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A Primer on Linear Models

A Primer on Linear Models

A Primer on Linear Models presents a unified thorough and rigorous development of the theory behind the statistical methodology of regression and analysis of variance (ANOVA). It seamlessly incorporates these concepts using non-full-rank design matrices and emphasizes the exact finite sample theory supporting common statistical methods. With coverage steadily progressing in complexity the text first provides examples of the general linear model including multiple regression models one-way ANOVA mixed-effects models and time series models. It then introduces the basic algebra and geometry of the linear least squares problem before delving into estimability and the Gauss Markov model. After presenting the statistical tools of hypothesis tests and confidence intervals the author analyzes mixed models such as two-way mixed ANOVA and the multivariate linear model. The appendices review linear algebra fundamentals and results as well as Lagrange multipliers. This book enables complete comprehension of the material by taking a general unifying approach to the theory fundamentals and exact results of linear models.

GBP 175.00
1

The Theory of Statistical Implicative Analysis Or the Implausibility of Falsehood ... When the Exception Confirms the Rule

The Theory of Statistical Implicative Analysis Or the Implausibility of Falsehood ... When the Exception Confirms the Rule

This book summarizes the methods and concepts of Statistical Implicative Analysis (SIA) created by Régis Gras in the 1980s to study in a new way the behavioural responses of French pupils to mathematics tests. Using a multidimensional non-symmetrical data analysis method SIA crosses a set of subjects or objects with a set of variables. It effectively complements traditional correlational and psychometric methods. SIA through its various extensions is today presented as a broad Artificial Intelligence method aimed at extracting trends and possible causalities in the form of rules from a set of variables. It is based on the unlikeliness of the existence of these relationships i. e. on the relative weakness of their counter-examples compared to what chance alone would produce. It establishes a dual topological relationship between the set of subjects and the set of variables. Many applications of this approach driving forces or crucibles for the development of SIA have concerned and still concern various fields such as didactics evaluation and assessment psychology sociology medicine biology economics art history and others. Key Features: Presents the foundations and representations of SIA Provides extensions of variable sets and subjects Includes a bonus exercise | The Theory of Statistical Implicative Analysis Or the Implausibility of Falsehood . When the Exception Confirms the Rule

GBP 120.00
1

Survival Analysis

Survival Analysis

Survival analysis generally deals with analysis of data arising from clinical trials. Censoring truncation and missing data create analytical challenges and the statistical methods and inference require novel and different approaches for analysis. Statistical properties essentially asymptotic ones of the estimators and tests are aptly handled in the counting process framework which is drawn from the larger arm of stochastic calculus. With explosion of data generation during the past two decades survival data has also enlarged assuming a gigantic size. Most statistical methods developed before the millennium were based on a linear approach even in the face of complex nature of survival data. Nonparametric nonlinear methods are best envisaged in the Machine Learning school. This book attempts to cover all these aspects in a concise way. Survival Analysis offers an integrated blend of statistical methods and machine learning useful in analysis of survival data. The purpose of the offering is to give an exposure to the machine learning trends for lifetime data analysis. Features: Classical survival analysis techniques for estimating statistical functional and hypotheses testing Regression methods covering the popular Cox relative risk regression model Aalen’s additive hazards model etc. Information criteria to facilitate model selection including Akaike Bayes and Focused Penalized methods Survival trees and ensemble techniques of bagging boosting and random survival forests A brief exposure of neural networks for survival data R program illustration throughout the book

GBP 99.99
1

A Course in Categorical Data Analysis

A Course in Categorical Data Analysis

Categorical data-comprising counts of individuals objects or entities in different categories-emerge frequently from many areas of study including medicine sociology geology and education. They provide important statistical information that can lead to real-life conclusions and the discovery of fresh knowledge. Therefore the ability to manipulate understand and interpret categorical data becomes of interest-if not essential-to professionals and students in a broad range of disciplines. Although t-tests linear regression and analysis of variance are useful valid methods for analysis of measurement data categorical data requires a different methodology and techniques typically not encountered in introductory statistics courses. Developed from long experience in teaching categorical analysis to a multidisciplinary mix of undergraduate and graduate students A Course in Categorical Data Analysis presents the easiest most straightforward ways of extracting real-life conclusions from contingency tables. The author uses a Fisherian approach to categorical data analysis and incorporates numerous examples and real data sets. Although he offers S-PLUS routines through the Internet readers do not need full knowledge of a statistical software package. In this unique text the author chooses methods and an approach that nurtures intuitive thinking. He trains his readers to focus not on finding a model that fits the data but on using different models that may lead to meaningful conclusions. The book offers some simple innovative techniques not highighted in other texts that help make the book accessible to a broad interdisciplinary audience. A Course in Categorical Data Analysis enables readers to quickly use its offering of tools for drawing scientific medical or real-life conclusions from categorical data sets.

