Selection of variables in regression models based on inflated distributions

K. Aruna Rao, K. Sumathi

Research output: Contribution to journalArticle

Abstract

Regression models based on zero inflated distributions are often used in exploratory data analysis having excess zeroes. The difficulty faced by many researchers is with regard to the selection of covariates to be included in the model. Following the idea of focused information criterion, observed focused information criterion is proposed for model selection. The motivation for this has its roots in the concept of observed Fisher information. Using this criterion, a forward selection procedure is proposed for selection of variables in regression models based on inflated distributions. The procedure is illustrated using a dataset on decayed missing filled teeth (DMFT) index using the modified observed focused information criterion.

Original languageEnglish
Pages (from-to)381-390
Number of pages10
JournalPakistan Journal of Statistics and Operation Research
Volume7
Issue number2 SPECIAL ISSUE
Publication statusPublished - 01-10-2011

Fingerprint

Observed Information
Selection of Variables
Information Criterion
Regression Model
Model-based
Zero Distribution
Exploratory Data Analysis
Fisher Information
Selection Procedures
Model Selection
Excess
Covariates
Roots
Zero
Regression model
Information criterion
Model

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modelling and Simulation
  • Statistics, Probability and Uncertainty
  • Management Science and Operations Research

Cite this

Aruna Rao, K. ; Sumathi, K. / Selection of variables in regression models based on inflated distributions. In: Pakistan Journal of Statistics and Operation Research. 2011 ; Vol. 7, No. 2 SPECIAL ISSUE. pp. 381-390.
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Selection of variables in regression models based on inflated distributions. / Aruna Rao, K.; Sumathi, K.

In: Pakistan Journal of Statistics and Operation Research, Vol. 7, No. 2 SPECIAL ISSUE, 01.10.2011, p. 381-390.

Research output: Contribution to journalArticle

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