Censored Regression with Conditional Heteroscedasticity
Create a Censored Logistic Distribution
Create a Censored Normal Distribution
Create a Censored Student's T Distribution
The Censored Logistic Distribution
The Censored Normal Distribution
Methods for Boosted crch Models
Methods for Fitted crch Models
Methods for Fitted hxlr Models
Auxiliary Functions for Boosting crch Models
Control Options for crch Models
Censored and Truncated Regression with Conditional Heteroscedasticy
Auxiliary Functions for Stability Selection Using Boosting
The Censored Student-t Distribution
Control Options for hxlr Models
Heteroscedastic Extended Logistic Regression
Visualizing Coefficient Paths for Boosted crch Models
Predictions for Boosted crch Models
Predictions for Fitted crch Models
Predictions for Fitted hxlr Models
The Truncated Logistic Distribution
The Truncated Normal Distribution
Create a Truncated Logistic Distribution
Create a Truncated Normal Distribution
Create a Truncated Student's T Distribution
The Truncated Student-t Distribution
Different approaches to censored or truncated regression with conditional heteroscedasticity are provided. First, continuous distributions can be used for the (right and/or left censored or truncated) response with separate linear predictors for the mean and variance. Second, cumulative link models for ordinal data (obtained by interval-censoring continuous data) can be employed for heteroscedastic extended logistic regression (HXLR). In the latter type of models, the intercepts depend on the thresholds that define the intervals. Infrastructure for working with censored or truncated normal, logistic, and Student-t distributions, i.e., d/p/q/r functions and distributions3 objects.
Useful links