The information matrix test for Gaussian mixtures

Dante Amengual (), Gabriele Fiorentini and Enrique Sentana
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Dante Amengual: CEMFI, Centro de Estudios Monetarios y Financieros, https://www.cemfi.es/

Working Papers from CEMFI

Abstract: In incomplete data models the EM principle implies the moments the Information Matrix test assesses are the expectation given the observations of the moments it would assess were the underlying components observed. This principle also leads to interpretable expressions for their asymptotic covariance matrix adjusted for sampling variability in the parameter estimators under correct specification. Monte Carlo simulations for finite Gaussian mixtures indicate that the parametric bootstrap provides reliable finite sample sizes and good power against various misspecification alternatives. We confirm that 3-component Gaussian mixtures accurately describe cross-sectional distributions of per capita income in the 1960-2000 Penn World Tables.

Keywords: Expectation-Maximisation principle; incomplete data; Hessian matrix; outer product of the score. (search for similar items in EconPapers)
JEL-codes: C46 C52 O47 (search for similar items in EconPapers)
Date: 2024-02
New Economics Papers: this item is included in nep-ecm
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