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作者：Lloyd, CJ; Jones, MC
作者单位：Open University - UK
摘要：We present a kernel estimator for the density of a Variable when sampling probabilities depend on that Variable. Both the density and sampling bias weight functions are unknown and are estimated nonparametrically;. To achieve this; the method requires that two independent samples be taken from a fixed finite population. An estimator of population size follows simply from our density estimator. Asymptotic bias and standard errors for these estimators are provided, and the methodology is illustr...
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作者：Mugglin, AS; Carlin, BP; Gelfand, AE
作者单位：University System of Ohio; Ohio State University; University of Minnesota System; University of Minnesota Twin Cities; University of Connecticut
摘要：We consider inference using multivariate data that are spatially misaligned; that is, involving variables (typically counts or rates) that are aggregated over differing sets of regional boundaries. Geographic information systems enable the simultaneous display of such datasets, but their current capabilities are essentially only descriptive, not inferential. We describe a hierarchical modeling approach that provides a natural solution to this problem through its ability to sensibly combine inf...
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作者：Royall, R
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作者：Worsley, KJ
作者单位：McGill University
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作者：Natarajan, R; Kass, RE
作者单位：State University System of Florida; University of Florida; Carnegie Mellon University
摘要：Bayesian methods furnish an attractive approach to inference in generalized linear mixed models. In the absence of subjective prior information for the random-effect variance components, these analyses are typically conducted using either the standard invariant prior for normal responses or diffuse conjugate priors. Previous work has pointed out serious difficulties with both strategies, and we show here that as in normal mixed models, the standard invariant prior leads to an improper posterio...
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作者：Cappé, O; Robert, CP
作者单位：Universite PSL; Universite Paris-Dauphine; Institut Polytechnique de Paris; ENSAE Paris
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作者：Rubin, DB; Thomas, N
作者单位：Harvard University; Bristol-Myers Squibb
摘要：Propensity score matching refers to a class of multivariate methods used in comparative studies to construct treated and matched control samples that have similar distributions on many covariates. This matching is the observational study analog of randomization in ideal experiments, but is far less complete as it can only balance the distribution of observed covariates, whereas randomization balances the distribution of all covariates, both observed and unobserved. An important feature of prop...
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作者：Weinberg, JM; Lagakos, SW
作者单位：Boston University; Harvard University; Harvard T.H. Chan School of Public Health
摘要：Tests based on the permutation of observations are a common and attractive method of comparing two groups of outcomes. in part because they retain proper test size with minimal assumptions and can have high efficiency toward specific alternatives of interest. In addition, permutation tests may be used with discrete or categorical outcomes, for which linear rank tests are not designed. Permutation tests are now increasingly used to analyze discrete or continuous responses that themselves are fu...
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作者：Black, DA; Berger, MC; Scott, FA
作者单位：Syracuse University; University of Kentucky; University of Kentucky
摘要：The bias introduced by errors in the measurement of independent variables has increasingly been a topic of interest among researchers estimating economic parameters. However, studies typically use the assumption of classical measurement error; that is, the variable of interest and its measurement error are uncorrelated, End the expected value of the mismeasured variable is equal to the expected value of the true measure. These assumptions often arise from convenience rather than conviction. Wh...
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作者：Kim, JT
摘要：The classical problem of assessing the goodness of tit of a postulated parametric distribution is investigated using techniques from nonparametric density estimation. A new test is proposed based on the data-selected order of a Fourier series density estimator. This test has the novel feature of providing an associated nonparametric estimator that can be used to estimate the unknown density when the null hypothesis is rejected. The limiting null distribution of the proposed test statistic is d...
