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作者：Cai, Tony; Liu, Weidong; Luo, Xi
作者单位：University of Pennsylvania; Shanghai Jiao Tong University
摘要：This article proposes a constrained l(1) minimization method for estimating a sparse inverse covariance matrix based on a sample of n iid p-variate random variables. The resulting estimator is shown to have a number of desirable properties. In particular, the rate of convergence between the estimator and the true s-sparse precision matrix under the spectral norm is s root logp/n when the population distribution has either exponential-type tails or polynomial-type tails. We present convergence ...
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作者：Ding, Peng; Geng, Zhi; Yan, Wei; Zhou, Xiao-Hua
作者单位：University of Washington; University of Washington Seattle; Peking University; Peking University; US Department of Veterans Affairs; Veterans Health Administration (VHA); Vet Affairs Puget Sound Health Care System
摘要：In medical studies, there are many situations where the final outcomes are truncated by death, in which patients die before outcomes of interest are measured. In this article we consider identifiability and estimation of causal effects by principal stratification when some outcomes are truncated by death. Previous studies mostly focused on large sample bounds, Bayesian analysis, sensitivity analysis. In this article, we propose a new method for identifying the causal parameter of interest unde...
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作者：Laber, Eric B.; Murphy, Susan A.
作者单位：University of Michigan System; University of Michigan
摘要：The estimated test error of a learned classifier is the most commonly reported measure of classifier performance. However, constructing a high-quality point estimator of the test error has proved to be very difficult. Furthermore, common interval estimators (e.g., confidence intervals) are based on the point estimator of the test error and thus inherit all the difficulties associated with the point estimation problem. As a result, these confidence intervals do not reliably deliver nominal cove...
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作者：She, Yiyuan; Owen, Art B.
作者单位：State University System of Florida; Florida State University; Stanford University
摘要：This article studies the outlier detection problem from the standpoint of penalized regression. In the regression model, we add one mean shift parameter for each of the n data points. We then apply a regularization favoring a sparse vector of mean shift parameters. The usual L-1 penalty yields a convex criterion, but fails to deliver a robust estimator. The L-1 penalty corresponds to soft thresholding. We introduce a thresholding (denoted by Theta) based iterative procedure for outlier detecti...
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作者：Van Aelst, Stefan; Willems, Gert
作者单位：Ghent University
摘要：We propose robust tests as alternatives to the classical Wilks' Lambda test in one-way MANOVA. The robust tests use highly robust and efficient multisample multivariate S-estimators or MM-estimators instead of the empirical covariances. The properties of several robust test statistics are compared. Under the null hypothesis, the distribution of the test statistics is proportional to a chi-square distribution. As an alternative to the asymptotic distribution, we develop a fast robust bootstrap ...
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作者：Schwartz, Scott L.; Li, Fan; Mealli, Fabrizia
作者单位：Texas A&M University System; Texas A&M University College Station; Texas A&M AgriLife Research; Duke University; University of Florence
摘要：In causal inference studies, treatment comparisons often need to be adjusted for confounded post-treatment variables. Principal stratification (PS) is a framework to deal with such variables within the potential outcome approach to causal inference. Continuous intermediate variables introduce inferential challenges to PS analysis. Existing methods either dichotomize the intermediate variable, or assume a fully parametric model for the joint distribution of the potential intermediate variables....
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作者：Rosenbaum, Paul R.
作者单位：University of Pennsylvania
摘要：An observational study or nonrandomized experiment has exact evidence factors if it permits several statistically independent tests of the same null hypothesis Ho of no treatment effect, where these several tests depend upon different assumptions about bias from nonrandom treatment assignment. In an observational study, we are typically uncertain about what assumptions truly describe treatment assignment. If independent tests each reject Ho, and if each of these tests is valid under assumption...
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作者：Crainiceanu, Ciprian M.; Caffo, Brian S.; Luo, Sheng; Zipunnikov, Vadim M.; Punjabi, Naresh M.
作者单位：Johns Hopkins University; University of Texas System; University of Texas Health Science Center Houston; University of Texas School Public Health; Johns Hopkins University
摘要：Images, often stored in multidimensional arrays, are fast becoming ubiquitous in medical and public health research. Analyzing populations of images is a statistical problem that raises a host of daunting challenges. The most significant challenge is the massive size of the datasets incorporating images recorded for hundreds or thousands of subjects at multiple visits. We introduce the population value decomposition (PVD), a general method for simultaneous dimensionality reduction of large pop...
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作者：Lin, Dongyu; Foster, Dean P.; Ungar, Lyle H.
作者单位：University of Pennsylvania; University of Pennsylvania; University of Pennsylvania
摘要：We propose a fast and accurate algorithm. VIF regression, for doing feature selection in large regression problems. VIP regression is extremely fast: it uses a one-pass search over the predictors and a computationally efficient method of testing each potential predictor for addition to the model. VIE regression provably avoids model overfitting, controlling the marginal false discovery rate. Numerical results show that it is much faster than any other published algorithm for regression with fe...
