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作者：Nygren, Kjell; Nygren, Lan Ma
作者单位：Rider University
摘要：We introduce likelihood subgradient densities and explore their basic properties. Using mixtures of likelihood subgradient densities, we propose an approach for constructing tight enveloping functions in the Bayesian context. In the case of normal priors with normal data, the area underneath the resulting enveloping function is bounded above by 2/root pi approximate to 1.128. The approach is extended to k-dimensional models where the corresponding bound is (2/root pi)(k). More generally, our a...
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作者：Kaciroti, Niko A.; Raghunathan, Trivellore E.; Schork, M. Anthony; Clark, Noreen M.; Gong, Molly
作者单位：University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
摘要：Asthma, a chronic inflammatory disease of the airways, affects an estimated 6.3 million children under age 18 in the United States. A key to successful asthma management, and hence improved quality of life (QOL), calls for an active partnership between asthma patients and their health care providers. To foster this partnership, an intervention program was designed and evaluated using a randomized longitudinal study. The study focused on several outcomes where typically missing data remained a ...
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作者：Han, D; Tsung, FG
作者单位：Shanghai Jiao Tong University; Hong Kong University of Science & Technology
摘要：To detect and estimate nonconstant, time-varying mean shifts, statistical process control (SPC) tools, such as the cumulative score (Cuscore) and generalized likelihood ratio test (GLRT) charts, have recently been proposed. However, their efficiency is based on previous and exact knowledge of a reference pattern. In this article a reference-free Cuscore (RFCuscore) chart is proposed that can trace and detect dynamic mean changes quickly without knowing the reference pattern. In addition, a uni...
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作者：Zou, Hui
作者单位：University of Minnesota System; University of Minnesota Twin Cities
摘要：The lasso is a popular technique for simultaneous estimation and variable selection. Lasso variable selection has been shown to be consistent under certain conditions. In this work we derive a necessary condition for the lasso variable selection to be consistent. Consequently, there exist certain scenarios where the lasso is inconsistent for variable selection. We then propose a new version of the lasso, called the adaptive lasso, where adaptive weights are used for penalizing different coeffi...
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作者：Goldstein, Michael; Rougier, Jonathan
摘要：A calibration-based approach is developed for predicting the behavior of a physical system that is modeled by a computer simulator. The approach is based on Bayes linear adjustment using both system observations and evaluations of the simulator at parameterizations that appear to give good matches to those observations. This approach can be applied to complex high-dimensional systems with expensive simulators, where a fully Bayesian approach would be impractical. It is illustrated with an exam...
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作者：Kulldorff, Martin
作者单位：Harvard University; Harvard Medical School; Harvard Pilgrim Health Care
摘要：In many applications, it is of interest to test whether a spatial point pattern is randomly generated after adjusting for an underlying spatial inhomogeneity. A great variety of different test statistics have been proposed for this purpose by scientists in different fields; these are reviewed in this article. Despite apparent dissimilarities in terms of their original formulations, most of these statistics can be placed into one general framework of which they are special cases. This makes it ...
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作者：Kabaila, Paul; Leeb, Hannes
作者单位：La Trobe University; Yale University
摘要：We give a large-sample analysis of the minimal coverage probability of the usual confidence intervals for regression parameters when the underlying model is chosen by a conservative (or overconsistent) model selection procedure. We derive an upper bound for the large-sample limit minimal coverage probability of such intervals that applies to a large class of model selection procedures including the Akaike information criterion as well as various pretesting procedures. This upper bound can be u...
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作者：Aban, IB; Meerschaert, MM; Panorska, AK
作者单位：University of Alabama System; University of Alabama Birmingham; University of Otago; Nevada System of Higher Education (NSHE); University of Nevada Reno
摘要：The Pareto distribution is a simple model for nonnegative data with a power law probability tail. In many practical applications, there is a natural upper bound that truncates the probability tail. This article derives estimators for the truncated Pareto distribution, investigates their properties, and illustrates a way to check for fit. These methods are illustrated with applications from finance, hydrology, and atmospheric science.
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作者：Bair, E; Hastie, T; Paul, D; Tibshirani, R
作者单位：University of California System; University of California San Francisco; Stanford University; Stanford University
摘要：In regression problems where the number of predictors greatly exceeds the number of observations, conventional regression techniques may produce unsatisfactory results. We describe a technique called supervised principal components that call be applied to this type of problem. Supervised principal components is similar to conventional principal components analysis except that it uses a subset of the predictors selected based on their association with the outcome. Supervised principal component...
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作者：Chen, JH; Huo, XM
作者单位：University System of Georgia; Georgia Institute of Technology
摘要：We consider the length of the longest significance run in a (two-dimensional) Bernoulli net and derive its asymptotic limit distribution. Our theoretical results: (1) reliabilityresults can be considered as generalizations of known theorems in significance runs. We give three types of t style lower and upper bounds, (2) Erdos-Renyi law, and (3) the asymptotic limit distribution. To understand the rate of convergence to the asymptotic distributions, we carry out numerical simulations. The conve...
