A variable selection technique in discriminant analysis with application in marketing data

A. K. Gupta; T. P. Logan; Jie Chen

doi:10.1080/00949659908548525

A variable selection technique in discriminant analysis with application in marketing data

A. K. Gupta, T. P. Logan, Jie Chen

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

In this paper, we have developed a likelihood ratio factorization technique for variable selection for multiple observations model. The asymptotic distribution of the selection criterion is also given. This technique has been applied to a marketing data set for illustration of the technique developed.

Original language	English (US)
Pages (from-to)	187-199
Number of pages	13
Journal	Journal of Statistical Computation and Simulation
Volume	63
Issue number	2
DOIs	https://doi.org/10.1080/00949659908548525
State	Published - Jan 1 1999
Externally published	Yes

Keywords

Hierarchical model
Likelihood ratio factorization technique
Multiple observations
Multivariate normal distribution

ASJC Scopus subject areas

Statistics and Probability
Modeling and Simulation
Statistics, Probability and Uncertainty
Applied Mathematics

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title = "A variable selection technique in discriminant analysis with application in marketing data",

abstract = "In this paper, we have developed a likelihood ratio factorization technique for variable selection for multiple observations model. The asymptotic distribution of the selection criterion is also given. This technique has been applied to a marketing data set for illustration of the technique developed.",

keywords = "Hierarchical model, Likelihood ratio factorization technique, Multiple observations, Multivariate normal distribution",

author = "Gupta, {A. K.} and Logan, {T. P.} and Jie Chen",

year = "1999",

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AU - Logan, T. P.

AU - Chen, Jie

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KW - Likelihood ratio factorization technique

KW - Multiple observations

KW - Multivariate normal distribution

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A variable selection technique in discriminant analysis with application in marketing data

Abstract

Keywords

ASJC Scopus subject areas

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