Logistic ridge regression for clinical data analysis (a case study)

E. Vágó, S. Kemény

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

This paper focuses on regression with binomial response data. In these cases logit regression is the most used model. An example is a retrospective biomedical problem, where multicollinearity occurs, thus the variances of the estimated parameters are large. In this paper we propose to apply the ridge method to the maximum likelihood estimation of the logit model parameters. The efficiency of the proposed technique was investigated using a biomedical data set. A random sampling technique was used to study the effect of sample size on the ML and the logistic ML estimation.

Original languageEnglish
Pages (from-to)171-179
Number of pages9
JournalApplied Ecology and Environmental Research
Volume4
Issue number2
Publication statusPublished - 2006

Fingerprint

logistics
data analysis
case studies
logit analysis
methodology
sampling
parameter
sampling technique
method
effect

Keywords

  • Bootstrap
  • Logit
  • Multicollinearity
  • Restless legs

ASJC Scopus subject areas

  • Ecology
  • Environmental Science(all)

Cite this

Logistic ridge regression for clinical data analysis (a case study). / Vágó, E.; Kemény, S.

In: Applied Ecology and Environmental Research, Vol. 4, No. 2, 2006, p. 171-179.

Research output: Contribution to journalArticle

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