-
作者：Jackson, Christopher; Presanis, Anne; Conti, Stefano; De Angelis, Daniela
作者单位：MRC Biostatistics Unit; University of Cambridge
摘要：Suppose we have a Bayesian model that combines evidence from several different sources. We want to know which model parameters most affect the estimate or decision from the model, or which of the parameter uncertainties drive the decision uncertainty. Furthermore, we want to prioritize what further data should be collected. These questions can be addressed by Value of Information (VoI) analysis, in which we estimate expected reductions in loss from learning specific parameters or collecting da...
-
作者：Zhang, Kai
作者单位：University of North Carolina; University of North Carolina Chapel Hill
摘要：We study the problem of nonparametric dependence detection. Many existing methods may suffer severe power loss due to nonuniform consistency, which we illustrate with a paradox. To avoid such power loss, we approach the nonparametric test of independence through the new framework of binary expansion statistics (BEStat) and binary expansion testing (BET), which examine dependence through a novel binary expansion filtration approximation of the copula. Through a Hadamard transform, we find that ...
-
作者：Li, Furong; Sang, Huiyan
作者单位：Ocean University of China; Texas A&M University System; Texas A&M University College Station
摘要：Spatial regression models have been widely used to describe the relationship between a response variable and some explanatory variables over a region of interest, taking into account the spatial dependence of the observations. In many applications, relationships between response variables and covariates are expected to exhibit complex spatial patterns. We propose a new approach, referred to as spatially clustered coefficient (SCC) regression, to detect spatially clustered patterns in the regre...
-
作者：Shin, Seung Jun; Yuan, Ying; Strong, Louise C.; Bojadzieva, Jasmina; Wang, Wenyi
作者单位：Korea University; University of Texas System; UTMD Anderson Cancer Center; University of Texas System; UTMD Anderson Cancer Center; University of Texas System; UTMD Anderson Cancer Center
摘要：Penetrance, which plays a key role in genetic research, is defined as the proportion of individuals with the genetic variants (i.e., genotype) that cause a particular trait and who have clinical symptoms of the trait (i.e., phenotype). We propose a Bayesian semiparametric approach to estimate the cancer-specific age-at-onset penetrance in the presence of the competing risk of multiple cancers. We employ a Bayesian semiparametric competing risk model to model the duration until individuals in a...
-
作者：Ma, Li; Mao, Jialiang
作者单位：Duke University
摘要：We introduce a methodcalled Fisher exact scanning (FES)for testing and identifying variable dependency that generalizes Fisher's exact test on 2 x 2 contingency tables to R x C contingency tables and continuous sample spaces. FES proceeds through scanning over the sample space using windows in the form of 2 x 2 tables of various sizes, and on each window completing a Fisher's exact test. Based on a factorization of Fisher's multivariate hypergeometric (MHG) likelihood into the product of the u...
-
作者：Lesage, Eric; Haziza, David; D'Haultfouille, Xavier
作者单位：Universite de Montreal; Institut Polytechnique de Paris; ENSAE Paris
摘要：Response rates have been steadily declining over the last decades, making survey estimates vulnerable to nonresponse bias. To reduce the potential bias, two weighting approaches are commonly used in National Statistical Offices: the one-step and the two-step approaches. In this article, we focus on the one-step approach, whereby the design weights are modified in a single step with two simultaneous goals in mind: reduce the nonresponse bias and ensure the consistency between survey estimates a...
-
作者：Pan, Wenliang; Wang, Xueqin; Xiao, Weinan; Zhu, Hongtu
作者单位：Sun Yat Sen University; University of Texas System; UTMD Anderson Cancer Center
摘要：Extracting important features from ultra-high dimensional data is one of the primary tasks in statistical learning, information theory, precision medicine, and biological discovery. Many of the sure independent screening methods developed to meet these needs are suitable for special models under some assumptions. With the availability of more data types and possible models, a model-free generic screening procedure with fewer and less restrictive assumptions is desirable. In this article, we pr...
-
作者：Quiroz, Matias; Kohn, Robert; Villani, Mattias; Minh-Ngoc Tran
作者单位：University of New South Wales Sydney; Linkoping University; University of Sydney
摘要：We propose subsampling Markov chain Monte Carlo (MCMC), an MCMC framework where the likelihood function for n observations is estimated from a random subset of m observations. We introduce a highly efficient unbiased estimator of the log-likelihood based on control variates, such that the computing cost is much smaller than that of the full log-likelihood in standard MCMC. The likelihood estimate is bias-corrected and used in two dependent pseudo-marginal algorithms to sample from a perturbed ...
-
作者：Lee, Kwonsang; Small, Dylan S.
作者单位：Harvard University; Harvard T.H. Chan School of Public Health; University of Pennsylvania
摘要：Malaria is a major health problem in many tropical regions. Fever is a characteristic symptom of malaria. The fraction of fevers that are attributable to malaria, the malaria attributable fever fraction (MAFF), is an important public health measure in that the MAFF can be used to calculate the number of fevers that would be avoided if malaria was eliminated. Despite such causal interpretation, the MAFF has not been considered in the framework of causal inference. We define the MAFF using the p...
-
作者：Chakraborty, Shubhadeep; Zhang, Xianyang
作者单位：Texas A&M University System; Texas A&M University College Station
摘要：Many statistical applications require the quantification of joint dependence among more than two random vectors. In this work, we generalize the notion of distance covariance to quantify joint dependence among random vectors. We introduce the high-order distance covariance to measure the so-called Lancaster interaction dependence. The joint distance covariance is then defined as a linear combination of pairwise distance covariances and their higher-order counterparts which together completely ...
