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作者：Genovese, CR
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作者：Gustafson, P
作者单位：University of British Columbia
摘要：There have been many recent suggestions as to how to build and estimate flexible Bayesian regression models, using constructs such as trees, neural networks, and Gaussian processes. Although there is much to commend these methods, their implementation and interpretation can be daunting for practitioners. This article presents a spline-based methodology for flexible Bayesian regression that is quite simple in terms of computation and interpretation. Smooth bivariate interactions are modeled in ...
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作者：Duncan, GT; Mukherjee, S
作者单位：Carnegie Mellon University; Carnegie Mellon University; Nova Southeastern University
摘要：Disclosure limitation methods transform statistical databases to protect confidentiality, a practical concern of statistical agencies. A statistical database responds to queries with aggregate statistics. The database administrator should maximize legitimate data access while keeping the risk of disclosure below an acceptable level. Legitimate users seek statistical information, generally in aggregate form; malicious users-the data snoopers-attempt to infer confidential information about an in...
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作者：Stoumbos, ZG; Reynolds, MR Jr; Ryan, TP; Woodall, WH
作者单位：Rutgers University System; Rutgers University New Brunswick; Rutgers University Newark; Rutgers University System; Rutgers University Newark; Rutgers University New Brunswick; Virginia Polytechnic Institute & State University; Virginia Polytechnic Institute & State University; University of Michigan System; University of Michigan
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作者：Lawless, J
作者单位：University of Waterloo
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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：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...
