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作者：Mallik, A.; Sen, B.; Banerjee, M.; Michailidis, G.
作者单位：University of Michigan System; University of Michigan; Columbia University
摘要：We use p-values to identify the threshold level at which a regression function leaves its baseline value, a problem motivated by applications in toxicological and pharmacological dose-response studies and environmental statistics. We study the problem in two sampling settings: one where multiple responses can be obtained at a number of different covariate levels, and the other the standard regression setting involving limited number of response values at each covariate. Our procedure involves ...
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作者：Belloni, A.; Chernozhukov, V.; Wang, L.
作者单位：Duke University; Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT)
摘要：We propose a pivotal method for estimating high-dimensional sparse linear regression models, where the overall number of regressors p is large, possibly much larger than n, but only s regressors are significant. The method is a modification of the lasso, called the square-root lasso. The method is pivotal in that it neither relies on the knowledge of the standard deviation Sigma nor does it need to pre-estimate Sigma. Moreover, the method does not rely on normality or sub-Gaussianity of noise....
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作者：Siegmund, D. O.; Zhang, N. R.; Yakir, B.
作者单位：Stanford University; Hebrew University of Jerusalem
摘要：The false discovery rate is a criterion for controlling Type I error in simultaneous testing of multiple hypotheses. For scanning statistics, due to local dependence, clusters of neighbouring hypotheses are likely to be rejected together. In such situations, it is more intuitive and informative to group neighbouring rejections together and count them as a single discovery, with the false discovery rate defined as the proportion of clusters that are falsely declared among all declared clusters....
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作者：Leng, Chenlei; Li, Bo
作者单位：National University of Singapore; Tsinghua University
摘要：We propose a simple forward adaptive banding method for estimating large covariance matrices using the modified Cholesky decomposition. This approach requires the fitting of a prespecified set of models due to the adaptive banding structure and can be efficiently implemented. Aside from its computational attractiveness, we propose a novel Bayes information criterion that gives consistent model selection for estimating high dimensional covariance matrices. The method compares favourably to its ...
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作者：Feng, Xingdong; He, Xuming; Hu, Jianhua
作者单位：Shanghai University of Finance & Economics; University of Michigan System; University of Michigan; University of Texas System; UTMD Anderson Cancer Center
摘要：The existing theory of the wild bootstrap has focused on linear estimators. In this note, we broaden its validity by providing a class of weight distributions that is asymptotically valid for quantile regression estimators. As most weight distributions in the literature lead to biased variance estimates for nonlinear estimators of linear regression, we propose a modification of the wild bootstrap that admits a broader class of weight distributions for quantile regression. A simulation study on...
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作者：Drton, Mathias; Williams, Benjamin
作者单位：University of Chicago; University of Chicago
摘要：When testing geometrically irregular parametric hypotheses, the bootstrap is an intuitively appealing method to circumvent difficult distribution theory. It has been shown, however, that the usual bootstrap is inconsistent in estimating the asymptotic distributions involved in such problems. This paper is concerned with the asymptotic size of likelihood ratio tests when critical values are computed using the inconsistent bootstrap. We clarify how the asymptotic size of such a test can be obtai...
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作者：De Luna, Xavier; Waernbaum, Ingeborg; Richardson, Thomas S.
作者单位：Umea University; University of Washington; University of Washington Seattle
摘要：Observational studies in which the effect of a nonrandomized treatment on an outcome of interest is estimated are common in domains such as labour economics and epidemiology. Such studies often rely on an assumption of unconfounded treatment when controlling for a given set of observed pre-treatment covariates. The choice of covariates to control in order to guarantee unconfoundedness should primarily be based on subject matter theories, although the latter typically give only partial guidance...
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作者：Huang, Chunfeng; Hsing, Tailen; Cressie, Noel
作者单位：Indiana University System; Indiana University Bloomington; University of Michigan System; University of Michigan; University System of Ohio; Ohio State University
摘要：In the study of intrinsically stationary spatial processes, a new nonparametric variogram estimator is proposed through its spectral representation. The methodology is based on estimation of the variogram's spectrum by solving a regularized inverse problem through quadratic programming. The estimated variogram is guaranteed to be conditionally negative-definite. Simulation shows that our estimator is flexible and generally has smaller mean integrated squared error than the parametric estimator...
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作者：Skinner, C. J.; D'arrigo, J.
作者单位：University of London; London School Economics & Political Science; University of Southampton
摘要：Correlated nonresponse within clusters arises in certain survey settings. It is often represented by a random effects model and assumed to be cluster-specific nonignorable, in the sense that survey and nonresponse outcomes are conditionally independent given cluster-level random effects. Two basic forms of inverse probability weights are considered: response propensity weights based on a marginal model, and weights based on predicted random effects. It is shown that both approaches can lead to...
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作者：Tang, Cheng Yong; Leng, Chenlei
作者单位：National University of Singapore
摘要：We propose a novel quantile regression approach for longitudinal data analysis which naturally incorporates auxiliary information from the conditional mean model to account for within-subject correlations. The efficiency gain is quantified theoretically and demonstrated empirically via simulation studies and the analysis of a real dataset.
