Linear Statistical Inference, T. Calinski; W. Klonecki
Автор: Malley Название: Statistical Learning for Biomedical Data ISBN: 0521699096 ISBN-13(EAN): 9780521699099 Издательство: Cambridge Academ Рейтинг: Цена: 6494.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Biomedical researchers need machine learning techniques to make predictions such as survival/death or response to treatment when data sets are large and complex. This highly motivating introduction to these machines explains underlying principles in nontechnical language, using many examples and figures, and connects these new methods to familiar techniques.
Автор: Cheng Russell C H Название: Non-Standard Parametric Statistical Inference ISBN: 0198505043 ISBN-13(EAN): 9780198505044 Издательство: Oxford Academ Рейтинг: Цена: 19404.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This research monograph gives a unified view of non-standard estimation problems. It provides an overall mathematical framework, but also draws together and studies in detail a large number of practical problems, previously only treated separately, offering solution methods and numerical procedures for each.
Описание: Uses the method of maximum likelihood to a large extent to ensure reasonable, and in some cases optimal procedures. This work treats the basic and important topics in multivariate statistics.
Автор: Bradley Efron and Trevor Hastie Название: Computer Age Statistical Inference ISBN: 1107149894 ISBN-13(EAN): 9781107149892 Издательство: Cambridge Academ Рейтинг: Цена: 9029.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.
Finance and insurance companies are facing a wide range of parametric statistical problems. Statistical experiments generated by a sample of independent and identically distributed random variables are frequent and well understood, especially those consisting of probability measures of an exponential type. However, the aforementioned applications also offer non-classical experiments implying observation samples of independent but not identically distributed random variables or even dependent random variables.
Three examples of such experiments are treated in this book. First, the Generalized Linear Models are studied. They extend the standard regression model to non-Gaussian distributions. Statistical experiments with Markov chains are considered next. Finally, various statistical experiments generated by fractional Gaussian noise are also described.
In this book, asymptotic properties of several sequences of estimators are detailed. The notion of asymptotical efficiency is discussed for the different statistical experiments considered in order to give the proper sense of estimation risk. Eighty examples and computations with R software are given throughout the text.
Examines a range of statistical inference methods in the context of finance and insurance applications
Presents the LAN (local asymptotic normality) property of likelihoods
Combines the proofs of LAN property for different statistical experiments that appears in financial and insurance mathematics
Provides the proper description of such statistical experiments and invites readers to seek optimal estimators (performed in R) for such statistical experiments
Автор: Myers, Jerome L Название: Research design and statistical analysis ISBN: 0805864318 ISBN-13(EAN): 9780805864311 Издательство: Taylor&Francis Рейтинг: Цена: 22202.00 р. Наличие на складе: Поставка под заказ.
Описание: This interdisciplinary group of scholars-anthropologists, archaeologists, architects, educators, lawyers, heritage administrators, policy analysts, and consultants-make the first attempt to define and assess heritage values on a local, national and global level. Chapters range from the theoretical to policy frameworks to case studies of heritage practice, written by scholars from eight countries.
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.
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