Abstract
The crch package provides functions for maximum likelihood estimation of censored or truncated regression models with conditional heteroscedasticity along with suitable standard methods to summarize the fitted models and compute predictions, residuals, etc. The supported distributions include leftor right-censored or truncated Gaussian, logistic, or student-t distributions with potentially different sets of regressors for modeling the conditional location and scale. The models and their R implementation are introduced and illustrated by numerical weather prediction tasks using precipitation data for Innsbruck (Austria).
Highlights
Censored or truncated response variables occur in a variety of applications
If wind speeds below this minimum are recorded as ≤ minimum the data are censored
Linear models like the tobit or truncated regression models assume homoscedasticity which means that the variance of an underlying normal distribution does not depend on covariates
Summary
Censored or truncated response variables occur in a variety of applications. Censored data arise if exact values are only reported in a restricted range. Linear models like the tobit or truncated regression models assume homoscedasticity which means that the variance of an underlying normal distribution does not depend on covariates. Sometimes this assumption does not hold and models that can consider conditional heteroscedasticity should be used. Such models have been proposed, e.g., for generalized linear models (Nelder and Pregibon, 1987; Smyth, 1989), generalized additive models (Rigby and Stasinopoulos, 1996, 2005), or beta regression (Cribari-Neto and Zeileis, 2010). These models and their implementation are illustrated with numerical weather prediction data of precipitation in Innsbruck (Austria)
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