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作者：Kume, Alfred
作者单位：University of Kent; University of Nottingham
摘要：A method is developed for fitting smooth curves through a series of shapes of landmarks in two dimensions using unrolling and unwrapping procedures in Riemannian manifolds. An explicit method of calculation is given which is analogous to that of Jupp & Kent ( 1987) for spherical data. The resulting splines are called shape-space smoothing splines. The method resembles that of fitting smoothing splines in real spaces in that, if the smoothing parameter is zero, the resulting curve interpolates ...
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作者：Bartolucci, Francesco; Pennoni, Fulvia
作者单位：University of Perugia; University of Milan
摘要：Following Cox & Wermuth ( 1994, 2002), we show that the distribution of a set of binary observable variables, induced by a certain discrete latent variable model, may be approximated by a quadratic exponential distribution. This discrete latent variable model is equivalent to the latent-class version of the two-parameter logistic model of Birnbaum ( 1968), which may be seen as a generalized version of the Rasch model ( Rasch, 1960, 1961). On the basis of this result, we develop an approximate ...
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作者：Cook, Dennis; Li, Bing; Chiaromonte, Francesca
作者单位：University of Minnesota System; University of Minnesota Twin Cities; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：Regressions in which the fixed number of predictors p exceeds the number of independent observational units n occur in a variety of scientific fields. Sufficient dimension reduction provides a promising approach to such problems, by restricting attention to d < n linear combinations of the original p predictors. However, standard methods of sufficient dimension reduction require inversion of the sample predictor covariance matrix. We propose a method for estimating the central subspace that el...
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作者：Li, Lexin
作者单位：North Carolina State University
摘要：Existing sufficient dimension reduction methods suffer from the fact that each dimension reduction component is a linear combination of all the original predictors, so that it is difficult to interpret the resulting estimates. We propose a unified estimation strategy, which combines a regression-type formulation of sufficient dimension reduction methods and shrinkage estimation, to produce sparse and accurate solutions. The method can be applied to most existing sufficient dimension reduction ...
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作者：Cox, D. R.
作者单位：University of Oxford
摘要：A relationship due to W. G. Cochran showing the effect on least squares regression coefficients of marginalizing over or conditioning on an explanatory variable is generalized to quantile regression coefficients. The condition under which conditioning does not induce interaction or effect reversal is shown. Examples are given. The discussion is simplest when all variables are continuous; the extension to discrete variables is outlined.
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作者：Carvalho, Carlos M.; Massam, Helene; West, Mike
作者单位：Duke University; York University - Canada
摘要：We introduce and exemplify an efficient method for direct sampling from hyper-inverse Wishart distributions. The method relies very naturally on the use of standard junction-tree representation of graphs, and couples these with matrix results for inverse Wishart distributions. We describe the theory and resulting computational algorithms for both decomposable and nondecomposable graphical models. An example drawn from financial time series demonstrates application in a context where inferences...
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作者：Prendergast, Luke A.
作者单位：La Trobe University
摘要：Sliced inverse regression, sliced inverse regression II and sliced average variance estimation are three related dimension-reduction methods that require relatively mild model assumptions. As an approximation for the relative influence of single observations from large samples, the influence function is used to compare the sensitivity of the three methods to particular observational types. The analysis carried out here helps to explain why there is a lack of agreement concerning the preferabil...
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作者：Wang, Hansheng; Li, Runze; Tsai, Chih-Ling
作者单位：Peking University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of California System; University of California Davis
摘要：The penalized least squares approach with smoothly clipped absolute deviation penalty has been consistently demonstrated to be an attractive regression shrinkage and selection method. It not only automatically and consistently selects the important variables, but also produces estimators which are as efficient as the oracle estimator. However, these attractive features depend on appropriate choice of the tuning parameter. We show that the commonly used generalized crossvalidation cannot select...
