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Penalized regression, standard errors, and Bayesian lassos
The performance of the Bayesian lassos is compared to their fre- quentist counterparts using simulations, data sets that previous lasso papers have used, and a di-cult modeling problem for predicting the collapse of governments around the world.
Small Area Estimation: An Appraisal
Empirical best linear unbiased prediction as well as empirical and hierarchical Bayes seem to have a distinct advantage over other methods in small area estimation.
Multivariate negative dependence
- M. Ghosh
Various notions of multivariate negative dependence are introduced and their interrelationship is studied. Examples are given to illustrate these concepts. Applications of the results in statistics…
Constrained Bayes Estimation with Applications
- M. Ghosh
- 1 June 1992
Abstract Bayesian techniques are widely used in these days for simultaneous estimation of several parameters in compound decision problems. Often, however, the main objective is to produce an…
Bayesian Variable Selection and Estimation for Group Lasso
The Bayesian group lasso is revisits and the Bayesian sparse group selection is proposed again with spike and slab priors to select variables both at the group level and also within a group, and it is demonstrated via simulation that the posterior median estimator of the spikeand slab models has excellent performance for both variable selection and estimation.
Second-order probability matching priors
SUMMARY The paper considers priors obtained by ensuring approximate frequentist validity of (a) posterior quantiles, and of (b) the posterior distribution function. It is seen that, at the second…
Sequential Estimation: Ghosh/Sequential
Some remarks on noninformative priors
Abstract This article focuses primarily on a comparison between the reference priors of Berger and Bernardo and the reverse reference priors suggested by J. K. Ghosh. Sufficient conditions are given…
Design Issues for Generalized Linear Models: A Review
Generalized linear models (GLMs) have been used quite effectively in the modeling of a mean response under nonstandard conditions, where discrete as well as continuous data distributions can be…
Bayesian classification of tumours by using gene expression data
It is shown through simulation and examples that support vector machine models with multiple shrinkage parameters produce fewer misclassification errors than several existing classical methods as well as Bayesian methods based on the logistic likelihood or those involving only one shrinkage parameter.