GBP 170.00
1

Nonparametric Statistical Inference

Nonparametric Statistical Inference

Praise for previous editions: … a classic with a long history. – Statistical Papers The fact that the first edition of this book was published in 1971 … [is] testimony to the book’s success over a long period. – ISI Short Book Reviews … one of the best books available for a theory course on nonparametric statistics. … very well written and organized … recommended for teachers and graduate students. – Biometrics … There is no competitor for this book and its comprehensive development and application of nonparametric methods. Users of one of the earlier editions should certainly consider upgrading to this new edition. – Technometrics … Useful to students and research workers … a good textbook for a beginning graduate-level course in nonparametric statistics. – Journal of the American Statistical Association Since its first publication in 1971 Nonparametric Statistical Inference has been widely regarded as the source for learning about nonparametrics. The Sixth Edition carries on this tradition and incorporates computer solutions based on R. Features Covers the most commonly used nonparametric procedures States the assumptions develops the theory behind the procedures and illustrates the techniques using realistic examples from the social behavioral and life sciences Presents tests of hypotheses confidence-interval estimation sample size determination power and comparisons of competing procedures Includes an Appendix of user-friendly tables needed for solutions to all data-oriented examples Gives examples of computer applications based on R MINITAB STATXACT and SAS Lists over 100 new references Nonparametric Statistical Inference Sixth Edition has been thoroughly revised and rewritten to make it more readable and reader-friendly. All of the R solutions are new and make this book much more useful for applications in modern times. It has been updated throughout and contains 100 new citations including some of the most recent to make it more current and useful for researchers.

GBP 99.99
1

Probability and Statistics for Computer Scientists

Probability and Statistics for Computer Scientists

Praise for the Second Edition: The author has done his homework on the statistical tools needed for the particular challenges computer scientists encounter. [He] has taken great care to select examples that are interesting and practical for computer scientists. . The content is illustrated with numerous figures and concludes with appendices and an index. The book is erudite and … could work well as a required text for an advanced undergraduate or graduate course. Computing Reviews Probability and Statistics for Computer Scientists Third Edition helps students understand fundamental concepts of Probability and Statistics general methods of stochastic modeling simulation queuing and statistical data analysis; make optimal decisions under uncertainty; model and evaluate computer systems; and prepare for advanced probability-based courses. Written in a lively style with simple language and now including R as well as MATLAB this classroom-tested book can be used for one- or two-semester courses. Features: Axiomatic introduction of probability Expanded coverage of statistical inference and data analysis including estimation and testing Bayesian approach multivariate regression chi-square tests for independence and goodness of fit nonparametric statistics and bootstrap Numerous motivating examples and exercises including computer projects Fully annotated R codes in parallel to MATLAB Applications in computer science software engineering telecommunications and related areas In-Depth yet Accessible Treatment of Computer Science-Related TopicsStarting with the fundamentals of probability the text takes students through topics heavily featured in modern computer science computer engineering software engineering and associated fields such as computer simulations Monte Carlo methods stochastic processes Markov chains queuing theory statistical inference and regression. It also meets the requirements of the Accreditation Board for Engineering and Technology (ABET). About the Author Michael Baron is David Carroll Professor of Mathematics and Statistics at American University in Washington D. C. He conducts research in sequential analysis and optimal stopping change-point detection Bayesian inference and applications of statistics in epidemiology clinical trials semiconductor manufacturing and other fields. M. Baron is a Fellow of the American Statistical Association and a recipient of the Abraham Wald Prize for the best paper in Sequential Analysis and the Regents Outstanding Teaching Award. M. Baron holds a Ph. D. in statistics from the University of Maryland. In his turn he supervised twelve doctoral students mostly employed on academic and research positions.

GBP 99.99
1