نتایج جستجو برای: james stein estimator

تعداد نتایج: 56551  

2007
Adrian Wiestner Mahsa Tehrani Michael Chiorazzi George Wright Federica Gibellini Kazutaka Nakayama Hui Liu Andreas Rosenwald H. Konrad Muller-Hermelink German Ott Wing C. Chan Timothy C. Greiner Dennis D. Weisenburger Julie Vose James O. Armitage Randy D. Gascoyne Joseph M. Connors Elias Campo Emilio Montserrat Francesc Bosch Erlend B. Smeland Stein Kvaloy Harald Holte Jan Delabie Richard I. Fisher Thomas M. Grogan Thomas P. Miller Wyndham H. Wilson Elaine S. Jaffe

Adrian Wiestner,1,2 Mahsa Tehrani,2 Michael Chiorazzi,1 George Wright,1 Federica Gibellini,2 Kazutaka Nakayama,2 Hui Liu,2 Andreas Rosenwald,3 H. Konrad Muller-Hermelink,3 German Ott,3 Wing C. Chan,4 Timothy C. Greiner,4 Dennis D. Weisenburger,4 Julie Vose,4 James O. Armitage,4 Randy D. Gascoyne,5 Joseph M. Connors,5 Elias Campo,6 Emilio Montserrat,6 Francesc Bosch,6 Erlend B. Smeland,7 Stein K...

2005
Shinichi Nakajima Sumio Watanabe

It is well known that in unidentifiable models, the Bayes estimation has the advantage of generalization performance to the maximum likelihood estimation. However, accurate approximation of the posterior distribution requires huge computational costs. In this paper, we consider an empirical Bayes approach where a part of the parameters are regarded as hyperparameters, which we call a subspace B...

1999
Akimichi Takemura Satoshi Kuriki

In Kuriki and Takemura (1997a) we established a general theory of James-Stein type shrinkage to convex sets with smooth boundary. In this paper we show that our results can be generalized to the case where shrinkage is toward smooth non-convex cones. A primary example of this shrinkage is descriptive principal component analysis, where one shrinks small singular values of the data matrix. Here ...

2009
G. Yüksel M. Candan Çetin

Pre-test estimator has been studied to estimate the mean of a normal distribution when non-sample prior information is avaliable. Our aim is to consider pre-test estimator of the mean in thepresence of outliers. A well known procedure to define pre-test estimator of the mean is using thesample mean. However, the sample mean is not a robust location parameter. In order to overcom...

2007
Alfred Hamerle Wei Lin

The present paper deals with the estimation of a frailty model of multivariate failure times The failure times are modeled by an Accelerated Failure Time Model including observed covariates and an unobservable frailty component The frailty is assumed random and di ers across elementary units but is constant across the spells of a unit or a group We develop an estimator of the regression paramet...

2009
Lawrence D. Brown Linda H. Zhao LINDA H. ZHAO

Many multivariate Gaussian models can conveniently be split into independent, block-wise problems. Common settings where this situation arises are balanced ANOVA models, balanced longitudinal models, and certain block-wise shrinkage estimators in nonparametric regression estimation involving orthogonal bases such as Fourier or wavelet bases. It is well known that the standard, least squares est...

2003
Michael P. Dubé James H. Stein Judith A. Aberg Carl J. Fichtenbaum John G. Gerber Karen T. Tashima W. Keith Henry Judith S. Currier Dennis Sprecher

Michael P. Dubé, James H. Stein, Judith A. Aberg, Carl J. Fichtenbaum, John G. Gerber, Karen T. Tashima, W. Keith Henry, Judith S. Currier, Dennis Sprecher, and Marshall J. Glesby, for the Adult AIDS Clinical Trials Group Cardiovascular Subcommittee Indiana University, Indianapolis; University of Wisconsin, Madison; Washington University, St. Louis, Missouri; University of Cincinnati and Clevel...

2007
Jeffrey S. Rosenthal

We analyze a hierarchical Bayes model which is related to the usual empirical Bayes formulation of James-Stein estimators. We consider running a Gibbs sampler on this model. Using previous results about convergence rates of Markov chains, we provide rigorous, numerical, reasonable bounds on the running time of the Gibbs sampler, for a suitable range of prior distributions. We apply these result...

2013
Kai-Tai Fang Gang Li Xuyang Lu Hong Qin

This paper develops a new empirical likelihood method for semiparametric linear regression with a completely unknown error distribution and right censored survival data. The method is based on the Buckley-James (1979) estimating equation. It inherits some appealing properties of the complete data empirical likelihood method. For example, it does not require variance estimation which is problema...

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