A First Course in Statistical Inference, Gillard Jonathan
Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman Название: The Elements of Statistical Learning ISBN: 0387848576 ISBN-13(EAN): 9780387848570 Издательство: Springer Рейтинг: Цена: 69870.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.
Автор: Hald Anders Название: A History of Parametric Statistical Inference from Bernoulli to Fisher, 1713-1935 ISBN: 0387464085 ISBN-13(EAN): 9780387464084 Издательство: Springer Рейтинг: Цена: 111790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This is a history of parametric statistical inference, written by one of the most important historians of statistics of the 20th century, Anders Hald. This book can be viewed as a follow-up to his two most recent books, although this current text is much more streamlined and contains new analysis of many ideas and developments. And unlike his other books, which were encyclopedic by nature, this book can be used for a course on the topic, the only prerequisites being a basic course in probability and statistics.The book is divided into five main sections:* Binomial statistical inference;* Statistical inference by inverse probability;* The central limit theorem and linear minimum variance estimation by Laplace and Gauss;* Error theory, skew distributions, correlation, sampling distributions;* The Fisherian Revolution, 1912-1935.Throughout each of the chapters, the author provides lively biographical sketches of many of the main characters, including Laplace, Gauss, Edgeworth, Fisher, and Karl Pearson. He also examines the roles played by DeMoivre, James Bernoulli, and Lagrange, and he provides an accessible exposition of the work of R.A. Fisher.This book will be of interest to statisticians, mathematicians, undergraduate and graduate students, and historians of science.
Автор: Moulin Pierre Название: Statistical Inference for Engineers and Data Scientists ISBN: 1107185920 ISBN-13(EAN): 9781107185920 Издательство: Cambridge Academ Рейтинг: Цена: 67590.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: An up-to-date and mathematically accessible introduction to the tools needed to address modern inference problems in engineering and data science. Richly illustrated with examples and exercises connecting the theory with practice, it is the `go to` guide for students studying the topic, and an excellent reference for researchers and practitioners.
Автор: Peter D. Hoff Название: A First Course in Bayesian Statistical Methods ISBN: 0387922997 ISBN-13(EAN): 9780387922997 Издательство: Springer Рейтинг: Цена: 60550.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: A self-contained introduction to probability, exchangeability and Bayes` rule provides a theoretical understanding of the applied material. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.
Название: Probability Theory and Statistical Inference ISBN: 0521424089 ISBN-13(EAN): 9780521424080 Издательство: Cambridge Academ Рейтинг: Цена: 48570.00 T Наличие на складе: Поставка под заказ. Описание: This major new textbook from a distinguished econometrician is intended for students taking introductory courses in probability theory and statistical inference. No prior knowledge other than a basic familiarity with descriptive statistics is assumed. The primary objective of this book is to establish the framework for the empirical modelling of observational (non-experimental) data. This framework known as 'Probabilistic Reduction' is formulated with a view to accommodating the peculiarities of observational (as opposed to experimental) data in a unifying and logically coherent way. Probability Theory and Statistical Inference differs from traditional textbooks in so far as it emphasizes concepts, ideas, notions and procedures which are appropriate for modelling observational data. Aimed at students at second-year undergraduate level and above studying econometrics and economics, this textbook will also be useful for students in other disciplines which make extensive use of observational data, including finance, biology, sociology and psychology and climatology.
Автор: Bing Li; G. Jogesh Babu Название: A Graduate Course on Statistical Inference ISBN: 1493997599 ISBN-13(EAN): 9781493997596 Издательство: Springer Рейтинг: Цена: 130430.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This textbook offers an accessible and comprehensive overview of statistical estimation and inference that reflects current trends in statistical research. It draws from three main themes throughout: the finite-sample theory, the asymptotic theory, and Bayesian statistics. The authors have included a chapter on estimating equations as a means to unify a range of useful methodologies, including generalized linear models, generalized estimation equations, quasi-likelihood estimation, and conditional inference. They also utilize a standardized set of assumptions and tools throughout, imposing regular conditions and resulting in a more coherent and cohesive volume. Written for the graduate-level audience, this text can be used in a one-semester or two-semester course.
A hands-on approach to statistical inference that addresses the latest developments in this ever-growing field
This clear and accessible book for beginning graduate students offers a practical and detailed approach to the field of statistical inference, providing complete derivations of results, discussions, and MATLAB programs for computation. It emphasizes details of the relevance of the material, intuition, and discussions with a view towards very modern statistical inference. In addition to classic subjects associated with mathematical statistics, topics include an intuitive presentation of the (single and double) bootstrap for confidence interval calculations, shrinkage estimation, tail (maximal moment) estimation, and a variety of methods of point estimation besides maximum likelihood, including use of characteristic functions, and indirect inference. Practical examples of all methods are given. Estimation issues associated with the discrete mixtures of normal distribution, and their solutions, are developed in detail. Much emphasis throughout is on non-Gaussian distributions, including details on working with the stable Paretian distribution and fast calculation of the noncentral Student's t. An entire chapter is dedicated to optimization, including development of Hessian-based methods, as well as heuristic/genetic algorithms that do not require continuity, with MATLAB codes provided.
