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. Discusses both theoretical statistics and the practical applications of the theoretical developments. Includes a large numer of exercises covering both theory and applications. This book introduces statistical inference in spatial statistics and its applications. This book will be welcomed by both the student and practising statistician wishing to study at a fairly elementary level, the basic conceptual and interpretative distinctions between the different approaches, how they interrelate, what ... This volume is a compressed survey containing recent results on statistics of stochastic processes and on identification with incomplete observations. This approach distinguishes it from many other texts using statistical decision theory as their underlying philosophy. This volume covers concepts from probability theory, backed by numerous problems with selected answers. New to the Second Edition New material on empirical Bayes and penalized likelihoods and their impact on regression models Expanded material on hypothesis testing, method of moments, bias correction, and hierarchical models More examples and ... This user-friendly introduction to the mathematics of probability and statistics (for readers with a background in calculus) uses numerous applications--drawn from biology, education, economics, engineering, environmental studies, exercise ... This book covers modern statistical inference based on likelihood with applications in medicine, epidemiology and biology. These chapters also deal with the principal components, factor models, canonical correlations, and time series. This book will prove useful to statisticians, mathematicians, and advance mathematics students. 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 ... In this book the author presents with elegance and precision some of the basic mathematical theory required for statistical inference at a level which will make it readable by most students of statistics. Beginning with an introduction to the basic ideas and techniques in probability theory and progressing to more rigorous topics, Probability and Statistical Inference studies the Helmert transformation for normal distributions and the ... Take an exhilarating journey through the modern revolution in statistics with two of the ringleaders. This book is written to be user-friendly for students and practitioners who are not experts in statistics, but who want to gain a solid understanding of basic statistical inference. This book showcases Ian Hacking's early ideas on the central issues surrounding statistical reasoning. In this groundbreaking book Zoltán Dienes introduces students to key issues in the philosophy of science and statistics that have a direct and vital bearing on the practice of research in psychology. A comprehensive, balanced account of the theory of statistical inference, its main ideas and controversies. The second half of the book addresses statistical inference, beginning with a discussion on point estimation and followed by coverage of consistency and confidence intervals. Introduction to statistical inference; Specification of a statistical problem; Classifications of statistical problems; Some criteria for choosing a procedure; Linear unbiased estimation; Sufficiency; Point estimation; Hypothesis testing; ... This third edition expands the discussion of many of the techniques presented, and includes additional examples as well as exercise sets at the end of each chapter. This book offers a brief course in statistical inference that requires only a basic familiarity with probability and matrix and linear algebra. This book builds theoretical statistics from the first principles of probability theory. Statistical inference is the foundation on which much of statistical practice is built. This book covers the topic at a level suitable for students and professionals who need to understand these foundations. Designed for students with a background in calculus, this book continues to reinforce basic mathematical concepts with numerous real-world examples and applications to illustrate the relevance of key concepts. Likelihood methods; Two - parameter likelihoods; Checking the model; Tests of significance; Intervals from significance tests; Inferences for normal distribution parameters; Fitting a straight line; Topics in statistical inference. This Third Edition: Introduces an all-new chapter on Bayesian statistics and offers thorough explanations of advanced statistics and probability topics Includes 650 problems and over 400 examples - an excellent resource for the mathematical ... The book is suitable for students and researchers in statistics, computer science, data mining and machine learning. This book covers a much wider range of topics than a typical introductory text on mathematical statistics. Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. 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. The purpose of this book is to present up-to-date theory and techniques of statistical inference in a logically integrated and practical form. 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. These are here brought together for the first time as the central themes of a book on statistical inference, appropriate as an advanced undergraduate or graduate text in mathematical statistics. This excellent text emphasizes the inferential and decision-making aspects of statistics. Everyone knows it is easy to lie with statistics. Throughout the book, expert David Aronson provides you with comprehensive coverage of this new methodology, which is specifically designed for evaluating the performance of rules/signals that are discovered by data mining. Discusses probability theory and to many methods used in problems of statistical inference. The Third Edition features material on descriptive statistics. Found insideFrom the reviews: The purpose of the book under review is to give a survey of methods for the Bayesian or likelihood-based analysis of data. Found insidePresented 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 ... Found insideUnlock today's statistical controversies and irreproducible results by viewing statistics as probing and controlling errors. The book also serves as a valuable reference for researchers and practitioners who would like to develop further insights into essential statistical tools. Features: ● Assumes minimal prerequisites, notably, no prior calculus nor coding experience ● Motivates theory using real-world data, including all domestic flights leaving New York City in 2013, the Gapminder project, and the data ... This work is devoted to several problems of parametric (mainly) and nonparametric estimation through the observation of Poisson processes defined on general spaces. This review article covered developments in the field from 1966 through 1976. A few minor typographical errors in the original edition have been corrected in this new edition. Intended as a text for the postgraduate students of statistics, this well-written book gives a complete coverage of Estimation theory and Hypothesis testing, in an easy-to-understand style. This authoritative book draws on the latest research to explore the interplay of high-dimensional statistics with optimization. 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