Doubly Stochastic Poisson Processes
Author | : J. Grandell |
Publisher | : Springer |
Total Pages | : 244 |
Release | : 2006-11-14 |
Genre | : Mathematics |
ISBN | : 3540382585 |
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Author | : J. Grandell |
Publisher | : Springer |
Total Pages | : 244 |
Release | : 2006-11-14 |
Genre | : Mathematics |
ISBN | : 3540382585 |
Author | : Günter Last |
Publisher | : |
Total Pages | : 26 |
Release | : 1988 |
Genre | : |
ISBN | : |
Author | : Vinaykumar K. Ingle |
Publisher | : |
Total Pages | : 230 |
Release | : 1981 |
Genre | : |
ISBN | : |
Author | : Anders Tolver Jensen |
Publisher | : |
Total Pages | : 132 |
Release | : 2005 |
Genre | : |
ISBN | : |
Author | : Tingting Zhang |
Publisher | : |
Total Pages | : 234 |
Release | : 2008 |
Genre | : |
ISBN | : |
We illustrate through examples that those nonparametric Bayes estimates based on the Bernstein-Dirichlet process are more robust to sample variation and tend to have smaller estimation errors than those based on the Dirichlet process. In certain settings, the new estimators can even outperform Stein's estimator and Efron and Morris's limited translation estimator. Chapter 3 examines the asymptotic behavior of the correlation pursuit stepwise variable selection procedure that has been proposed recently by (Zhong et al ., 2008). More specifically, we analyze the asymptotic distribution of the test statistics under the null hypothesis of no effect for selected predictors and the power of the test under the alternative hypothesis. We also compare the new procedure with the classical linear regression algorithm for linear models, and discuss the possibility of generalizing the method to multiple index models.
Author | : Columbia University. Department of Mathematics |
Publisher | : |
Total Pages | : 20 |
Release | : 1977 |
Genre | : Air |
ISBN | : |
Author | : John G. Romanski |
Publisher | : |
Total Pages | : 368 |
Release | : 1976 |
Genre | : |
ISBN | : |
Author | : Robert B. Asher |
Publisher | : |
Total Pages | : 28 |
Release | : 1976 |
Genre | : Poisson processes, Doubly stochastic |
ISBN | : |
The optimal adaptive estimator structures for a class of doubly stochastic Poisson processes (DSPP) are presented. The structure is used along with a moment assumption to obtain implementable estimators. The class of DSPP considered is that of a linear Markov diffusion process modulating a linear intensity rate. The uncertainty for which the adaptation process is developed includes both structure uncertainty in the Markov diffusion process, and parameter uncertainty in the Markov diffusion process and the intensity rate process. Results are given on the problem of adaptation of which of a finite number of Markov realizations is modulating the intensity process. The nonlinear adaptive estimator structures are obtained by use of a particular theorem that yields an optimal structure for the adaptive estimator. The structure is used to obtain a quasi-optimal adaptive estimator for the problem by use of a zero third central moment assumption. The estimator structure consists of a nonlinear, nonadaptive part, and a nonlinear, adaptive part which contains the parameter structure adaptations.
Author | : Kuang-Hua Daphne Chou |
Publisher | : |
Total Pages | : 354 |
Release | : 2002 |
Genre | : Point processes |
ISBN | : |
Author | : |
Publisher | : |
Total Pages | : |
Release | : 1976 |
Genre | : |
ISBN | : |
Some questions associated with statistical inference of doubly stochastic Poisson processes are discussed. This general model appears to be of value in studying a number of problems in environmental health in which the factors which affect the rate at which certain diseases occur cannot be deterministically characterized. The model's applicability to a study of air pollution and incidence of acute respiratory diseases in the New York City area is examined.