Nonparametric Regression and Spline Smoothing, Second Edition
Nonparametric Regression and Spline Smoothing, Second Edition

Author: Randall L. Eubank

Publisher: CRC Press

Published: 1999-02-09

Total Pages: 368

ISBN-13: 9780824793371

DOWNLOAD EBOOK

Provides a unified account of the most popular approaches to nonparametric regression smoothing. This edition contains discussions of boundary corrections for trigonometric series estimators; detailed asymptotics for polynomial regression; testing goodness-of-fit; estimation in partially linear models; practical aspects, problems and methods for confidence intervals and bands; local polynomial regression; and form and asymptotic properties of linear smoothing splines.

Building Regression Models with SAS
Building Regression Models with SAS

Author: Robert N. Rodriguez

Publisher: SAS Institute

Published: 2023-04-18

Total Pages: 464

ISBN-13: 1951684001

DOWNLOAD EBOOK

Advance your skills in building predictive models with SAS! Building Regression Models with SAS: A Guide for Data Scientists teaches data scientists, statisticians, and other analysts who use SAS to train regression models for prediction with large, complex data. Each chapter focuses on a particular model and includes a high-level overview, followed by basic concepts, essential syntax, and examples using new procedures in both SAS/STAT and SAS Viya. By emphasizing introductory examples and interpretation of output, this book provides readers with a clear understanding of how to build the following types of models: general linear models quantile regression models logistic regression models generalized linear models generalized additive models proportional hazards regression models tree models models based on multivariate adaptive regression splines Building Regression Models with SAS is an essential guide to learning about a variety of models that provide interpretability as well as predictive performance.

Generalized Additive Models
Generalized Additive Models

Author: T.J. Hastie

Publisher: Routledge

Published: 2017-10-19

Total Pages: 356

ISBN-13: 1351445960

DOWNLOAD EBOOK

This book describes an array of power tools for data analysis that are based on nonparametric regression and smoothing techniques. These methods relax the linear assumption of many standard models and allow analysts to uncover structure in the data that might otherwise have been missed. While McCullagh and Nelder's Generalized Linear Models shows how to extend the usual linear methodology to cover analysis of a range of data types, Generalized Additive Models enhances this methodology even further by incorporating the flexibility of nonparametric regression. Clear prose, exercises in each chapter, and case studies enhance this popular text.

Modeling Time-Varying Unconditional Variance by Means of a Free-Knot Spline-GARCH Model
Modeling Time-Varying Unconditional Variance by Means of a Free-Knot Spline-GARCH Model

Author: Oliver Old

Publisher: Springer Nature

Published: 2022-07-27

Total Pages: 260

ISBN-13: 3658386185

DOWNLOAD EBOOK

The book addresses the problem of a time-varying unconditional variance of return processes utilizing a spline function. The knots of the spline functions are estimated as free parameters within a joined estimation process together with the parameters of the mean, the conditional variance and the spline function. With the help of this method, the knots are placed in regions where the unconditional variance is not smooth. The results are tested within an extensive simulation study and an empirical study employing the S&P500 index.

Regression for Categorical Data
Regression for Categorical Data

Author: Gerhard Tutz

Publisher: Cambridge University Press

Published: 2011-11-21

Total Pages: 573

ISBN-13: 1139499580

DOWNLOAD EBOOK

This book introduces basic and advanced concepts of categorical regression with a focus on the structuring constituents of regression, including regularization techniques to structure predictors. In addition to standard methods such as the logit and probit model and extensions to multivariate settings, the author presents more recent developments in flexible and high-dimensional regression, which allow weakening of assumptions on the structuring of the predictor and yield fits that are closer to the data. A generalized linear model is used as a unifying framework whenever possible in particular parametric models that are treated within this framework. Many topics not normally included in books on categorical data analysis are treated here, such as nonparametric regression; selection of predictors by regularized estimation procedures; ternative models like the hurdle model and zero-inflated regression models for count data; and non-standard tree-based ensemble methods. The book is accompanied by an R package that contains data sets and code for all the examples.

Bayesian Methods for Nonlinear Classification and Regression
Bayesian Methods for Nonlinear Classification and Regression

Author: David G. T. Denison

Publisher: John Wiley & Sons

Published: 2002-05-06

Total Pages: 302

ISBN-13: 9780471490364

DOWNLOAD EBOOK

Bei der Regressionsanalyse von Datenmaterial erhält man leider selten lineare oder andere einfache Zusammenhänge (parametrische Modelle). Dieses Buch hilft Ihnen, auch komplexere, nichtparametrische Modelle zu verstehen und zu beherrschen. Stärken und Schwächen jedes einzelnen Modells werden durch die Anwendung auf Standarddatensätze demonstriert. Verbreitete nichtparametrische Modelle werden mit Hilfe von Bayes-Verfahren in einen kohärenten wahrscheinlichkeitstheoretischen Zusammenhang gebracht.