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作者：Lahiri, P; Larsen, MD
作者单位：University System of Maryland; University of Maryland College Park; Iowa State University
摘要：Record linkage, or exact matching, can be used to join together two files that contain information on the same individuals but lack unique personal identification codes. The possibility of errors in linkage causes problems for estimating the relationships between variables on the two files. The effect is analogous to the impact of measurement error. A model of a linear regression relationship between variables in linked files is proposed. Assuming the probabilities that pairs of records are li...
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作者：Craig, BA
作者单位：Purdue University System; Purdue University
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作者：Benjamini, Y; Yekutieli, D
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作者：Zhang, JT
作者单位：National University of Singapore
摘要：In this article we study how to approximate a random variable T of general chi-squared-type mixtures by a random variable of the form alpha chi(2)(d) + beta via matching the first three cumulants. The approximation error bounds for the density functions of the chi-squared approximation and the normal approximation are established. Applications of the results to some nonparametric goodness-of-fit tests, including those tests based on orthogonal series, smoothing splines, and local polynomial sm...
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作者：Yuan, M; Lin, Y
作者单位：University System of Georgia; Georgia Institute of Technology; University of Wisconsin System; University of Wisconsin Madison
摘要：We propose an empirical Bayes method for variable selection and coefficient estimation in linear regression models. The method is based on a particular hierarchical Bayes formulation, and the empirical Bayes estimator is shown to be closely related to the LASSO estimator. Such a connection allows us to take advantage of the recently developed quick LASSO algorithm to compute the empirical Bayes estimate, and provides a new way to select the tuning parameter in the LASSO method. Unlike previous...
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作者：Staudenmayer, J; Buonaccorsi, JR
作者单位：University of Massachusetts System; University of Massachusetts Amherst
摘要：Time series data are often subject to measurement error, usually the result of needing to estimate the variable of interest. Although it is often reasonable to assume that the measurement error is additive (i.e., the estimator is conditionally unbiased for the missing true value), the measurement error variances often vary as a result of changes in the population/process over time and/or changes in sampling effort. In this article we address estimation of the parameters in linear autoregressiv...
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作者：Edwards, D
作者单位：University of South Carolina System; University of South Carolina Columbia
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作者：He, XM; Fung, WK; Zhu, ZY
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Hong Kong; East China Normal University
摘要：In this article we consider robust generalized estimating equations for the analysis of semiparametric generalized partial linear models (GPLMs) for longitudinal data or clustered data in general. We approximate the nonparametric function in the GPLM by a regression spline, and use bounded scores and leverage-based weights in the estimating equation to achieve robustness against outliers. We show that the regression spline approach avoids some of the intricacies associated with the profile-ker...
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作者：Louis, TA
作者单位：Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health
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作者：Efron, B
作者单位：Stanford University
摘要：Broadly speaking, nineteenth century statistics was Bayesian, while the twentieth century was frequentist, at least from the point of view of most scientific practitioners. Here in the twenty-first century scientists are bringing statisticians much bigger problems to solve, often comprising millions of data points and thousands of parameters. Which statistical philosophy will dominate practice? My guess, backed up with some recent examples, is that a combination of Bayesian and frequentist ide...
