A lack-of-fit test in Tobit errors-in-variables regression models

A Lack-of-Fit Test in Tobit Errors-in-Variables Regression Models

The problem of fitting a parametric model in Tobit errors-in-variables regression models is discussed in this paper. The proposed test is based on the supremum of the Khamaladze type transformation of a certain partial sum process of calibrated residuals. This framework covers the usual error-free Tobit model as a special case. The asymptotic null distribution of this transformed process is sho...

Empirical L2-distance lack-of-fit tests for Tobit regression models

Errors-in-variables beta regression models

Beta regression models provide an adequate approach for modeling continuous outcomes limited to the interval (0, 1). This paper deals with an extension of beta regression models that allow for explanatory variables to be measured with error. The structural approach, in which the covariates measured with error are assumed to be random variables, is employed. Three estimation methods are presente...

A goodness-of-fit test of logistic regression models for case-control data with measurement errors

We study the problem of goodness-of-fit tests for logistic regression models for case-control data when some covariates are measured with errors. We first study the applicability of traditional test methods for this problem by simply ignoring measurement errors and show that in some scenarios they are still effective despite the inconsistency of the parameter estimators. We then develop a test ...

Instrumental Variables Regression with Measurement Errors and Multicollinearity in Instruments

In this paper we obtain a consistent estimator when there exist some measurement errors and multicollinearity in the instrumental variables in a two stage least square estimation of parameters. We investigate the asymptotic distribution of the proposed estimator and discuss its properties using some theoretical proofs and a simulation study. A real numerical application is also provided for mor...