Maximum Likelihood Estimation and Uniform Inference with Sporadic Identication Failure

Supplemental Appendix to MAXIMUM LIKELIHOOD ESTIMATION AND UNIFORM INFERENCE WITH SPORADIC IDENTIFICATION FAILURE By

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...

Inference on Pr(X > Y ) Based on Record Values From the Power Hazard Rate Distribution

In this article, we consider the problem of estimating the stress-strength reliability $Pr (X > Y)$ based on upper record values when $X$ and $Y$ are two independent but not identically distributed random variables from the power hazard rate distribution with common scale parameter $k$. When the parameter $k$ is known, the maximum likelihood estimator (MLE), the approximate Bayes estimator and ...

Inference for the Type-II Generalized Logistic Distribution with Progressive Hybrid Censoring

This article presents the analysis of the Type-II hybrid progressively censored data when the lifetime distributions of the items follow Type-II generalized logistic distribution. Maximum likelihood estimators (MLEs) are investigated for estimating the location and scale parameters. It is observed that the MLEs can not be obtained in explicit forms. We provide the approximate maximum likelihood...

MAXIMUM LIKELIHOOD ESTIMATION AND UNIFORM INFERENCE WITH SPORADIC IDENTIFICATION FAILURE By

This paper analyzes the properties of a class of estimators, tests, and confidence sets (CSs) when the parameters are not identified in parts of the parameter space. Specifically, we consider estimator criterion functions that are sample averages and are smooth functions of a parameter θ . This includes log likelihood, quasi-log likelihood, and least squares criterion functions. We determine th...