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Handbook of Regression Methods concisely covers numerous traditional, contemporary, and nonstandard regression methods. The handbook;
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Handbook and reference guide for students and practitioners of statistical regression-based analyses in R Handbook of Regression;
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Covering both general and advanced aspects of multivariate methods, this handbook focuses on regression analysis of cross-sectional and;
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A Comprehensive Account for Data Analysts of the Methods and Applications of Regression Analysis. Written by two established experts in the;
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and with a discussion of the directions of current research and perspectives. It covers the broad area of PLS methods, from regression to;
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Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques;
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Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques;
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> Specific topics covered include minimax probability machine regression, stochastic finite element method, relevance vector machine, logistic;
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Simultaneous confidence bands enable more intuitive and detailed inference of regression analysis than the standard inferential methods;
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Simultaneous confidence bands enable more intuitive and detailed inference of regression analysis than the standard inferential methods;
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Over the years, I have had the opportunity to teach several regression courses, and I cannot think of a better undergraduate text than this;
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displaying and describing distributions, and covers a range of working methods including probability and sampling, simple linear regression;
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Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research;
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including distributions, estimation, testing of hypothesis, correlation and regression in a very simple and understandable language. An attempt;
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sections on the nearest neighbor classifier, support vector machines, model checking and regularization, partial least squares regression, and;
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regression, and multivariate adaptive regression splines. Web ResourceThe authors include algorithmic descriptions of the procedures as well;
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, Multivariate Nonparametric Regression and Visualization provides readers with the core statistical concepts to obtain complete and accurate predictions;
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Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis;
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Flexible Bayesian Regression Modeling is a step-by-step guide to the Bayesian revolution in regression modeling, for use in advanced;
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The first book to discuss robust aspects of nonlinear regression-with applications using R software Robust Nonlinear Regression: with;
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simple regression and analysis of variance methods adequate. The contribution of this handbook is to assemble a state-of-the-art view of this;
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simple regression and analysis of variance methods adequate. The contribution of this handbook is to assemble a state-of-the-art view of this;
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as the Gaussian Mixture Model (GMM), Dirichlet processes Mixture Model (DMM), Poisson Mixture Regression Model (PMRM), Hierarchical Gamma;
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This set contains ISBN 978-0-470-08186-0 Modern Regression Methods, 2nd Edition and the Solutions Manual 978-0-470-09606-2.;
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Focusing on the methods that are commonly used by social scientists, this text introduces the regression methods for analysing spatial data.;
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Praise for the Fourth Edition: This book is ...an excellent source of examples for regression analysis. It has been and still is readily;
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While there have been a large number of estimation methods proposed and developed for linear regression, none has proved good for all;
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