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作者：Bi, Xuan; Feng, Long; Li, Cai; Zhang, Heping
作者单位：University of Minnesota System; University of Minnesota Twin Cities; City University of Hong Kong; St Jude Children's Research Hospital; Yale University
摘要：The polycystic ovary syndrome (PCOS) is a most common cause of infertility among women of reproductive age. Unfortunately, the etiology of PCOS is poorly understood. Large-scale clinical trials for pregnancy in polycystic ovary syndrome (PPCOS) were conducted to evaluate the effectiveness of treatments. Ovulation, pregnancy, and live birth are three sequentially nested binary outcomes, typically analyzed separately. However, the separate models may lose power in detecting the treatment effects...
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作者：Ren, Haojie; Zou, Changliang; Chen, Nan; Li, Runze
作者单位：Shanghai Jiao Tong University; Nankai University; Nankai University; National University of Singapore; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：Monitoring large-scale datastreams with limited resources has become increasingly important for real-time detection of abnormal activities in many applications. Despite the availability of large datasets, the challenges associated with designing an efficient change-detection when clustering or spatial pattern exists are not yet well addressed. In this article, a design-adaptive testing procedure is developed when only a limited number of streaming observations can be accessed at each time. We ...
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作者：Fan, Jianqing; Guo, Jianhua; Zheng, Shurong
作者单位：Princeton University; Northeast Normal University - China; Northeast Normal University - China
摘要：Determining the number of common factors is an important and practical topic in high-dimensional factor models. The existing literature is mainly based on the eigenvalues of the covariance matrix. Owing to the incomparability of the eigenvalues of the covariance matrix caused by the heterogeneous scales of the observed variables, it is not easy to find an accurate relationship between these eigenvalues and the number of common factors. To overcome this limitation, we appeal to the correlation ...
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作者：Fan, Jianqing; Masini, Ricardo; Medeiros, Marcelo C.
作者单位：Princeton University; Princeton University; Getulio Vargas Foundation; Pontificia Universidade Catolica do Rio de Janeiro
摘要：Optimal pricing, that is determining the price level that maximizes profit or revenue of a given product, is a vital task for the retail industry. To select such a quantity, one needs first to estimate the price elasticity from the product demand. Regression methods usually fail to recover such elasticities due to confounding effects and price endogeneity. Therefore, randomized experiments are typically required. However, elasticities can be highly heterogeneous depending on the location of st...
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作者：Hallin, M.; La Vecchia, D.; Liu, H.
作者单位：Universite Libre de Bruxelles; University of Geneva; Lancaster University
摘要：We propose a new class of R-estimators for semiparametric VARMA models in which the innovation density plays the role of the nuisance parameter. Our estimators are based on the novel concepts of multivariate center-outward ranks and signs. We show that these concepts, combined with Le Cam's asymptotic theory of statistical experiments, yield a class of semiparametric estimation procedures, which are efficient (at a given reference density), root-n consistent, and asymptotically normal under a ...
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作者：Rabinowicz, Assaf; Rosset, Saharon
作者单位：Tel Aviv University
摘要：K-fold cross-validation (CV) with squared error loss is widely used for evaluating predictive models, especially when strong distributional assumptions cannot be taken. However, CV with squared error loss is not free from distributional assumptions, in particular in cases involving non-iid data. This article analyzes CV for correlated data. We present a criterion for suitability of standard CV in presence of correlations. When this criterion does not hold, we introduce a bias corrected CV esti...
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作者：Zhang, Yan Dora; Naughton, Brian P.; Bondell, Howard D.; Reich, Brian J.
作者单位：University of Hong Kong; North Carolina State University; University of Melbourne
摘要：Prior distributions for high-dimensional linear regression require specifying a joint distribution for the unobserved regression coefficients, which is inherently difficult. We instead propose a new class of shrinkage priors for linear regression via specifying a prior first on the model fit, in particular, the coefficient of determination, and then distributing through to the coefficients in a novel way. The proposed method compares favorably to previous approaches in terms of both concentrat...
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作者：Gu, Jiaying; Koenker, Roger
作者单位：University of Toronto; University of London; University College London
摘要：The venerable method of maximum likelihood has found numerous recent applications innonparametricestimation of regression and shape constrained densities. For mixture models the nonparametric maximum likelihood estimator (NPMLE) of Kiefer and Wolfowitz plays a central role in recent developments of empirical Bayes methods. The NPMLE has also been proposed by Cosslett as an estimation method for single index linear models for binary response with random coefficients. However, computational diff...
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作者：Brown, D. Andrew; McMahan, Christopher S.; Shinohara, Russell T.; Linn, Kristin A.
作者单位：Clemson University; University of Pennsylvania; University of Pennsylvania
摘要：Alzheimer's disease is a neurodegenerative condition that accelerates cognitive decline relative to normal aging. It is of critical scientific importance to gain a better understanding of early disease mechanisms in the brain to facilitate effective, targeted therapies. The volume of the hippocampus is often used in diagnosis and monitoring of the disease. Measuring this volume via neuroimaging is difficult since each hippocampus must either be manually identified or automatically delineated, ...
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作者：Jochmans, Koen
作者单位：University of Cambridge
摘要：We consider inference in linear regression models that is robust to heteroscedasticity and the presence of many control variables. When the number of control variables increases at the same rate as the sample size the usual heteroscedasticity-robust estimators of the covariance matrix are inconsistent. Hence, tests based on these estimators are size distorted even in large samples. An alternative covariance-matrix estimator for such a setting is presented that complements recent work by Cattan...
