Model robust inference with two-stage maximum likelihood estimation for copulas

Maximum Likelihood Estimation of Parameters in Generalized Functional Linear Model

Sometimes, in practice, data are a function of another variable, which is called functional data. If the scalar response variable is categorical or discrete, and the covariates are functional, then a generalized functional linear model is used to analyze this type of data. In this paper, a truncated generalized functional linear model is studied and a maximum likelihood approach is used to esti...

Local Maximum Likelihood Estimation and Inference

Robust maximum likelihood estimation in the linear model

This paper addresses the problem of maximum likelihood parameter estimation in linear models a!ected by Gaussian noise, whose mean and covariance matrix are uncertain. The proposed estimate maximizes a lower bound on the worst-case (with respect to the uncertainty) likelihood of the measured sample, and is computed solving a semide"nite optimization problem (SDP). The problem of linear robust e...

Maximum Likelihood Estimation and Uniform Inference with Sporadic Identication Failure

This paper analyzes the properties of a class of estimators, tests, and con…dence sets (CS’s) when the parameters are not identi…ed in parts of the parameter space. Speci…cally, we consider estimator criterion functions that are sample averages and are smooth functions of a parameter : This includes log likelihood, quasi-log likelihood, and least squares criterion functions. We determine the as...

Maximum likelihood estimation and uniform inference with sporadic identification failure

This paper analyzes the properties of a class of estimators, tests, and con…dence sets (CS’s) when the parameters are not identi…ed in parts of the parameter space. Speci…cally, we consider estimator criterion functions that are sample averages and are smooth functions of a parameter : This includes log likelihood, quasi-log likelihood, and least squares criterion functions. We determine the as...