Estimating Multinomial Logit Model with Multicollinear Data

Ida Camminatie, Antonio Lucadamo

Type: Article

Publication Date: 2010-03-15

Citations: 21

DOI: https://doi.org/10.3923/ajms.2010.93.101

Abstract

The multinomial model is used to study the dependence relationship between a categorical response variable with more than two categories and a set of explicative variables.In presence of multicollinearity, the estimation of the multinomial model parameters becomes inaccurate.To solve this problem we develop an extension of Principal Component Logistic Regression (PCLR), model proposed by Aguilera et al. (2006).Finally a case study illustrates the advantages of the method.

Locations

Asian Journal of Mathematics & Statistics - View
CiteSeer X (The Pennsylvania State University) - View - PDF

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Works Cited by This (8)

Action	Title	Year	Authors
+	A continuum of principal component generalized linear regressions	1992	Brian D. Marx
+	PLS generalised linear regression	2004	Philippe Bastien Vincenzo Esposito Vinzi Michel Tenenhaus
+ PDF Chat	Using principal components for estimating logistic regression with high-dimensional multicollinear data	2005	Ana M. Aguilera Manuel Escabias Mariano J. Valderrama
+	LOGISTIC RIDGE REGRESSION FOR CLINICAL DATA ANALYSIS (A CASE STUDY)	2006	Emese Vágó Sándor Kemény
+	Partial Least Squares	2005	Herman Wold
+	Modern Regression Methods	1998	Brian S. Yandell Thomas P. Ryan
+	A COMPARISON OF GOODNESS-OF-FIT TESTS FOR THE LOGISTIC REGRESSION MODEL	1997	D Hosmer Trina Hosmer Saskia le Cessie Stanley Lemeshow
+	Ridge Regression: Biased Estimation for Nonorthogonal Problems	1970	Arthur E. Hoerl Robert W. Kennard