-
作者：Tzeng, JY; Roeder, K
作者单位：North Carolina State University; Carnegie Mellon University
-
作者：Li, HZ
作者单位：University of Pennsylvania
-
作者：Teh, Yee Whye; Jordan, Michael I.; Beal, Matthew J.; Blei, David M.
作者单位：University of California System; University of California Berkeley; State University of New York (SUNY) System; University at Buffalo, SUNY; Princeton University
摘要：We consider problems involving groups of data where each observation within a group is a draw from a mixture model and where it is desirable to share mixture components between groups. We assume that the number of mixture components is unknown a priori and is to be inferred from the data. In this setting it is natural to consider sets of Dirichlet processes, one for each group, where the well-known clustering property of the Dirichlet process provides a nonparametric prior for the number of mi...
-
作者：Zeng, Donglin; Yin, Guosheng; Ibrahim, Joseph G.
作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of Texas System; UTMD Anderson Cancer Center
摘要：We propose a class of transformation models for survival data with a cure fraction. The class of transformation models is motivated by biological considerations and includes both the proportional hazards and the proportional odds cure models as two special cases. An efficient recursive algorithm is proposed to calculate the maximum likelihood estimators (MLEs). Furthermore, the MLEs for the regression coefficients are shown to be consistent and asymptotically normal, and their asymptotic varia...
-
作者：Wang, Xinlei; Stokes, Lynne; Lim, Johan; Chen, Min
作者单位：Southern Methodist University; Yonsei University; University of Texas System; University of Texas Austin
摘要：We generalize the definition of a concomitant of an order statistic in the multivariate case, develop general expressions for its density, and establish related properties. We study the concomitant of a normal random vector in detail and discuss methods for calculating its moments. Furthermore, we apply the theory to develop new estimators of the mean from a judgment poststratified sample, where poststrata are formed by rank classes of auxiliary variables. Our estimators are shown to be more e...
-
作者：Sabatti, C
作者单位：University of California System; University of California Los Angeles; University of California System; University of California Los Angeles
-
作者：Jones, Galin L.; Haran, Murali; Caffo, Brian S.; Neath, Ronald
作者单位：University of Minnesota System; University of Minnesota Twin Cities; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Johns Hopkins University
摘要：Markov chain Monte Carlo is a method of producing a correlated sample to estimate features of a target distribution through ergodic averages. A fundamental question is when sampling should stop; that is, at what point the ergodic averages are good estimates of the desired quantities. We consider a method that stops the simulation when the width of a confidence interval based on an ergodic average is less than a user-specified value. Hence calculating a Monte Carlo standard error is a critical ...
-
作者：Jensen, Soren Tolver; Madsen, Jesper
作者单位：University of Copenhagen; Novo Nordisk
摘要：We consider k groups of observations X-11...,X(1n)1..,X-kl...,X-knk and unknown scalars lambda l,...,lambda k, and we assume that the distribution of the scaled observations X-11/ lambda(1),...,X-ln1/lambda(1),...,X-k1/lambda X-k,...,(knk)/lambda(k) follows a normal linear model on Rnl+...+nk. This general setup includes several interesting models that have appeared in the literature in different contexts and fields of application. The simplest example is the model of equal coefficients of var...
-
作者：Mueller, Hans-Georg; Stadtmueller, Ulrich; Yao, Fang
作者单位：University of California System; University of California Davis; Ulm University; Colorado State University System; Colorado State University Fort Collins
摘要：We introduce the notion of a functional variance process to quantify variation in functional data. The functional data are modeled as samples of smooth random trajectories observed under additive noise. The noise is assumed to be composed of white noise and a smooth random process-the functional variance process-which gives rise to smooth random trajectories of variance. The functional variance process is a tool for analyzing stochastic time trends in noise variance. As a smooth random process...
-
作者：Liu, Ruixue; Owen, Art B.
作者单位：Stanford University
摘要：Analysis of variance (ANOVA) is now often applied to functions defined on the unit cube, where it serves as a tool for the exploratory analysis of functions. The mean dimension of a function, defined as a natural weighted combination of its ANOVA mean squares, provides one measure of how hard or easy it is to integrate the function by quasi-Monte Carlo sampling. This article presents some new identities relating the mean dimension, and some analogously defined higher moments, to the variance i...
