-
作者：Dunson, D. B.; Johndrow, J. E.
作者单位：Duke University; University of Pennsylvania
摘要：In a 1970 Biometrika paper, W. K. Hastings developed a broad class of Markov chain algorithms for sampling from probability distributions that are difficult to sample from directly. The algorithm draws a candidate value from a proposal distribution and accepts the candidate with a probability that can be computed using only the unnormalized density of the target distribution, allowing one to sample from distributions known only up to a constant of proportionality. The stationary distribution o...
-
作者：Wang, Yixin; Zubizarreta, Jose R.
作者单位：Columbia University; Harvard University
摘要：Weighting methods are widely used to adjust for covariates in observational studies, sample surveys, and regression settings. In this paper, we study a class of recently proposed weighting methods, which find the weights of minimum dispersion that approximately balance the covariates. We call these weights 'minimal weights' and study them under a common optimization framework. Our key observation is that finding weights which achieve approximate covariate balance is equivalent to performing sh...
-
作者：Legramanti, Sirio; Durante, Daniele; Dunson, David B.
作者单位：Bocconi University; Duke University
摘要：The dimension of the parameter space is typically unknown in a variety of models that rely on factorizations. For example, in factor analysis the number of latent factors is not known and has to be inferred from the data. Although classical shrinkage priors are useful in such contexts, increasing shrinkage priors can provide a more effective approach that progressively penalizes expansions with growing complexity. In this article we propose a novel increasing shrinkage prior, called the cumula...
