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作者：Li, Quefeng; Li, Lexin
作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of California System; University of California Berkeley
摘要：Multiple types of data measured on a common set of subjects arise in many areas. Numerous empirical studies have found that integrative analysis of such data can result in better statistical performance in terms of prediction and feature selection. However, the advantages of integrative analysis have mostly been demonstrated empirically. In the context of two-class classification, we propose an integrative linear discriminant analysis method and establish a theoretical guarantee that it achiev...
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作者：Weihs, L.; Drton, M.; Meinshausen, N.
作者单位：University of Washington; University of Washington Seattle; Swiss Federal Institutes of Technology Domain; ETH Zurich
摘要：The need to test whether two random vectors are independent has spawned many competing measures of dependence. We focus on nonparametric measures that are invariant under strictly increasing transformations, such as Kendall's tau, Hoeffding's D, and the Bergsma-Dassios sign covariance. Each exhibits symmetries that are not readily apparent from their definitions. Making these symmetries explicit, we define a new class of multivariate nonparametric measures of dependence that we call symmetric ...
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作者：Cao, Yuanpei; Lin, Wei; Li, Hongzhe
作者单位：University of Pennsylvania; Peking University
摘要：Compositional data are ubiquitous in many scientific endeavours. Motivated by microbiome and metagenomic research, we consider a two-sample testing problem for high-dimensional compositional data and formulate a testable hypothesis of compositional equivalence for the means of two latent log basis vectors. We propose a test through the centred log-ratio transformation of the compositions. The asymptotic null distribution of the test statistic is derived and its power against sparse alternative...
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作者：Cronie, O.; van Lieshout, M. N. M.
作者单位：Umea University; University of Twente
摘要：We propose a new bandwidth selection method for kernel estimators of spatial point process intensity functions. The method is based on an optimality criterion motivated by the Campbell formula applied to the reciprocal intensity function. The new method is fully nonparametric, does not require knowledge of higher-order moments, and is not restricted to a specific class of point process. Our approach is computationally straightforward and does not require numerical approximation of integrals.
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作者：Kong, Xin-Bing; Xu, Shao-Jun; Zhou, Wang
作者单位：Nanjing Audit University; Shanghai University of Finance & Economics; National University of Singapore
摘要：Volatility functionals are widely used in financial econometrics. In the literature, they are estimated with realized volatility functionals using high-frequency data. In this paper we introduce a nonparametric local bootstrap method that resamples the high-frequency returns with replacement in local windows shrinking to zero. While the block bootstrap in time series (Hall et al., 1995) aims to reduce correlation, the local bootstrap is intended to eliminate the heterogeneity of volatility. We...
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作者：Mukherjee, A.; Chen, K.; Wang, N.; Zhu, J.
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作者：Proietti, Tommaso; Giovannelli, Alessandro
作者单位：University of Rome Tor Vergata
摘要：The autocovariance matrix of a stationary random process plays a central role in prediction theory and time series analysis. When the dimension of the matrix is of the same order of magnitude as the number of observations, the sample autocovariance matrix gives an inconsistent estimator. In the nonparametric framework, recent proposals have concentrated on banding and tapering the sample autocovariance matrix. We introduce an alternative approach via a modified Durbin-Levinson algorithm that r...
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作者：Zhang, Xinyu; Chiou, Jeng-Min; Ma, Yanyuan
作者单位：Chinese Academy of Sciences; Academia Sinica - Taiwan; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：Prediction is often the primary goal of data analysis. In this work, we propose a novel model averaging approach to the prediction of a functional response variable. We develop a crossvalidation model averaging estimator based on functional linear regression models in which the response and the covariate are both treated as random functions. We show that the weights chosen by the method are asymptotically optimal in the sense that the squared error loss of the predicted function is as small as...
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作者：Frazier, D. T.; Martin, G. M.; Robert, C. P.; Rousseau, J.
作者单位：Monash University; Universite PSL; Universite Paris-Dauphine; University of Oxford
摘要：Approximate Bayesian computation allows for statistical analysis using models with intractable likelihoods. In this paper we consider the asymptotic behaviour of the posterior distribution obtained by this method. We give general results on the rate at which the posterior distribution concentrates on sets containing the true parameter, the limiting shape of the posterior distribution, and the asymptotic distribution of the posterior mean. These results hold under given rates for the tolerance ...
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作者：Shively, T. S.; Walker, S. G.
作者单位：University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin
摘要：We show that the Bayes factor for testing whether a subset of coefficients are zero in the normal linear regression model gives the uniformly most powerful test amongst the class of invariant tests discussed in Lehmann & Romano (2005) if the prior distributions for the regression coefficients are in a specific class of distributions. The priors in this class can have any elliptical distribution, with a specific scale matrix, for the subset of coefficients that are being tested. We also show un...
