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作者：Andersen, Torben G.; Thyrsgaard, Martin; Todorov, Viktor
作者单位：Northwestern University; National Bureau of Economic Research; Aarhus University; CREATES
摘要：We develop a nonparametric test for whether return volatility exhibits time-varying intraday periodicity using a long time series of high-frequency data. Our null hypothesis, commonly adopted in work on volatility modeling, is that volatility follows a stationary process combined with a constant time-of-day periodic component. We construct time-of-day volatility estimates and studentize the high-frequency returns with these periodic components. If the intraday periodicity is invariant, then th...
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作者：Jeng, X. Jessie; Zhang, Teng; Tzeng, Jung-Ying
作者单位：North Carolina State University; National Taiwan University; National Cheng Kung University
摘要：This article addresses the challenge of efficiently capturing a high proportion of true signals for subsequent data analyses when sample sizes are relatively limited with respect to data dimension. We propose the signal missing rate (SMR) as a new measure for false-negative control to account for the variability of false-negative proportion. Novel data-adaptive procedures are developed to control SMR without incurring many unnecessary false positives under dependence. We justify the efficiency...
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作者：Kong, Efang; Xia, Yingcun; Zhong, Wei
作者单位：University of Electronic Science & Technology of China; National University of Singapore; Xiamen University; Xiamen University
摘要：In this article, we propose to measure the dependence between two random variables through a composite coefficient of determination (CCD) of a set of nonparametric regressions. These regressions take consecutive binarizations of one variable as the response and the other variable as the predictor. The resulting measure is invariant to monotonic marginal variable transformation, rendering it robust against heavy-tailed distributions and outliers, and convenient for independent testing. Estimati...
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作者：Fan, Jianqing; Ke, Yuan; Sun, Qiang; Zhou, Wen-Xin
作者单位：Fudan University; Princeton University; University System of Georgia; University of Georgia; University of Toronto; University of California System; University of California San Diego
摘要：Large-scale multiple testing with correlated and heavy-tailed data arises in a wide range of research areas from genomics, medical imaging to finance. Conventional methods for estimating the false discovery proportion (FDP) often ignore the effect of heavy-tailedness and the dependence structure among test statistics, and thus may lead to inefficient or even inconsistent estimation. Also, the commonly imposed joint normality assumption is arguably too stringent for many applications. To addres...
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作者：Scealy, J. L.; Wood, Andrew T. A.
作者单位：Australian National University; University of Nottingham
摘要：We propose a new distribution for analyzing paleomagnetic directional data, that is, a novel transformation of the von Mises-Fisher distribution. The new distribution has ellipse-like symmetry, as does the Kent distribution; however, unlike the Kent distribution the normalizing constant in the new density is easy to compute and estimation of the shape parameters is straightforward. To accommodate outliers, the model also incorporates an additional shape parameter, which controls the tail-weigh...
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作者：Pena, Daniel; Smucler, Ezequiel; Yohai, Victor J.
作者单位：Universidad Carlos III de Madrid; Universidad Carlos III de Madrid; Universidad Torcuato Di Tella; University of Buenos Aires; University of Buenos Aires; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET)
摘要：We define one-sided dynamic principal components (ODPC) for time series as linear combinations of the present and past values of the series that minimize the reconstruction mean squared error. Usually dynamic principal components have been defined as functions of past and future values of the series and therefore they are not appropriate for forecasting purposes. On the contrary, it is shown that the ODPC introduced in this article can be successfully used for forecasting high-dimensional mult...
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作者：Wong, Kin Yau; Zeng, Donglin; Lin, D. Y.
作者单位：Hong Kong Polytechnic University; University of North Carolina; University of North Carolina Chapel Hill
摘要：Analysis of genomic data is often complicated by the presence of missing values, which may arise due to cost or other reasons. The prevailing approach of single imputation is generally invalid if the imputation model is misspecified. In this article, we propose a robust score statistic based on imputed data for testing the association between a phenotype and a genomic variable with (partially) missing values. We fit a semiparametric regression model for the genomic variable against an arbitrar...
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作者：Zhang, Anru; Han, Rungang
作者单位：University of Wisconsin System; University of Wisconsin Madison
摘要：In this article, we consider the sparse tensor singular value decomposition, which aims for dimension reduction on high-dimensional high-order data with certain sparsity structure. A method named sparse tensor alternating thresholding for singular value decomposition (STAT-SVD) is proposed. The proposed procedure features a novel double projection & thresholding scheme, which provides a sharp criterion for thresholding in each iteration. Compared with regular tensor SVD model, STAT-SVD permits...
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作者：Youngman, Benjamin D.
作者单位：University of Exeter
摘要：Generalized additive model (GAM) forms offer a flexible approach to capturing marginal variation. Such forms are used here to represent distributional variation in extreme values and presented in terms of spatio-temporal variation, which is often evident in environmental processes. A two-stage procedure is proposed that identifies extreme values as exceedances of a high threshold, which is defined as a fixed quantile and estimated by quantile regression. Excesses of the threshold are modelled ...
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作者：Li, Xinran; Din, Peng; Lin, Qian; Yan, Dawei; Liu, Jun S.
作者单位：Harvard University; University of California System; University of California Berkeley; Tsinghua University; Peking University; Peking University
摘要：Many previous causal inference studies require no interference, that is, the potential outcomes of a unit do not depend on the treatments of other units. However, this no-interference assumption becomes unreasonable when a unit interacts with other units in the same group or cluster. In a motivating application, a top Chinese university admits students through two channels: the college entrance exam (also known as Gaokao) and recommendation (often based on Olympiads in various subjects). The u...
