A comparison of the maximum likelihood and discriminant function estimators of the coefficients of the logistic regression model for mixed continuous and discrete variables
作者:
Trina Hosmer,
David Hosmer,
Lloyd Fisher,
期刊:
Communications in Statistics - Simulation and Computation
(Taylor Available online 1983)
卷期:
Volume 12,
issue 1
页码: 23-43
ISSN:0361-0918
年代: 1983
DOI:10.1080/03610918308812298
出版商: Marcel Dekker, Inc.
关键词: bias;estimation;log linear models
数据来源: Taylor
摘要:
A model for mixed continuous and discrete variables suggested by Chang and Afifi (1974) and Krzanowski (1975) is used to explore the bias in the discriminant function (DF) approach to estimation of the coefficients in the multiple1ogistic regression model. When the data come from this mixed variable model the DF estimator of the coefficients of the continuous variables are asymptotically unbiased. The DF estimator of the intercept and coefficients for the discrete variables may be severely biased. The magnitude of the bias is shown to depend in a systematic way on the true value of the coefficients and the underlying probabilities of the out-come of discrete variables. The implications for analysis are discussed.
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