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...

It is not unusual for the response variable in a regression model to be subject to censoring or truncation. Tobit regression models are specific examples of such a situation, where for some observations the observed response is not the actual response, but the censoring value (often zero), and an indicator that censoring (from below) has occurred. It is well-known that the maximum likelihood es...

This paper studies financial behaviors of households in China’s Beijing urban area. Demographic and psychometrical data were collected through questionnaire surveys and analyzed by logistic regression and Tobit regression methods. Several important factors which affect the willingness of the investigated households to participate in investment activities and their portfolio allocation decisions...

The censored regression model and the Tobit model are standard tools in econometrics. This paper provides a formal asymptotic theory for dynamic time series censored regression when lags of the dependent variable have been included among the regressors. We derive fading memory properties of the model under the assumption that the regression error is strong mixing. We show the formal asymptotic ...

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...