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作者：Patterson, BH; Dayton, CM; Graubard, B
作者单位：National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); University System of Maryland; University of Maryland College Park; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics
摘要：High fruit and vegetable intake is associated with decreased cancer risk. However, dietary recall data from national surveys suggest that, on any given day, intake falls below the recommended minima of three daily servings of vegetables and two daily servings of fruit. There is no single widely accepted measure of usual intake. One approach is to regard the distribution of intake as a mixture of regular (relatively frequent) and nonregular (relatively infrequent) consumers, using an indicator ...
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作者：Meyer, MC; Laud, PW
作者单位：University System of Georgia; University of Georgia; Medical College of Wisconsin
摘要：Here we extend predictive method for model selection of Laud and Ibrahim to the generalized linear model. This prescription avoids the need to directly specify prior probabilities of models and prior densities for the parameters. Instead, a prior prediction for the response induces the required priors. We propose normal and conjugate priors for generalized linear models, each using a single prior prediction for the mean response to induce suitable priors for each variable-subset model. In this...
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作者：Liang, F
作者单位：National University of Singapore
摘要：This article describes a new Monte Carlo algorithm, dynamically weighted importance sampling (DWIS), for simulation and optimization. In DWIS, the state of the Markov chain is augmented to a population. At each iteration, the population is subject to two move steps, dynamic weighting and population control. These steps ensure that DWIS can move across energy barriers like dynamic weighting, but with the weights well controlled and with a finite expectation. The estimates can converge much fast...
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作者：Barsky, R; Bound, J; Charles, KK; Lupton, JP
作者单位：University of Michigan System; University of Michigan; Federal Reserve System - USA; Federal Reserve System Board of Governors
摘要：Many applications involve a decomposition of the mean intergroup difference in a given variable into the portion attributable to differences in the distribution of one or more explanatory variables and that due to differences in the conditional expectation function. This article notes two interrelated reasons why the Blinder-Oaxaca (B-O) method-the approach most commonly used in the literature-may yield misleading results. We suggest a natural solution that both provides a more reliable answer...
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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：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：Inoue, LYT; Parmigiani, G
作者单位：University of Texas System; UTMD Anderson Cancer Center; Johns Hopkins University; Johns Hopkins University; Johns Hopkins University
摘要：Studies of time-to-event are often conducted using follow-up sessions with subjects at risk. When these sessions must be widely spaced, their timing can significantly affect the efficiency of a study design. In this article we analyze the optimal timing of follow-up from a Bayesian decision theoretic standpoint. The article has two goals: (1) to develop the necessary distributional theory and computational approaches to determine optimal sequential and nonsequential follow-up schedules in the ...
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作者：Saltelli, A; Tarantola, S
作者单位：European Commission Joint Research Centre; EC JRC ISPRA Site
摘要：This article deals with global quantitative sensitivity analysis of the Level E model, a computer code used in safety assessment for nuclear waste disposal. The Level E code has been the Subject of two international benchmarks of risk assessment codes and Monte Carlo methods and is well known in the literature. We discuss the Level E model with reference to two different settings. In the first setting, the objective is to find the input factor that drives most of the output variance. In the se...
