Nonparametric regression on hidden phi-mixing variables: identifiability and consistency of a pseudo-likelihood based estimation procedureReport as inadecuate




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1 MODAL-X - Modélisation aléatoire de Paris X 2 Département de Mathématiques-Université de Paris XI

Abstract : This paper outlines a new nonparametric estimation procedure for unobserved phi-mixing processes. It is assumed that the only information on the stationary hidden states Xk is given by the process Yk, where Yk is a noisy observation of fXk. The paper introduces a maximum pseudo-likelihood procedure to estimate the function f and the distribution of the hidden states using blocks of observations of length b. The identifiability of the model is studied in the particular cases b=1 and b=2. The consistency of the estimators of f and of the distribution of the hidden states as the number of observations grows to infinity is established.

Keywords : Hidden variables Nonparametric Estimation Identifiability Maximum Likelihood





Author: Thierry Dumont - Sylvain Le Corff -

Source: https://hal.archives-ouvertes.fr/



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