Modern Regression Methods

Authors:
Thomas P. Ryan
Affiliations:
-
Venue:
Modern Regression Methods
Year:
2008

Citing 0
Cited 11

Minimum Message Length Criterion for Second-Order Polynomial Model Disovery

PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Logistic regression using covariates obtained by product-unit neural network models

Pattern Recognition
Algorithm for cardinality-constrained quadratic optimization

Computational Optimization and Applications
Discovering regression data quality through clustering methods

Proceedings of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008
Modelling and Prediction of Electronically Controlled Automotive Engine Power and Torque Using Support Vector Machines

Proceedings of the 2005 conference on Advances in Logic Based Intelligent Systems: Selected Papers of LAPTEC 2005
The performance of robust weighted least squares in the presence of outliers and heteroscedastic errors

WSEAS Transactions on Mathematics
Using principal components for estimating logistic regression with high-dimensional multicollinear data

Computational Statistics & Data Analysis
Prediction of automotive engine power and torque using least squares support vector machines and Bayesian inference

Engineering Applications of Artificial Intelligence
Logistic regression as a computational tool for dealing with intransitivity

EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
Face identification using linear regression

ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Improved efficient, nearly orthogonal, nearly balanced mixed designs

Proceedings of the Winter Simulation Conference

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Abstract

This new edition of Regression Methods has been updated and enhanced to include all-new information on the latest advances and research in the evolving field of regression analysis. It provides a unique treatment of fundamental regression methods, such as diagnostics, transformations, robust regression, and ridge regression. Unifying key concepts and procedures, this new edition emphasizes applications to provide a more hands-on and comprehensive understanding of regression diagnostics.