Sat, 19 Jul 2008 00:41:30 BST CiteULike: mshafiei's nonparametric http://www.citeulike.org/user/mshafiei/tag/nonparametric CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/mshafiei/article/2570774 <i>pp. 317-327.</i><br /><br />In analyzing data from multiple related studies, it often is of interest to borrow information across studies and to cluster similar studies. Although parametric hierarchical models are commonly used, of concern is sensitivity to the form chosen for the random-effects distribution. A Dirichlet process (DP) prior can allow the distribution to be unknown, while clustering studies; however, the DP does not allow local clustering of studies with respect to a subset of the coefficients without making independence assumptions. Motivated by this problem, we propose a matrix stick-breaking process (MSBP) as a prior for a matrix of random probability measures. Properties of the MSBP are considered, and methods are developed for posterior computation using Markov chain Monte Carlo. Using the MSBP as a prior for a matrix of study-specific regression coefficients, we demonstrate advantages over parametric modeling in simulated examples. The methods are further illustrated using a multinational uterotrophic bioassay study. The Matrix Stick-Breaking Process: Flexible Bayes Meta-Analysis David Dunson pp. 317-327. 2008-03-21T22:39:53-00:00 317 327 bayesian clustering dependent-partition dirichlet-process hierarchical matrix mixture-model nonparametric prior http://www.citeulike.org/user/mshafiei/article/2517325 <i>J. Mach. Learn. Res., Vol. 6 (2005), pp. 1551-1577.</i> A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior Hal Iii Daniel Marcu J. Mach. Learn. Res., Vol. 6 (2005), pp. 1551-1577. 2008-03-12T01:37:16-00:00 2005 J. Mach. Learn. Res. 1533-7928 6 1551 1577 MIT Press bayesian clustering conjugate crp mcmc non-conjugate nonparametric prior sampling supervised http://www.citeulike.org/user/mshafiei/article/2437252 <i>International Statistical Review / Revue Internationale de Statistique, Vol. 67, No. 2. (1999), pp. 187-210.</i><br /><br />The object of this paper is to review the main results obtained in semi- and non-parametric Bayesian analysis of duration models. Standard nonparametric Bayesian models for independent and identically distributed observations are reviewed in line with Ferguson's pioneering papers. Recent results on the characterization of Dirichlet processes and on nonparametric treatment of censoring and of heterogeneity in the context of mixtures of Dirichlet processes are also discussed. The final section considers a Bayesian semiparametric version of the proportional hazards model. /// L'objectif de cet article est de présenter les résultats principaux obtenus dans l'analyse bayésienne semi-paramétrique ou non-paramétrique des modèles de durée. Les résultats fondamentaux du modèle de base (données indépendantes et identiquement distribuées) sont rappelés en suivant les travaux initiaux de Ferguson et en utilisant des résultats récents relatifs aux représentations du processus de Dirichlet. La considération de mélanges de processus de Dirichlet permet d'étudier l'impact de la présence de données censurées et l'introduction de l'hétérogénéité non observée. La dernière section examine le traitement bayésien de modèles semi-paramétriques à risques proportionnels. Semi- and Non-Parametric Bayesian Analysis of Duration Models with Dirichlet Priors: A Survey JP Florens M Mouchart JM Rolin International Statistical Review / Revue Internationale de Statistique, Vol. 67, No. 2. (1999), pp. 187-210. 2008-02-27T15:17:54-00:00 1999 International Statistical Review / Revue Internationale de Statistique 67 2 187 210 bayesian dirichlet duration-models nonparametric http://www.citeulike.org/user/mshafiei/article/634913 <i>Journal of Computational and Graphical Statistics, Vol. 9, No. 2. (2000), pp. 249-265.</i><br /><br />This article reviews Markov chain methods for sampling from the posterior distribution of a Dirichlet process mixture model and presents two new classes of methods. One new approach is to make Metropolis-Hastings updates of the indicators specifying which mixture component is associated with each observation, perhaps supplemented with a partial form of Gibbs sampling. The other new approach extends Gibbs sampling for these indicators by using a set of auxiliary parameters. These methods are simple to implement and are more efficient than previous ways of handling general Dirichlet process mixture models with non-conjugate priors. Markov Chain Sampling Methods for Dirichlet Process Mixture Models Radford Neal Journal of Computational and Graphical Statistics, Vol. 9, No. 2. (2000), pp. 249-265. 2006-05-15T02:10:22-00:00 2000 Journal of Computational and Graphical Statistics 9 2 249 265 bayesian dirichlet mcmc nonparametric http://www.citeulike.org/user/mshafiei/article/306414 <i>Journal of the American Statistical Association, Vol. 86, No. 413. (1991), pp. 205-224.</i><br /><br />Advances in computation and the fast and cheap computational facilities now available to statisticians have had a significant impact upon statistical research, and especially the development of nonparametric data analysis procedures. In particular, theoretical and applied research on nonparametric density estimation has had a noticeable influence on related topics, such as nonparametric regression, nonparametric discrimination, and nonparametric pattern recognition. This article reviews recent developments in nonparametric density estimation and includes topics that have been omitted from review articles and books on the subject. The early density estimation methods, such as the histogram, kernel estimators, and orthogonal series estimators are still very popular, and recent research on them is described. Different types of restricted maximum likelihood density estimators, including order-restricted estimators, maximum penalized likelihood estimators, and sieve estimators, are discussed, where restrictions are imposed upon the class of densities or on the form of the likelihood function. Nonparametric density estimators that are data-adaptive and lead to locally smoothed estimators are also discussed; these include variable partition histograms, estimators based on statistically equivalent blocks, nearest-neighbor estimators, variable kernel estimators, and adaptive kernel estimators. For the multivariate case, extensions of methods of univariate density estimation are usually straightforward but can be computationally expensive. A method of multivariate density estimation that did not spring from a univariate generalization is described, namely, projection pursuit density estimation, in which both dimensionality reduction and density estimation can be pursued at the same time. Finally, some areas of related research are mentioned, such as nonparametric estimation of functionals of a density, robust parametric estimation, semiparametric models, and density estimation for censored and incomplete data, directional and spherical data, and density estimation for dependent sequences of observations. Recent Developments in Nonparametric Density Estimation Alan Izenman Journal of the American Statistical Association, Vol. 86, No. 413. (1991), pp. 205-224. 2005-08-29T22:46:29-00:00 1991 Journal of the American Statistical Association 86 413 205 224 bayesian nonparametric http://www.citeulike.org/user/mshafiei/article/936707 <i>The Annals of Statistics, Vol. 1, No. 2. (1973), pp. 209-230.</i> A Bayesian Analysis of Some Nonparametric Problems Thomas Ferguson The Annals of Statistics, Vol. 1, No. 2. (1973), pp. 209-230. 2006-11-08T17:44:16-00:00 1973 The Annals of Statistics 1 2 209 230 bayesian nonparametric http://www.citeulike.org/user/mshafiei/article/965794 <i>Journal of the American Statistical Association, Vol. 101, No. 476. (December 2006), pp. 1566-1581.</i> Hierarchical Dirichlet Processes Teh Yee Whye Jordan I Michael Beal J Matthew Blei M David doi:10.1198/016214506000000302 Journal of the American Statistical Association, Vol. 101, No. 476. (December 2006), pp. 1566-1581. 2006-11-29T01:37:01-00:00 2006 Journal of the American Statistical Association 0162-1459 101 476 1566 1581 American Statistical Association bayesian clustering dirichlet nonparametric