEEDINGS OF STATISTICS 2011 CANADA/IMST 2011-FIM XX

Statistical Theory: A Concise Introduction (Chapman & Hall/CRC Texts in Statistical Science)

R Programming: Statistical Data Analysis in Research

Models for Probability and Statistical Inference: Theory and Applications (Wiley Series in Probability and Statistics)

Inferential Models: Reasoning with Uncertainty (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)

Foundations of Statistics for Data Scientists: With R and Python (Chapman & Hall/CRC Texts in Statistical Science)

Foundations and Applications of Statistics: An Introduction Using R (Pure and Applied Undergraduate Texts)

Linear Mixed Models: A Practical Guide Using Statistical Software

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse (Chapman & Hall/CRC The R Series)

Contributions on Theory of Mathematical Statistics

A First Course in Linear Model Theory (Chapman & Hall/CRC Texts in Statistical Science)

Analytical Methods in Statistics: AMISTAT, Liberec, Czech Republic, September 2019: 329 (Springer Proceedings in Mathematics & Statistics, 329)

Linear Models in Statistics

Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition (Chapman & Hall/CRC Texts in Statistical Science)

Seminal Ideas and Controversies in Statistics (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)

Seminal Ideas and Controversies in Statistics (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)

Richly Parameterized Linear Models: Additive, Time Series, and Spatial Models Using Random Effects (Chapman & Hall/CRC Texts in Statistical Science)

Utts & Heckard's Mind on Statistics

Deep-Learning-Assisted Statistical Methods with Examples in R (Chapman & Hall/CRC Data Science Series)

Introduction to Probabilistic and Statistical Methods with Examples in R: 176 (Intelligent Systems Reference Library, 176)

Understanding The New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis (Multivariate Applications Series)

Theory of Stochastic Objects: Probability, Stochastic Processes and Inference (Chapman & Hall/CRC Texts in Statistical Science)

Richly Parameterized Linear Models: Additive, Time Series, and Spatial Models Using Random Effects (Chapman & Hall/CRC Texts in Statistical Science)