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作者：Chen, YG; Diaconis, P; Holmes, SR; Liu, JS
作者单位：Duke University; Stanford University; Harvard University; Harvard University
摘要：We describe a sequential importance sampling (SIS) procedure for analyzing two-way zero-one or contingency tables with fixed marginal sums. An essential feature of the new method is that it samples the columns of the table progressively according to certain special distributions. Our method produces Monte Carlo samples that are remarkably close to the uniform distribution, enabling one to approximate closely the null distributions of various test statistics about these tables. Our method compa...
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作者：Peng, RD; Schoenberg, FP; Woods, JA
作者单位：University of California System; University of California Los Angeles; California State University System; California State University Long Beach
摘要：Numerical indices are commonly used as tools to aid wildfire management and hazard assessment. Although the use of such indices is widespread, assessment of these indices in their respective regions of application is rare. We evaluate the effectiveness of the burning index (BI) for predicting wildfire occurrences in Los Angeles County, California using space-time point-process models. These models are based on an additive decomposition of the conditional intensity, with separate terms used to ...
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作者：Dukic, VM; Peña, EA
作者单位：University of Chicago; University of South Carolina System; University of South Carolina Columbia
摘要：This article considers the problem of estimating the dispersion parameter in a Gaussian model that is intermediate between a model where the mean parameter is fully known (fixed) and a model where the mean parameter is completely unknown. One of the goals is to understand the implications of the two-step process of first selecting a model among a finite number of submodels, then estimating a parameter of interest after the model selection, but using the same sample data. The estimators are cla...
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作者：Tamhane, AC
作者单位：Northwestern University
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作者：Chen, SN; Dahl, GB; Khan, S
作者单位：Hong Kong University of Science & Technology; University of Rochester
摘要：In this article we consider identification and estimation of a censored nonparametric location scale-model. We first show that in the case where the location function is strictly less than the (fixed) censoring point for all values in the support of the explanatory variables, the location function is not identified anywhere. In contrast, when the location function is greater or equal to the censoring point with positive probability, the location function is identified on the entire support, in...
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作者：Ibrahim, JG; Chen, MH; Lipsitz, SR; Herring, AH
作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of Connecticut; Medical University of South Carolina
摘要：Missing data is a major issue in many applied problems, especially in the biomedical sciences. We review four common approaches for inference in generalized linear models (GLMs) with missing covariate data: maximum likelihood (ML), multiple imputation (MI), fully Bayesian (FB), and weighted estimating equations (WEEs). There is considerable interest in how these four methodologies are related, the properties of each approach, the advantages and disadvantages of each methodology, and computatio...
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作者：Lou, XY; Casella, G; Todhunter, RJ; Yang, MCK; Wu, RL
作者单位：State University System of Florida; University of Florida; Cornell University; Zhejiang A&F University
摘要：The nonrandom association between different genes, termed linkage disequilibrium (LD), provides a powerful tool for high-resolution mapping of quantitative trait loci (QTL) underlying complex traits. This LD-based mapping approach can be made more efficient when it is coupled with interval mapping characterizing the genetic distance between markers and QTL. This article describes a general statistical framework for simultaneously estimating the linkage and LD that are related in a two-stage hi...
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作者：Peña, D; Sánchez, I
摘要：This article presents a new procedure for multifold predictive validation in time series. The procedure is based on the so-called filtered residuals, in-sample prediction errors evaluated in such a way that they are similar to out-of-sample ones. The filtered residuals are obtained from parameters estimated by eliminating from the estimation process the estimated innovations at the points to be predicted. Thus, instead of using the deletion of observations to validate the predictions, as in cl...
