Degeneracy in Heteroscedastic Regression Models

Variance Function Estimation in Heteroscedastic Regression Models

Efficient Quantile Regression for Heteroscedastic Models

Quantile regression provides estimates of a range of conditional quantiles. This stands in contrast to traditional regression techniques, which focus on a single conditional mean function. Lee et al. (2012) proposed efficient quantile regression by rounding the sharp corner of the loss. The main modification generally involves an asymmetric l2 adjustment of the loss function around zero. We ext...

Assessing the adequacy of variance function in heteroscedastic regression models.

Heteroscedastic data arise in many applications. In heteroscedastic regression analysis, the variance is often modeled as a parametric function of the covariates or the regression mean. We propose a kernel-smoothing type nonparametric test for checking the adequacy of a given parametric variance structure. The test does not need to specify a parametric distribution for the random errors. It is ...

Generalizing Full Rank Conditions in Heteroscedastic Censored Regression Models

Powell's(1984) Censored Least Absolute Deviations (CLAD) estimator for the censored linear regression model has been regarded as a desirable alternative to maximum likelihood estimation methods due to its robustness to conditional heteroscedasticity and distributional misspeci ̄cation of the error term. However, the CLAD estimation procedure has failed in certain empirical applications (e.g. Hon...

Minimum distance conditional variance function checking in heteroscedastic regression models