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作者：Zhu, Hongtu; Ibrahim, Joseph G.; Cho, Hyunsoon
作者单位：University of North Carolina; University of North Carolina Chapel Hill
摘要：Cook's distance [Technometrics 19 (1977) 15-18] is one of the most important diagnostic tools for detecting influential individual or subsets of observations in linear regression for cross-sectional data. However, for many complex data structures (e.g., longitudinal data), no rigorous approach has been developed to address a fundamental issue: deleting subsets with different numbers of observations introduces different degrees of perturbation to the current model fitted to the data, and the ma...
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作者：Chetelat, Didier; Wells, Martin T.
作者单位：Cornell University
摘要：We consider the problem of estimating the mean vector of a p-variate normal (theta, Sigma) distribution under invariant quadratic loss, (delta - theta)'Sigma(-1) (delta - theta), when the covariance is unknown. We propose a new class of estimators that dominate the usual estimator delta(0)(X) = X. The proposed estimators of theta depend upon X and an independent Wishart matrix S with n degrees of freedom, however, S is singular almost surely when p > n. The proof of domination involves the dev...
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作者：Antognini, Alessandro Baldi; Zagoraiou, Maroussa
作者单位：University of Bologna
摘要：The present paper deals with the problem of allocating patients to two competing treatments in the presence of covariates or prognostic factors in order to achieve a good trade-off among ethical concerns, inferential precision and randomness in the treatment allocations. In particular we suggest a multipurpose design methodology that combines efficiency and ethical gain when the linear homoscedastic model with both treatment/covariate interactions and interactions among covariates is adopted. ...
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作者：Adler, Robert J.; Subag, Eliran; Taylor, Jonathan E.
作者单位：Technion Israel Institute of Technology; Stanford University
摘要：We provide a new approach, along with extensions, to results in two important papers of Worsley, Siegmund and coworkers closely tied to the statistical analysis of fMRI (functional magnetic resonance imaging) brain data. These papers studied approximations for the exceedence probabilities of scale and rotation space random fields, the latter playing an important role in the statistical analysis of fMRI data. The techniques used there came either from the Euler characteristic heuristic or via t...
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作者：Ma, Shujie
作者单位：University of California System; University of California Riverside
摘要：We propose a two-step estimating procedure for generalized additive partially linear models with clustered data using estimating equations. Our proposed method applies to the case that the number of observations per cluster is allowed to increase with the number of independent subjects. We establish oracle properties for the two-step estimator of each function component such that it performs as well as the univariate function estimator by assuming that the parametric vector and all other funct...
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作者：Neuvial, Pierre; Roquain, Etienne
作者单位：INRAE; Universite Paris Saclay; Universite Paris Cite; Sorbonne Universite
摘要：We study the properties of false discovery rate (FDR) thresholding, viewed as a classification procedure. The 0-class (null) is assumed to have a known density while the 1-class (alternative) is obtained from the 0-class either by translation or by scaling. Furthermore, the 1-class is assumed to have a small number of elements w.r.t. the 0-class (sparsity). We focus on densities of the Subbotin family, including Gaussian and Laplace models. Nonasymptotic oracle inequalities are derived for the...
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作者：Yang, Min; Stufken, John
作者单位：University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; University System of Georgia; University of Georgia
摘要：We extend the approach in [Ann. Statist. 38 (2010) 2499-2524] for identifying locally optimal designs for nonlinear models. Conceptually the extension is relatively simple, but the consequences in terms of applications are profound. As we will demonstrate, we can obtain results for locally optimal designs under many optimality criteria and for a larger class of models than has been done hitherto. In many cases the results lead to optimal designs with the minimal number of support points.
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作者：Kaiser, Mark S.; Lahiri, Soumendra N.; Nordman, Daniel J.
作者单位：Iowa State University; Texas A&M University System; Texas A&M University College Station
摘要：This paper develops goodness of fit statistics that can be used to formally assess Markov random field models for spatial data, when the model distributions are discrete or continuous and potentially parametric. Test statistics are formed from generalized spatial residuals which are collected over groups of nonneighboring spatial observations, called concliques. Under a hypothesized Markov model structure, spatial residuals within each conclique are shown to be independent and identically dist...
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作者：Morgan, Kari Lock; Rubin, Donald B.
作者单位：Duke University; Harvard University
摘要：Randomized experiments are the gold standard for estimating causal effects, yet often in practice, chance imbalances exist in covariate distributions between treatment groups. If covariate data are available before units are exposed to treatments, these chance imbalances can be mitigated by first checking covariate balance before the physical experiment takes place. Provided a precise definition of imbalance has been specified in advance, unbalanced randomizations can be discarded, followed by...
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作者：Bickel, P. J.; Kleijn, B. J. K.
作者单位：University of California System; University of California Berkeley; University of Amsterdam
摘要：In a smooth semiparametric estimation problem, the marginal posterior for the parameter of interest is expected to be asymptotically normal and satisfy frequentist criteria of optimality if the model is endowed with a suitable prior. It is shown that, under certain straightforward and interpretable conditions, the assertion of Le Cam's acclaimed, but strictly parametric, Bernstein-von Mises theorem [Univ. California Publ. Statist. 1 (1953) 277-329] holds in the semiparametric situation as well...
