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作者：Forastiere, Laura; Mattei, Alessandra; Ding, Peng
作者单位：University of Florence; University of California System; University of California Berkeley
摘要：In causal mediation analysis, the definitions of the natural direct and indirect effects involve potential outcomes that can never be observed, so-called a priori counterfactuals. This conceptual challenge translates into issues in identification, which requires strong and often unverifiable assumptions, including sequential ignorability. Alternatively, we can deal with post-treatment variables using the principal stratification framework, where causal effects are defined as comparisons of obs...
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作者：Lee, S. M. S.; Wu, Y.
作者单位：University of Hong Kong; University of Waterloo
摘要：We propose a general bootstrap recipe for estimating the distributions of post-model-selection least squares estimators under a linear regression model. The recipe constrains residual bootstrapping within the most parsimonious, approximately correct, models to yield a distribution estimator which is consistent provided any wrong candidate model is sufficiently separated from the approximately correct ones. Our theory applies to a broad class of model selection methods based on information crit...
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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...
