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Comparative performance of tests of normality in detecting mixtures of parallel regression lines
Journal article   Peer reviewed

Comparative performance of tests of normality in detecting mixtures of parallel regression lines

Lalit K Aggarwal and Steve M Bajgier
Communications in statistics. Theory and methods, v 16(9), pp 2541-2563
01 Jan 1987
DOI: https://doi.org/10.1080/03610928708829522
Featured in Collection :   UN Sustainable Development Goals @ Drexel

Abstract

goodness-of-fit heterogeneity of intercept kurtosis mixed normal distributions
This paper addresses the problem of detecting a mixture of parallel regression lines when information about group member¬ship of individual cases is not given. The problem is approached as a missing variable problem, with the missing variables being the dummy variables that code for groups. If a mixture of par¬allel regression lines with normally distributed error terms is present, a simple regression model without dummy variables will produce residuals that follow approximately a mixed normal dis¬tribution. In a simulation studyr several goodness-of-fit tests of normality were used to test the residuals obtained from mis-specified models that excluded dummy variables, Factors varied in the simulation included the number and the separation of the parallel lines and the sample size, The goodness-of-fit test based on the sample kurtosis (82) was overall most powerful in detecting mixtures of parallel regression lines, Applications are discussed.

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Web of Science research areas
Statistics & Probability
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