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作者：Qian, Peter Z. G.; Wu, C. F. Jeff
作者单位：University of Wisconsin System; University of Wisconsin Madison; University System of Georgia; Georgia Institute of Technology
摘要：We propose an approach to constructing a new type of design, a sliced space-filling design, intended for computer experiments with qualitative and quantitative factors. The approach starts with constructing a Latin hypercube design based on a special orthogonal array for the quantitative factors and then partitions the design into groups corresponding to different level combinations of the qualitative factors. The points in each group have good space-filling properties. Some illustrative examp...
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作者：Qian, Peter Z. G.
作者单位：University of Wisconsin System; University of Wisconsin Madison
摘要：We propose an approach to constructing nested Latin hypercube designs. Such designs are useful for conducting multiple computer experiments with different levels of accuracy. A nested Latin hypercube design with two layers is defined to be a special Latin hypercube design that contains a smaller Latin hypercube design as a subset. Our method is easy to implement and can accommodate any number of factors. We also extend this method to construct nested Latin hypercube designs with more than two ...
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作者：Carvalho, C. M.; Scott, J. G.
作者单位：University of Chicago; Duke University
摘要：This paper presents a default model-selection procedure for Gaussian graphical models that involves two new developments. First, we develop a default version of the hyper-inverse Wishart prior for restricted covariance matrices, called the hyper-inverse Wishart g-prior, and show how it corresponds to the implied fractional prior for selecting a graph using fractional Bayes factors. Second, we apply a class of priors that automatically handles the problem of multiple hypothesis testing. We demo...
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作者：James, Gareth M.; Radchenko, Peter
作者单位：University of Southern California
摘要：The Dantzig selector performs variable selection and model fitting in linear regression. It uses an L-1 penalty to shrink the regression coefficients towards zero, in a similar fashion to the lasso. While both the lasso and Dantzig selector potentially do a good job of selecting the correct variables, they tend to overshrink the final coefficients. This results in an unfortunate trade-off. One can either select a high shrinkage tuning parameter that produces an accurate model but poor coeffici...
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作者：Guan, Yongtao
作者单位：Yale University
摘要：We introduce two new variance estimation procedures that use non-overlapping and overlapping blocks, respectively. The non-overlapping blocks estimator can be viewed as the limit of the thinned block bootstrap estimator recently proposed in Guan Loh (2007), by letting the number of thinned processes and bootstrap samples therein both increase to infinity. The non-overlapping blocks estimator can be obtained quickly since it does not require any thinning or bootstrap steps, and it is more stabl...
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作者：Kim, Jae Kwang; Rao, J. N. K.
作者单位：Iowa State University; Carleton University
摘要：Variance estimation after imputation is an important practical problem in survey sampling. When deterministic imputation or stochastic imputation is used, we show that the variance of the imputed estimator can be consistently estimated by a unifying linearize and reverse approach. We provide some applications of the approach to regression imputation, fractional categorical imputation, multiple imputation and composite imputation. Results from a simulation study, under a factorial structure for...
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作者：Kosmidis, Ioannis; Firth, David
作者单位：University of Warwick
摘要：In Firth (1993, Biometrika) it was shown how the leading term in the asymptotic bias of the maximum likelihood estimator is removed by adjusting the score vector, and that in canonical-link generalized linear models the method is equivalent to maximizing a penalized likelihood that is easily implemented via iterative adjustment of the data. Here a more general family of bias-reducing adjustments is developed for a broad class of univariate and multivariate generalized nonlinear models. The res...
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作者：Goga, C.; Deville, J-C.; Ruiz-Gazen, A.
作者单位：Universite Bourgogne Europe; Ecole Nationale de la Statistique et de l'Analyse de l'Information (ENSAI); Universite de Toulouse; Universite Toulouse 1 Capitole; Toulouse School of Economics
摘要：An important problem associated with two-sample surveys is the estimation of nonlinear functions of finite population totals such as ratios, correlation coefficients or measures of income inequality. Computation and estimation of the variance of such complex statistics are made more difficult by the existence of overlapping units. In one-sample surveys, the linearization method based on the influence function approach is a powerful tool for variance estimation. We introduce a two-sample linear...
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作者：Li, P.; Chen, J.; Marriott, P.
作者单位：University of Alberta; University of British Columbia; University of Waterloo
摘要：Even simple examples of finite mixture models can fail to fulfil the regularity conditions that are routinely assumed in standard parametric inference problems. Many methods have been investigated for testing for homogeneity in finite mixture models, for example, but all rely on regularity conditions including the finiteness of the Fisher information and the space of the mixing parameter being a compact subset of some Euclidean space. Very simple examples where such assumptions fail include mi...
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作者：Hall, Peter; Robinson, Andrew P.
作者单位：University of Melbourne
摘要：One of the attractions of crossvalidation, as a tool for smoothing-parameter choice, is its applicability to a wide variety of estimator types and contexts. However, its detractors comment adversely on the relatively high variance of crossvalidatory smoothing parameters, noting that this compromises the performance of the estimators in which those parameters are used. We show that the variability can be reduced simply, significantly and reliably by employing bootstrap aggregation or bagging. W...
