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作者：Petersen, Alexander; Mueller, Hans-Georg
作者单位：University of California System; University of California Davis
摘要：Functional data that are nonnegative and have a constrained integral can be considered as samples of one-dimensional density functions. Such data are ubiquitous. Due to the inherent constraints, densities do not live in a vector space and, therefore, commonly used Hilbert space based methods of functional data analysis are not applicable. To address this problem, we introduce a transformation approach, mapping probability densities to a Hilbert space of functions through a continuous and inver...
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作者：Arias-Castro, Ery; Verzelen, