Curve Fitting and Modeling with Splines Using Statistical Variable Selection Techniques
Author:
Publisher:
Published: 1982
Total Pages: 86
ISBN-13:
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Scientific and Technical Aerospace Reports
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Publisher:
Published: 1989
Total Pages: 988
ISBN-13:
DOWNLOAD EBOOKNonparametric Regression and Spline Smoothing
Author: Randall L. Eubank
Publisher: CRC Press
Published: 1999-02-09
Total Pages: 359
ISBN-13: 1482273144
DOWNLOAD EBOOKProvides 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 co
Building Regression Models with SAS
Author: Robert N. Rodriguez
Publisher: SAS Institute
Published: 2023-04-18
Total Pages: 464
ISBN-13: 1951684001
DOWNLOAD EBOOKAdvance 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.
Spline Regression Models
Author: Lawrence C. Marsh
Publisher: SAGE
Published: 2001-09-14
Total Pages: 86
ISBN-13: 9780761924203
DOWNLOAD EBOOKSpline Regression Models shows how to use dummy variables to formulate and estimate spline regression models both in situations where the number and location of the spline knots are known in advance, and where estimation is required.
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 EBOOKBei 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.
Handbook of Quantile Regression
Author: Roger Koenker
Publisher: CRC Press
Published: 2017-10-12
Total Pages: 463
ISBN-13: 1498725295
DOWNLOAD EBOOKQuantile regression constitutes an ensemble of statistical techniques intended to estimate and draw inferences about conditional quantile functions. Median regression, as introduced in the 18th century by Boscovich and Laplace, is a special case. In contrast to conventional mean regression that minimizes sums of squared residuals, median regression minimizes sums of absolute residuals; quantile regression simply replaces symmetric absolute loss by asymmetric linear loss. Since its introduction in the 1970's by Koenker and Bassett, quantile regression has been gradually extended to a wide variety of data analytic settings including time series, survival analysis, and longitudinal data. By focusing attention on local slices of the conditional distribution of response variables it is capable of providing a more complete, more nuanced view of heterogeneous covariate effects. Applications of quantile regression can now be found throughout the sciences, including astrophysics, chemistry, ecology, economics, finance, genomics, medicine, and meteorology. Software for quantile regression is now widely available in all the major statistical computing environments. The objective of this volume is to provide a comprehensive review of recent developments of quantile regression methodology illustrating its applicability in a wide range of scientific settings. The intended audience of the volume is researchers and graduate students across a diverse set of disciplines.
Regression for Categorical Data
Author: Gerhard Tutz
Publisher: Cambridge University Press
Published: 2011-11-21
Total Pages: 573
ISBN-13: 1139499580
DOWNLOAD EBOOKThis 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.
Generalized Additive Models
Author: T.J. Hastie
Publisher: Routledge
Published: 2017-10-19
Total Pages: 356
ISBN-13: 1351445960
DOWNLOAD EBOOKThis 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.
