Least squares moment identification of binary regression mixture models

Benjamin Auder (), Elisabeth Gassiat () and Mor Absa Loum ()
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Benjamin Auder: Université Paris-Saclay
Elisabeth Gassiat: Université Paris-Saclay
Mor Absa Loum: Université Paris-Saclay

Metrika: International Journal for Theoretical and Applied Statistics, 2021, vol. 84, issue 4, No 6, 593 pages

Abstract: Abstract We consider finite mixtures of generalized linear models with binary output. We prove that cross moments (between the output and the regression variables) up to order three are sufficient to identify all parameters of the model. We propose a least-squares estimation method based on those moments and we prove the consistency and the Gaussian asymptotic behavior of the estimator. We provide simulation results and comparisons with likelihood methods. Numerical experiments were conducted using the R-package morpheus that we developed for our least-squares moment method and with the R-package flexmix for likelihood methods. We then give some possible extensions to finite mixtures of regressions with binary output including both continuous and categorical covariates, and possibly longitudinal data.

Keywords: Generalized linear model; Mixture model; Moment method; Spectral method; Binary regression (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s00184-020-00787-x

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