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April 2020 Consistent maximum likelihood estimation using subsets with applications to multivariate mixed models
Karl Oskar Ekvall, Galin L. Jones
Ann. Statist. 48(2): 932-952 (April 2020). DOI: 10.1214/19-AOS1830

Abstract

We present new results for consistency of maximum likelihood estimators with a focus on multivariate mixed models. Our theory builds on the idea of using subsets of the full data to establish consistency of estimators based on the full data. It requires neither that the data consist of independent observations, nor that the observations can be modeled as a stationary stochastic process. Compared to existing asymptotic theory using the idea of subsets, we substantially weaken the assumptions, bringing them closer to what suffices in classical settings. We apply our theory in two multivariate mixed models for which it was unknown whether maximum likelihood estimators are consistent. The models we consider have nonstochastic predictors and multivariate responses which are possibly mixed-type (some discrete and some continuous).

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Karl Oskar Ekvall. Galin L. Jones. "Consistent maximum likelihood estimation using subsets with applications to multivariate mixed models." Ann. Statist. 48 (2) 932 - 952, April 2020. https://doi.org/10.1214/19-AOS1830

Information

Received: 1 October 2018; Revised: 1 February 2019; Published: April 2020
First available in Project Euclid: 26 May 2020

zbMATH: 07241575
MathSciNet: MR4102682
Digital Object Identifier: 10.1214/19-AOS1830

Subjects:
Primary: 62F12
Secondary: 62J12

Rights: Copyright © 2020 Institute of Mathematical Statistics

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Vol.48 • No. 2 • April 2020
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