Journal of the American Statistical Association

最新文章

• Bayesian nonparametric spatial modeling with Dirichlet process mixing AI翻译
  作者：Gelfand, AE; Kottas, A; MacEachern, SN
  作者单位：Duke University; University of California System; University of California Santa Cruz; University System of Ohio; Ohio State University
  摘要：Customary modeling for continuous point-referenced data assumes a Gaussian process that is often taken to be stationary. When such models are fitted within a Bayesian framework, the unknown parameters of the process are assumed to be random. so a random Gaussian process results. Here we propose a novel spatial Dirichlet process mixture model to produce a random spatial process that is neither Gaussian nor stationary. We first develop a spatial Dirichlet process model for spatial data and discu...
• CATS: Clustering after transformation and smoothing AI翻译
  作者：Serban, N; Wasserman, L
  作者单位：Carnegie Mellon University
  摘要：CATS-clustering after transformation and smoothing-is a technique for nonparametrically estimating and clustering a large number of curves. Our motivating example is a genetic microarray experiment, but the method is very general. The method includes transformation and smoothing multiple curves, multiple nonparametric testing for screening out flat curves, clustering curves with similar shape, and nonparametrically inferring the clustering estimation error rate.
• Analyzing nonstationary spatial data using piecewise Gaussian processes AI翻译
  作者：Kim, HM; Mallick, BK; Holmes, CC
  作者单位：Konkuk University; Texas A&M University System; Texas A&M University College Station; University of Oxford
  摘要：In many problems in geostatistics the response variable of interest is strongly related to the underlying geology of the spatial location. In these situations there is often little correlation in the responses found in different rock strata, so the underlying covariance structure shows sharp changes at the boundaries of the rock types. Conventional stationary and nonstationary spatial methods are inappropriate, because they typically assume that the covariance between points is a smooth functi...
• Joint modeling and estimation for recurrent event processes and failure time data AI翻译
  作者：Huang, CY; Wang, MC
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health
  摘要：Recurrent event data are commonly encountered in longitudinal follow-up Studies related to biomedical science, econometrics. reliability, and demography. In many studies. recurrent events serve as important measurements for evaluating disease progression, health deterioration. or insurance risk. When analyzing recurrent event data, an independent censoring condition is typically required for the construction of statistical methods. In some situations. however, the terminating time for observin...
• Propriety of the posterior distribution and existence of the MLE for regression models with covariates missing at random AI翻译
  作者：Chen, MH; Ibrahim, JG; Shao, QM
  作者单位：University of Connecticut; University of North Carolina; University of North Carolina Chapel Hill; University of Oregon; National University of Singapore
  摘要：Characterizing model identifiability in the presence of missing covariate data is a very important issue in missing data problems. In this article. we characterize the propriety of the posterior distribution of the regression coefficients for some general classes of regression models, including the class of generalized linear models (GLM's) and parametric survival models with right-censored data. Toward this goal, we 14 derive some very general and easy-to-check conditions for the matrix of co...
• A two-stage regression model for epidemiological studies with multivariate disease classification data AI翻译
  作者：Chatterjee, N
  作者单位：National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics
  摘要：Polytomous logistic regression is commonly used to analyze epidemiological data with disease subtype information. In this approach effects of exposures on different disease subtypes are studied through separate exposure odds ratios comparing different case groups to the common control group. This article considers the situation where disease subtypes can be defined Using multiple characteristics of a disease. For efficient analysis of such data, a two-stage modeling approach is proposed. At th...
• Analysis of contaminant co-occurrence in community water systems AI翻译
  作者：Lockwood, JR; Schervish, MJ; Gurian, PL; Small, MJ
  作者单位：RAND Corporation; University of Texas System; University of Texas El Paso; Carnegie Mellon University; Carnegie Mellon University; Carnegie Mellon University
  摘要：The current framework for U.S. Environmental Protection Agency regulation of water quality in community drinking water supplies produces sequential rules for either single contaminants or small groups of similar contaminants. For both substantive and pragmatic reasons, some water industry experts have advocated the development of a more holistic regulatory process in which rules are promulgated less frequently but for larger contaminant classes. Such a framework would require the expansion of ...
• Causal inference with general treatment regimes: Generalizing the propensity score AI翻译
  作者：Imai, K; van Dyk, DA
  作者单位：Princeton University; University of California System; University of California Irvine
  摘要：In this article we develop the theoretical properties of the propensity function, which is a generalization of the propensity score of Rosenbaum and Rubin. Methods based on the propensity score have long been used for causal inference in observational studies; they are easy to use and can effectively reduce the bias caused by nonrandom treatment assignment. Although treatment regimes need not be binary in practice, the propensity score methods are generally confined to binary treatment scenari...
• Survival analysis with heterogeneous covariate measurement error AI翻译
  作者：Li, Y; Ryan, L
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute
  摘要：This article is motivated by a time-to-event analysis where the covariate of interest was measured at the wrong time. We show that the problem can be formulated as a special case of survival analysis with heterogeneous covariate measurement error and develop a general analytic framework. We study the asymptotic behavior of the naive partial likelihood estimates and analytically demonstrate that under the heterogeneous measurement error structure and the assumption that all components of the co...
• A model-based background adjustment for oligonucleotide expression arrays AI翻译
  作者：Wu, ZJ; Irizarry, RA; Gentleman, R; Martinez-Murillo, F; Spencer, F
  作者单位：Johns Hopkins University; Johns Hopkins University; Johns Hopkins University; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute
  摘要：High-density oligonucleotide expression arrays are widely used in many areas of biomedical research. Affymetrix GeneChip arrays are the most popular, In the Affymetrix system. a fair amount of further preprocessing and data reduction occurs after the image-processing step. Statistical procedures developed by academic groups have been successful in improving the default algorithms provided by the Affymetrix system. In this article we present a solution to one of the preprocessing steps-backgrou...