The book includes both theory and nontechnical discussions, along with a substantial reference to the literature, with an emphasis on alternative, more modern approaches. The recent literature on the misuse of hypothesis testing and p-values for model selection is discussed, and emphasis is given to alternative model selection methods, though hypothesis testing of distributional assumptions is covered in detail, notably for the normal distribution.
Presented in three parts--Essential Concepts in Statistics; Further Fundamental Concepts in Statistics; and Additional Topics--Fundamental Statistical Inference: A Computational Approach offers comprehensive chapters on: Introducing Point and Interval Estimation; Goodness of Fit and Hypothesis Testing; Likelihood; Numerical Optimization; Methods of Point Estimation; Q-Q Plots and Distribution Testing; Unbiased Point Estimation and Bias Reduction; Analytic Interval Estimation; Inference in a Heavy-Tailed Context; The Method of Indirect Inference; and, as an appendix, A Review of Fundamental Concepts in Probability Theory, the latter to keep the book self-contained, and giving material on some advanced subjects such as saddlepoint approximations, expected shortfall in finance, calculation with the stable Paretian distribution, and convergence theorems and proofs.
Автор: Juditsky Anatoli, Nemirovski Arkadi Название: Statistical Inference Via Convex Optimization ISBN: 0691197296 ISBN-13(EAN): 9780691197296 Издательство: Wiley Рейтинг: Цена: 92930.00 T Наличие на складе: Поставка под заказ. Описание:
This authoritative book draws on the latest research to explore the interplay of high-dimensional statistics with optimization. Through an accessible analysis of fundamental problems of hypothesis testing and signal recovery, Anatoli Juditsky and Arkadi Nemirovski show how convex optimization theory can be used to devise and analyze near-optimal statistical inferences.
Statistical Inference via Convex Optimization is an essential resource for optimization specialists who are new to statistics and its applications, and for data scientists who want to improve their optimization methods. Juditsky and Nemirovski provide the first systematic treatment of the statistical techniques that have arisen from advances in the theory of optimization. They focus on four well-known statistical problems--sparse recovery, hypothesis testing, and recovery from indirect observations of both signals and functions of signals--demonstrating how they can be solved more efficiently as convex optimization problems. The emphasis throughout is on achieving the best possible statistical performance. The construction of inference routines and the quantification of their statistical performance are given by efficient computation rather than by analytical derivation typical of more conventional statistical approaches. In addition to being computation-friendly, the methods described in this book enable practitioners to handle numerous situations too difficult for closed analytical form analysis, such as composite hypothesis testing and signal recovery in inverse problems.
Statistical Inference via Convex Optimization features exercises with solutions along with extensive appendixes, making it ideal for use as a graduate text.
Автор: Little Название: Statistical Analysis with Missing Data, Third Edit ion ISBN: 0470526793 ISBN-13(EAN): 9780470526798 Издательство: Wiley Рейтинг: Цена: 84430.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Reflecting new application topics, Statistical Analysis with Missing Data offers a thoroughly up-to-date, reorganized survey of current methodology for handling missing data problems. The third edition reviews historical approaches to the subject and describe rigorous yet simple methods for multivariate analysis with missing values.
Автор: Hahs-Vaughn Debbie L Название: Statistical Concepts - A First Course ISBN: 0367203995 ISBN-13(EAN): 9780367203993 Издательство: Taylor&Francis Рейтинг: Цена: 59190.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Statistical Concepts: A First Course presents the introductory chapters from An Introduction to Statistical Concepts, 4th Edition, designed for first and lower level statistics courses.
Автор: Hahs-Vaughn, Debbie L. , Lomax, Richard G. Название: Statistical Concepts - A First Course ISBN: 0367203960 ISBN-13(EAN): 9780367203962 Издательство: Taylor&Francis Рейтинг: Цена: 148010.00 T Наличие на складе: Невозможна поставка. Описание: Statistical Concepts: A First Course presents the introductory chapters from An Introduction to Statistical Concepts, 4th Edition, designed for first and lower level statistics courses.
Автор: W. John Braun , Duncan J. Murdoch Название: A First Course in Statistical Programming with R ISBN: 1108995144 ISBN-13(EAN): 9781108995146 Издательство: Cambridge Academ Рейтинг: Цена: 40120.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book is for students who are learning computing to use in data science and scientific applications. It takes the reader from basic principles of computing to numerical methods, using the R programming language. Worked examples, hundreds of exercises, and downloadable code, datasets, and solutions make a complete package.
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