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＜電子ブック＞
A SAS/IML Companion for Linear Models / by Jamis J. Perrett
(Statistics and Computing. ISSN:21971706)

版	1st ed. 2010.
出版者	(New York, NY : Springer New York : Imprint: Springer)
出版年	2010
本文言語	英語
大きさ	XIV, 228 p : online resource
著者標目	*Perrett, Jamis J author SpringerLink (Online service)
件　名	LCSH:Probabilities LCSH:Mathematics -- Data processing  全ての件名で検索 LCSH:Computer software LCSH:Statistics  FREE:Probability Theory FREE:Computational Mathematics and Numerical Analysis FREE:Mathematical Software FREE:Statistical Theory and Methods
一般注記	SAS/IML: A Brief Introduction -- IML Language Structure -- IML Programming Features -- Matrix Manipulations in SAS/IML -- Mathematical and Statistical Basics -- Linear Algebra -- The Multivariate Normal Distribution -- The General Linear Model -- Linear Mixed Models -- Statistical Computation Methods Linear models courses are often presented as either theoretical or applied. Consequently, students may find themselves either proving theorems or using high-level procedures like PROC GLM to analyze data. There exists a gap between the derivation of formulas and analyses that hide these formulas behind attractive user interfaces. This book bridges that gap, demonstrating theory put into practice. Concepts presented in a theoretical linear models course are often trivialized in applied linear models courses by the facility of high-level SAS procedures like PROC MIXED and PROC REG that require the user to provide a few options and statements and in return produce vast amounts of output. This book uses PROC IML to show how analytic linear models formulas can be typed directly into PROC IML, as they were presented in the linear models course, and solved using data. This helps students see the link between theory and application. This also assists researchers in developing new methodologies in the area of linear models. The book contains complete examples of SAS code for many of the computations relevant to a linear models course. However, the SAS code in these examples automates the analytic formulas. The code for high-level procedures like PROC MIXED is also included for side-by-side comparison. The book computes basic descriptive statistics, matrix algebra, matrix decomposition, likelihood maximization, non-linear optimization, etc. in a format conducive to a linear models or a special topics course. Also included in the book is an example of a basic analysis of a linear mixed model using restricted maximum likelihood estimation (REML). The example demonstrates tests for fixed effects, estimates of linear functions, and contrasts. The example starts by showing the steps for analyzing the data using PROC IML and then provides the analysis using PROC MIXED. This allows students to follow the process that lead to the output HTTP:URL=https://doi.org/10.1007/978-1-4419-5557-9

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