Fundamentals of Nonparametric Bayesian Inference

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Author: Subhashis Ghosal,Aad van der Vaart

Publisher: Cambridge University Press

ISBN: 0521878268

Category: Business & Economics

Page: 670

View: 7211

Bayesian nonparametrics comes of age with this landmark text synthesizing theory, methodology and computation.

Predictive Statistics

Analysis and Inference beyond Models

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Author: Bertrand S. Clarke,Jennifer L. Clarke

Publisher: Cambridge University Press

ISBN: 110863303X

Category: Mathematics

Page: N.A

View: 459

All scientific disciplines prize predictive success. Conventional statistical analyses, however, treat prediction as secondary, instead focusing on modeling and hence estimation, testing, and detailed physical interpretation, tackling these tasks before the predictive adequacy of a model is established. This book outlines a fully predictive approach to statistical problems based on studying predictors; the approach does not require predictors correspond to a model although this important special case is included in the general approach. Throughout, the point is to examine predictive performance before considering conventional inference. These ideas are traced through five traditional subfields of statistics, helping readers to refocus and adopt a directly predictive outlook. The book also considers prediction via contemporary 'black box' techniques and emerging data types and methodologies where conventional modeling is so difficult that good prediction is the main criterion available for evaluating the performance of a statistical method. Well-documented open-source R code in a Github repository allows readers to replicate examples and apply techniques to other investigations.

Essentials of Statistical Inference

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Author: G. A. Young,R. L. Smith,R. L. (University of North Carolina Smith, Chapel Hill)

Publisher: Cambridge University Press

ISBN: 9780521839716

Category: Mathematics

Page: 225

View: 8490

Concise account of main approaches; first textbook to synthesize modern computation with basic theory.

Asymptotic Statistics

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Author: A. W. van der Vaart

Publisher: Cambridge University Press

ISBN: 9780521784504

Category: Mathematics

Page: 443

View: 4773

A mathematically rigorous, practical introduction presenting standard topics plus research.

Computational Bayesian Statistics

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Author: M. Antónia Amaral Turkman,Carlos Daniel Paulino,Peter Müller

Publisher: Cambridge University Press

ISBN: 1108481035

Category: Business & Economics

Page: 275

View: 2857

This integrated introduction to fundamentals, computation, and software is your key to understanding and using advanced Bayesian methods.

Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators

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Author: Tailen Hsing,Randall Eubank

Publisher: John Wiley & Sons

ISBN: 0470016914

Category: Mathematics

Page: 480

View: 2881

Functional data is data in the form of curves that is becoming a popular method for interpreting scientific data. Statistical Analysis of Functional Data provides an authoritative account of function data analysis covering its foundations, theory, methodology, and practical implementation. It also contains examples taken from a wide range of disciplines, including finance, medicine, and psychology. The book includes a supporting Web site hosting the real data sets analyzed in the book and related software. Statistical researchers or practitioners analyzing functional data will find this book useful.

Causal Inference for Statistics, Social, and Biomedical Sciences

An Introduction

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Author: Guido W. Imbens,Donald B. Rubin

Publisher: Cambridge University Press

ISBN: 1316094391

Category: Mathematics

Page: N.A

View: 7975

Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher.