عنوان

Semiparametric regression with R /

1493988530

9781493988532

1493988514

9781493988518

Semiparametric regression with R /

[Book]

Jaroslaw Harezlak, David Ruppert, Matt P. Wand.

New York, NY :

Springer,

2018.

1 online resource (xi, 331 pages) :

illustrations (some color)

Use R!,

2197-5736

Intro; Preface; Contents; 1 Introduction; 1.1 Semiparametric Regression; 1.2 The R Language; 1.3 Some Examples; 1.3.1 Warsaw Apartments; 1.3.2 Boston Mortgage Applications; 1.3.3 Indiana Adolescent Growth Data; 1.3.4 Sydney Real Estate Data; 1.3.5 Michigan Panel Study of Income Dynamics Data; 1.3.6 All of the Datasets Used in This Book; 1.4 Aim of This Book; 2 Penalized Splines; 2.1 Introduction; 2.2 Penalized Spline Basics; 2.3 Choosing the Smoothing Parameter; 2.4 Choosing the Basis Size; 2.5 Checking the Residuals; 2.6 Effective Degrees of Freedom; 2.7 Mixed Model-Based Penalized Splines

2.8 Variability Bands2.9 Hypothesis Testing; 2.10 Bayesian Penalized Splines; 2.10.1 Multiple Chains Extension; 2.11 Choosing Between Different Penalized Spline Approaches; 2.12 Penalized Splines with Factor Effects; 2.12.1 A Simple Semiparametric Additive Model; 2.12.2 A Simple Semiparametric Interaction Model; 2.12.3 A Simple Factor-by-Curve Model; 2.13 Further Reading; 2.14 Exercises; 3 Generalized Additive Models; 3.1 Introduction; 3.2 Generalized Linear Models; 3.2.1 Example: Mortgage Applications in Boston; 3.2.2 Example: Physician Offices Visits; 3.3 Generalized Additive Models

3.3.1 Example: Test Scores of Children in California School Districts3.3.2 Example: Physician Office Visits; 3.3.3 Example: Mortgage Applications in Boston; 3.4 Model Selection; 3.4.1 Stepwise Model Selection; 3.4.2 Penalty-Based Model Selection; 3.4.2.1 Example: Tests Scores of California School Children; 3.5 Extension to Vector Responses; 3.6 Extension to Factor-by-Curve Interactions; 3.6.1 Example: Mortgage Applications in Boston; 3.7 Further Reading; 3.8 Exercises; 4 Semiparametric Regression Analysis of Grouped Data; 4.1 Introduction; 4.2 Additive Mixed Models; 4.2.1 Bayesian Approach

4.2.2 Serial Correlation Extension4.3 Models with Group-Specific Curves; 4.4 Marginal Models; 4.4.1 Marginal Nonparametric Regression; 4.4.1.1 Comparison with Random Intercept Model; 4.4.1.2 Approaches to Marginal Nonparametric Regression; 4.4.2 Additive Model Extension; 4.4.3 Incorporation of Interactions; 4.5 Extension to Non-Gaussian Response Variables; 4.5.1 Penalized Quasi-Likelihood Analysis; 4.5.2 Markov Chain Monte Carlo Analysis; 4.6 Further Readings; 4.7 Exercises; 5 Bivariate Function Extensions; 5.1 Introduction; 5.2 Bivariate Nonparametric Regression

5.2.1 Example: Ozone Levels in Midwest USA5.3 Geoadditive Models; 5.3.1 Example: House Prices in Sydney, Australia; 5.4 Varying-Coefficient Models; 5.4.1 Example: Daily Stock Returns; 5.5 Additional Semiparametric Regression Models; 5.6 Covariance Function Estimation; 5.6.1 Example: Gasoline Near-Infrared Spectra; 5.7 Estimating a Covariance Function with Sparse Data; 5.7.1 Example: Spinal Bone Mineral Density Data; 5.8 The Sandwich Smoother; 5.8.1 Example: Brain Imaging; 5.9 Further Reading; 5.10 Exercises; 6 Selection of Additional Topics; 6.1 Introduction

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This easy-to-follow applied book expands upon the authors' prior work on semiparametric regression to include the use of R software. In 2003, authors Ruppert and Wand co-wrote Semiparametric Regression with R.J. Carroll, which introduced the techniques and benefits of semiparametric regression in a concise and user-friendly fashion. Fifteen years later, semiparametric regression is applied widely, powerful new methodology is continually being developed, and advances in the R computing environment make it easier than ever before to carry out analyses. Semiparametric Regression with R introduces the basic concepts of semiparametric regression with a focus on applications and R software. This volume features case studies from environmental, economic, financial, and other fields. The examples and corresponding code can be used or adapted to apply semiparametric regression to a wide range of problems. It contains more than fifty exercises, and the accompanying HRW package contains all datasets and scripts used in the book, as well as some useful R functions. This book is suitable as a textbook for advanced undergraduates and graduate students, as well as a guide for statistically-oriented practitioners, and could be used in conjunction with Semiparametric Regression. Readers are assumed to have a basic knowledge of R and some exposure to linear models. For the underpinning principles, calculus-based probability, statistics, and linear algebra are desirable.--

Springer Nature

com.springer.onix.9781493988532

Semiparametric regression with R.

9781493988518

R (Computer program language)

Regression analysis.

R (Computer program language)

Regression analysis.

MAT029000

PBT

PBT

Harezlak, Jaroslaw

Ruppert, David,1948-

Wand, M. P., (Matt P.)

20200823034849.0

pn

[Book]

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