Empirical estimates for various correlations in longitudinal-dynamic heteroscedastic hierarchical normal models

Shrinkage estimates for multi-level heteroscedastic hierarchical normal linear models

Empirical Bayes approach is an attractive method for estimating hyperparameters in hierarchical models. But, under the assumption of normality for a multi-level heteroscedastic hierarchical model, which involves several explanatory variables, the analyst may often wonder whether the shrinkage estimators have efficient asymptotic properties in spite of the fact they involve numerous hyperparamet...

SURE Estimates for a Heteroscedastic Hierarchical Model.

Hierarchical models are extensively studied and widely used in statistics and many other scientific areas. They provide an effective tool for combining information from similar resources and achieving partial pooling of inference. Since the seminal work by James and Stein (1961) and Stein (1962), shrinkage estimation has become one major focus for hierarchical models. For the homoscedastic norm...

Empirical likelihood for heteroscedastic partially linear models

AMS 2000 subject classifications: 62F35 62G20 Keywords: Double robustness Empirical likelihood Heteroscedasticity Kernel estimation Partially linear model Semiparametric efficiency a b s t r a c t We make empirical-likelihood-based inference for the parameters in heteroscedastic partially linear models. Unlike the existing empirical likelihood procedures for heteroscedastic partially linear mod...

Optimal Shrinkage Estimation in Heteroscedastic Hierarchical Models

Hierarchical models are powerful statistical tools widely used in scientific and engineering applications. The homoscedastic (equal variance) case has been extensively studied, and it is well known that shrinkage estimates, the James-Stein estimate in particular, offer nice theoretical (e.g., risk) properties. The heteroscedastic (the unequal variance) case, on the other hand, has received less...

Doubly fractional models for dynamic heteroscedastic cycles

Strong persistence is a common phenomenon that has been documented not only in the levels but also in the volatility of many time series. The class of doubly fractional models is extended to include the possibility of long memory in cyclical (non-zero) frequencies in both the levels and the volatility and a new model, the GARMA-GARMASV (Gegenbauer AutoRegressive Mean Average Id. Stochastic Vola...

Heteroscedastic T-Optimum Designs for Multiresponse Dynamic Models