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作者：Hu, Lixia; Huang, Tao; You, Jinhong
作者单位：Shanghai Lixin University of Accounting & Finance; Shanghai University of Finance & Economics
摘要：The additive model and the varying-coefficient model are both powerful regression tools, with wide practical applications. However, our empirical study on a financial data has shown that both of these models have drawbacks when applied to locally stationary time series. For the analysis of functional data, Zhang and Wang have proposed a flexible regression method, called the varying-coefficient additive model (VCAM), and presented a two-step spline estimation method. Motivated by their approac...
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作者：Liu, Zhonghua; Lin, Xihong
作者单位：University of Hong Kong; Harvard University; Harvard T.H. Chan School of Public Health
摘要：Joint analysis of multiple phenotypes can increase statistical power in genetic association studies. Principal component analysis, as a popular dimension reduction method, especially when the number of phenotypes is high dimensional, has been proposed to analyze multiple correlated phenotypes. It has been empirically observed that the first PC, which summarizes the largest amount of variance, can be less powerful than higher-order PCs and other commonly used methods in detecting genetic associ...
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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：Giannone, Domenico; Lenza, Michele; Primiceri, Giorgio E.
作者单位：Federal Reserve System - USA; Federal Reserve Bank - New York; Centre for Economic Policy Research - UK; European Central Bank; Northwestern University; National Bureau of Economic Research
摘要：We propose a class of prior distributions that discipline the long-run behavior of vector autoregressions (VARs). These priors can be naturally elicited using economic theory, which provides guidance on the joint dynamics of macroeconomic time series in the long run. Our priors for the long run are conjugate, and can thus be easily implemented using dummy observations and combined with other popular priors. In VARs with standard macroeconomic variables, a prior based on the long-run prediction...
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作者：Mak, Simon; Wu, C. F. Jeff
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作者：Steingrimsson, Jon Arni; Diao, Liqun; Strawderman, Robert L.
作者单位：Brown University; University of Waterloo; University of Rochester
摘要：This article proposes a novel paradigm for building regression trees and ensemble learning in survival analysis. Generalizations of the classification and regression trees (CART) and random forests (RF) algorithms for general loss functions, and in the latter case more general bootstrap procedures, are both introduced. These results, in combination with an extension of the theory of censoring unbiased transformations (CUTs) applicable to loss functions, underpin the development of two new clas...
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作者：Wang, Haiying; Zhu, Rong; Ma, Ping
作者单位：University System of Georgia; University of Georgia
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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...
