عنوان

Advanced linear modeling :

(Number (ISBN

3030291642

(Number (ISBN

9783030291648

Erroneous ISBN

9783030291631

Title Proper

Advanced linear modeling :

General Material Designation

[Book]

Other Title Information

statistical learning and dependent data /

First Statement of Responsibility

Ronald Christensen.

Edition Statement

3rd ed.

Place of Publication, Distribution, etc.

Cham :

Name of Publisher, Distributor, etc.

Springer,

Date of Publication, Distribution, etc.

2019.

Specific Material Designation and Extent of Item

1 online resource (618 pages)

Series Title

Springer Texts in Statistics Ser.

Text of Note

5.8.1 Wald's Test

Text of Note

Intro -- Prefaces -- Contents -- 1 Nonparametric Regression -- 1.1 Basic Notation -- 1.2 Linear Approximations -- 1.3 Simple Nonparametric Regression -- 1.4 Estimation -- 1.4.1 Polynomials -- 1.4.2 Cosines -- 1.4.3 Haar Wavelets -- 1.4.4 Cubic Splines -- 1.4.5 Orthonormal Series Estimation -- 1.5 Variable Selection -- 1.6 Heteroscedastic Simple Nonparametric Regression -- 1.7 Approximating-Functions with Small Support -- 1.7.1 Polynomial Splines -- 1.7.1.1 B-Splines -- 1.7.1.2 Equivalence of Spline Methods* -- 1.7.2 Fitting Local Functions -- 1.7.3 Local Regression

Text of Note

2.3 Lasso Regression -- 2.4 Bayesian Connections -- 2.5 Another Approach -- 2.5.1 Geometry -- 2.5.1.1 More Lasso Geometry -- 2.5.2 Equivalence of Approaches -- 2.6 Two Other Penalty Functions -- References -- 3 Reproducing Kernel Hilbert Spaces -- 3.1 Introduction -- 3.1.1 Interpolating Splines -- 3.2 Banach and Hilbert Spaces -- 3.2.1 Banach Spaces -- 3.2.2 Hilbert Spaces -- 3.3 Reproducing Kernel Hilbert Spaces -- 3.3.1 The Projection Principle for an RKHS -- 3.4 Two Approaches -- 3.4.1 Testing Lack of Fit -- 3.5 Penalized Regression with RKHSs -- 3.5.1 Ridge and Lasso Regression

Text of Note

4.4 Linear Covariance Structures -- 4.5 MINQUE -- 4.5.1 Deriving the MINQUE Equations -- 4.6 MIVQUE -- 4.7 The Effect of Estimated Covariances -- 4.7.1 Mathematical Results* -- References -- 5 Mixed Models and Variance Components -- 5.1 Mixed Models -- 5.2 Mixed Model Equations -- 5.3 Equivalence of Random Effects and Ridge Regression -- 5.4 Partitioning and Linear Covariance Structures -- 5.5 Variance Component Models -- 5.5.1 Variance Component Estimation -- 5.6 A Longitudinal Model -- 5.7 Henderson's Method 3 -- 5.7.1 Additional Estimates -- 5.8 Exact F Tests for Variance Components

0

8

8

Text of Note

Now in its third edition, this companion volume to Ronald Christensen's Plane Answers to Complex Questions uses three fundamental concepts from standard linear model theory--best linear prediction, projections, and Mahalanobis distance-- to extend standard linear modeling into the realms of Statistical Learning and Dependent Data. This new edition features a wealth of new and revised content. In Statistical Learning it delves into nonparametric regression, penalized estimation (regularization), reproducing kernel Hilbert spaces, the kernel trick, and support vector machines. For Dependent Data it uses linear model theory to examine general linear models, linear mixed models, time series, spatial data, (generalized) multivariate linear models, discrimination, and dimension reduction. While numerous references to Plane Answers are made throughout the volume, Advanced Linear Modeling can be used on its own given a solid background in linear models. Accompanying R code for the analyses is available online.--

Source for Acquisition/Subscription Address

Springer Nature

Stock Number

com.springer.onix.9783030291648

Title

Advanced Linear Modeling : Statistical Learning and Dependent Data.

International Standard Book Number

9783030291631

Linear models (Statistics)

Linear models (Statistics)

MAT029000

PBT

PBT

PBWL

Number

Edition

Class number

Christensen, Ronald,1951-

Date of Transaction

20200823094002.0

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y