Survival Regression Models With Dependent Bayesian Nonparametric Priors

We present a novel Bayesian nonparametric model for regression in survival analysis. Our model builds on the classical neutral to the right model of Doksum and on the Cox proportional hazards model of Kim and Lee. The use of a vector of dependent Bayesian nonparametric priors allows us to efficiently model the hazard as a function of covariates while allowing nonproportionality. The model can be seen as having competing latent risks. We characterize the posterior of the underlying dependent vector of completely random measures and study the asymptotic behavior of the model. We show how an MCMC scheme can provide Bayesian inference for posterior means and credible intervals. The method is illustrated using simulated and real data. Supplementary materials for this article are available online

New isometry of Krall-Laguerre orthogonal polynomials in martingale spaces

In this paper we study how an inner product derived from an Uvarov transformation of the Laguerre weight function is used in the orthogonalization procedure of a sequence of martingales related to a Levy process. The orthogonalization is done by isometry. The resulting set of pairwise strongly orthogonal martingales involved are used as integrators in the so-called chaotic representation propertyEdmundo J. Huertas is supported by a grant from Ministerio de Ciencia e Innovación (MTM 2009-12740-C03-01), and by Fundação para a Ciência e Tecnologia (FCT), ref. SFRH/BPD/91841/2012, Portugal. Nuria Torrado is supported by Fundação para a Ciência e Tecnologia (FCT), ref. SFRH/BPD/91832/2012, Portugal. The research of Fabrizio Leisen has been partially supported by the Spanish Ministry of Science and Innovation through grant ECO2011-2570

Modelling and Computation Using NCoRM Mixtures for Density Regression

Normalized compound random measures are flexible nonparametric priors for related distributions. We consider building general nonparametric regression models using normalized compound random measure mixture models. Posterior inference is made using a novel pseudo-marginal Metropolis-Hastings sampler for normalized compound random measure mixture models. The algorithm makes use of a new general approach to the unbiased estimation of Laplace functionals of compound random measures (which includes completely random measures as a special case). The approach is illustrated on problems of density regression

Application of LANDSAT data and digital image processing

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Interacting Multiple Try Algorithms with Different Proposal Distributions

We propose a new class of interacting Markov chain Monte Carlo (MCMC) algorithms designed for increasing the efficiency of a modified multiple-try Metropolis (MTM) algorithm. The extension with respect to the existing MCMC literature is twofold. The sampler proposed extends the basic MTM algorithm by allowing different proposal distributions in the multiple-try generation step. We exploit the structure of the MTM algorithm with different proposal distributions to naturally introduce an interacting MTM mechanism (IMTM) that expands the class of population Monte Carlo methods. We show the validity of the algorithm and discuss the choice of the selection weights and of the different proposals. We provide numerical studies which show that the new algorithm can perform better than the basic MTM algorithm and that the interaction mechanism allows the IMTM to efficiently explore the state space

String-like Clusters and Cooperative Motion in a Model Glass-Forming Liquid

A large-scale molecular dynamics simulation is performed on a glass-forming Lennard-Jones mixture to determine the nature of dynamical heterogeneities which arise in this model fragile liquid. We observe that the most mobile particles exhibit a cooperative motion in the form of string-like paths (``strings'') whose mean length and radius of gyration increase as the liquid is cooled. The length distribution of the strings is found to be similar to that expected for the equilibrium polymerization of linear polymer chains.Comment: 6 pages of RevTex, 6 postscript figures, uses epsf.st

Doctor of Education Newsletter 2020

WSU Doctor of Education Cohort 2020 This newsletter was created by the second Education Doctorate graduate student cohort 2020.https://openriver.winona.edu/educationeddnewsletters/1001/thumbnail.jp

Objective Bayesian Analysis of the Yule-Simon Distribution with Applications

The Yule-Simon distribution is usually employed in the analysis of frequency data. As the Bayesian literature, so far, has ignored this distribution, here we show the derivation of two objective priors for the parameter of the Yule--Simon distribution. In particular, we discuss the Jeffreys prior and a loss-based prior, which has recently appeared in the literature. We illustrate the performance of the derived priors through a simulation study and the analysis of real datasets