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作者：Jandhyala, VK; Hawkins, DM; Fotopoulos, SB
作者单位：Washington State University; Washington State University; University of Minnesota System; University of Minnesota Twin Cities; Washington State University; Washington State University
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作者：Cai, TX; Pepe, MS
作者单位：University of Washington; University of Washington Seattle
摘要：The receiver operating characteristic (ROC) curve is a popular method for characterizing the accuracy of diagnostic tests when test results are not binary. Various methodologies for estimating and comparing ROC curves have been developed. One approach, due to Pepe, uses a parametric, regression model ROCx(u) = g(h(0)(u) +0(0)'x) with the baseline function h(0)(u) specified up to a finite-dimensional parameter. In this article we extend the regression models by allowing arbitrary nonparametric ...
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作者：Chernozhukov, V; Hong, H
作者单位：Massachusetts Institute of Technology (MIT); Princeton University
摘要：This article suggests very simple three-step estimators for censored quantile regression models with a separation restriction on the censoring probability. The estimators are theoretically attractive (i.e.. asymptotically as efficient as the celebrated Powell's censored least absolute deviation estimator). At the same time, they are conceptually simple and have trivial computational expenses. They are especially useful in samples of small size or models with many regressors. with desirable fin...
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作者：Marshall, P; Bradlow, ET
作者单位：Pontificia Universidad Catolica de Chile; University of Pennsylvania
摘要：We present a unified approach to conjoint analysis models using a Bayesian framework. One data source is used to form a prior distribution for the partworths, whereas full-profile evaluations under a rating scale, ranking, discrete choice, or constant-sum scale constitute the likelihood data (one model fits all). Standard existing models for conjoint analysis. considered in the literature. become particular cases of the proposed specification, and explicit formulas for the gains of using multi...
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作者：Cai, T; Cheng, SC; Wei, LJ
作者单位：University of Washington; University of Washington Seattle; Texas A&M University System; Texas A&M University College Station; Harvard University; Harvard T.H. Chan School of Public Health
摘要：The Cox proportional hazards model with a random effect has been proposed for the analysis of data which consist of a large number of small clusters of correlated failure time observations. The class of linear transformation models provides many useful alternatives to the Cox model for analyzing univariate failure time data. In this article, we generalize these models by incorporating random effects, which generate the dependence among the failure times within the cluster, to handle correlated...
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作者：Kibria, BMG; Sun, L; Zidek, JV; Le, ND
作者单位：State University System of Florida; Florida International University; University of British Columbia; British Columbia Cancer Agency
摘要：This article presents a multivariate spatial prediction methodology in a Bayesian framework, The method is especially suited for use in environmetrics, where vector-valued responses are observed at a small set of ambient monitoring stations ''(gauged sites)'' at successive time points. However, the stations may have varying start-up times so that the data have a ''staircase'' pattern (''monotone'' pattern in the terminology of Rubin and Shaffer). The lowest step corresponds to the newest stati...
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作者：Shen, XT; Ye, JM
作者单位：University System of Ohio; Ohio State University; City University of New York (CUNY) System; Baruch College (CUNY)
摘要：Most model selection procedures use a fixed penalty penalizing an increase in the size of a model. These nonadaptive selection procedures perform well only in one type of situation. For instance, Bayesian information criterion (BIC) with a large penalty per-forms well for small models and poorly for large models, and Akaike's information criterion (AIC) does just the opposite. This article proposes an adaptive model selection procedure that uses a data-adaptive complexity penalty based on a co...
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作者：He, XM; Hu, FF
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Virginia
摘要：Markov chain marginal bootstrap (MCMB) is a new method for constructing confidence intervals or regions for maximum likelihood estimators of certain parametric models and for a wide class of M estimators of linear regression. The MCMB method distinguishes itself from the usual bootstrap methods in two important aspects: it involves solving only one-dimensional equations for parameters of any dimension and produces a Markov chain rather than a (conditionally) independent sequence. It is designe...
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作者：Kelsall, J; Wakefield, J
作者单位：Lancaster University; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
摘要：A valuable public health practice is to examine disease incidence and mortality rates across geographic regions. The data available for the construction of disease maps are typically in the form of aggregate counts within sets of disjoint. politically defined areas, and the Poisson variation inherent in these counts can lead to extreme raw rates in small areas. Relative risks tend to be similar in neighboring areas, and a common approach is to use random-effects models that allow estimation of...
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作者：Berry, SM; Carroll, RJ; Ruppert, D
作者单位：Berry Consultants, LLC; Texas A&M University System; Texas A&M University College Station; Cornell University
摘要：In the presence of covariate measurement error, estimating a regression function nonparametrically is extremely difficult, the problem being related to deconvolution. Various frequentist approaches exist for this problem, but to date there has been no Bayesian treatment. In this article we describe Bayesian approaches to modeling a flexible regression function when the predictor variable is measured with error. The regression function is modeled with smoothing splines and regression P-splines....
