A nonparametric lack-of-fit test is proposed to check the adequacy of the presumed parametric form for the regression function in Tobit regression models by applying Zheng’s device with weighted residuals. It is shown that testing the null hypothesis for the standard Tobit regression models is equivalent to test a new null hypothesis of the classic regression models. An optimal weight function ...

Relatively recent research has illustrated the potential that tobit regression has in studying factors that affect vehicle accident rates (accidents per distance traveled) on specific roadway segments. Tobit regression has been used because accident rates on specific roadway segments are continuous data that are left-censored at zero (they are censored because accidents may not be observed on a...

Abstract We propose a variational inference-based framework for training Gaussian process regression model subject to censored observational data. Data censoring is typical problem encountered during the data gathering procedure and requires specialized techniques perform inference since resulting probabilistic models are typically analytically intractable. In this article we exploit sparse ind...

Studies investigating crash rates by roadway classification are few and far between and even more so if extended to focus on heavy vehicles. This study explores and compares two advanced econometric methods, random-parameter Tobit regression and latent class Tobit regression, to determine contributing factors for heavy vehicle crashes per million-vehicle-miles-traveled while accounting for the ...

This paper considers the censored regression model under the assumption that the regressors are integrated. We show that Maximum Likelihood estimation is superconsistent and asymptotically mixed normal, implying that standard inference techniques remain valid, and that in general least squares estimation based on the positive observations only is superconsistent, but not mixed normal. An except...

We consider the Bayes estimation of the Tobit censored regression model with normally distributed errors. A simple condition for the existence of posterior moments is provided. Suitable versions of Monte Carlo procedures based on symmetric multivariate-t distributions, and Laplacian approximations in a certain parametrization, are developed and illustrated. Ideas involving data augmentation and...

In this paper, we use a semi-parametric two-stage model to examine the effect of bankspecific, industry-specific and macroeconomic determinants of bank efficiency. This method, proposed by Simar and Wilson (2007), relaxes several deficiencies of previous two-stage analyses, which regress non-parametric estimates of bank efficiency on exogenous determinants. In particular, we propose a bootstrap...

The goal of this paper is to introduce a partially adaptive estimator for the censored regression model based on an error structure described by a mixture of two normal distributions. The model we introduce is easily estimated by maximum likelihood using the EM algorithm adapted from the work of Bartolucci and Scaccia (2004). A Monte Carlo study is conducted to examine the small sample properti...

In several regression applications, a different structural relationship might be anticipated for the higher or lower responses than the average responses. In such cases, quantile regression analysis can uncover important features that would likely be overlooked by traditional mean regression. We develop a Bayesian method for fully nonparametric model-based quantile regression. The approach invo...

This paper investigates how managerial capacity aspects influence efficiency of dairy farms in Sweden. Based on non-parametric methods, Tobit and logistic regressions, several managerial capacity aspects are found to influence long and short run input efficiency scores, but to influence output efficiency less. Examples of important aspects are: internal locus of control, positive profitability ...