Predictors with measurement error in mixtures of polynomial regressions

Xiaoqiong Fang (), Andy W. Chen () and Derek S. Young ()
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Xiaoqiong Fang: Corporate & Investment Bank, J.P. Morgan
Andy W. Chen: School of Business, Government, and Economics, Seattle Pacific University
Derek S. Young: University of Kentucky

Computational Statistics, 2023, vol. 38, issue 1, No 18, 373-401

Abstract: Abstract There has been a substantial body of research on mixtures-of-regressions models that has developed over the past 20 years. While much of the recent literature has focused on flexible mixtures-of-regressions models, there is still considerable utility for imposing structure on the mixture components through fully parametric models. One feature of the data that is scantly addressed in mixtures of regressions is the presence of measurement error in the predictors. The limited existing research on this topic concerns the case where classical measurement error is added to the classic mixtures-of-linear-regressions model. In this paper, we consider the setting of mixtures of polynomial regressions where the predictors are subject to classical measurement error. Moreover, each component is allowed to have a different degree for the polynomial structure. We utilize a generalized expectation-maximization algorithm for performing maximum likelihood estimation. For estimating standard errors, we extend a semiparametric bootstrap routine that has been employed for mixtures of linear regressions without measurement error in the predictors. Numeric work, for practical reasons identified, is limited to estimating two-component models. We consider a likelihood ratio test for determining if there is a higher-degree polynomial term in one of the components. Model selection criteria are also highlighted as a way for determining an appropriate model. A simulation study and an application to the classic nitric oxide emissions data are provided.

Keywords: Bootstrap; Finite mixture models; GEM algorithm; Model selection; Regression calibration; Surrogate data (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00180-022-01232-5

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