Regression Modeling: Methods, Theory, and Computation with SAS

ISBN-13: 9780367385675 / Angielski / Miękka / 2019 / 830 str.

Michael Panik

Opis

Requiring only basic knowledge of statistics and calculus, this comprehensive, accessible text provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs. He explores the well-known methods of OLS and maximum likelihood regression before developing many alternative regression techniques, such as nonparametric, logistic, Bayesian, robust, fuzzy, random coefficients, spatial, polynomial, ridge, semiparametric, and more. The book also covers nonlinear and time series modeling.

Spis treści

Preface. Review of Fundamentals of Statistics. Bivariate Linear Regression and Correlation. Misspecified Disturbance Terms. Nonparametric Regression. Logistic Regression. Bayesian Regression. Robust Regression. Fuzzy Regression. Random Coefficients Regression. L₁ and q-Quantile Regression. Regression in a Spatial Domain. Multiple Regression. Normal Correlation Models. Ridge Regression. Indicator Variables. Polynomial Model Estimation. Semiparametric Regression. Nonlinear Regression. Issues in Time Series Modeling and Estimation. Appendix. References. Index.

O autorze

Panik, Michael