SURE Estimates for a Heteroscedastic Hierarchical Model
نویسندگان
چکیده
منابع مشابه
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...
متن کاملEmpirical estimates for various correlations in longitudinal-dynamic heteroscedastic hierarchical normal models
In this paper, we first define longitudinal-dynamic heteroscedastic hierarchical normal models. These models can be used to fit longitudinal data in which the dependency structure is constructed through a dynamic model rather than observations. We discuss different methods for estimating the hyper-parameters. Then the corresponding estimates for the hyper-parameter that causes the association...
متن کاملShrinkage 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...
متن کاملOptimal 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...
متن کاملAlmost sure estimates for the concentration neighborhood of Sinai’s walk
We consider Sinai's random walk in random environment. We prove that infinitely often (i.o.) the size of the concentration neighborhood of this random walk is almost surely bounded. As an application we get that i.o. the maximal distance between two favorite sites is almost surely bounded.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of the American Statistical Association
سال: 2012
ISSN: 0162-1459,1537-274X
DOI: 10.1080/01621459.2012.728154