Robust regression analysis for non-normal situations under symmetric distributions arising in medical research

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2 Citations (Scopus)

Abstract

In medical research, while carrying out regression analysis, it is usually assumed that the independent (covariates) and dependent (response) variables follow a multivariate normal distribution. In some situations, the covariates may not have normal distribution and instead may have some symmetric distribution. In such a situation, the estimation of the regression parameters using Tiku's Modified Maximum Likelihood (MML) method may be more appropriate. The method of estimating the parameters is discussed and the applications of the method are illustrated using real sets of data from the field of public health.

Original languageEnglish
Pages (from-to)446-462
Number of pages17
JournalJournal of Modern Applied Statistical Methods
Volume13
Issue number1
Publication statusPublished - 2014

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Robust Regression
Symmetric Distributions
Regression Analysis
Covariates
Modified Maximum Likelihood
Multivariate Normal Distribution
Maximum Likelihood Method
Public Health
Gaussian distribution
Regression
Dependent
Regression analysis
Robust regression
Medical research

Keywords

  • Delta method
  • Maximum likelihood
  • Modified maximum likelihood
  • Order statistics
  • Student's tdistribution

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Statistics and Probability

Cite this

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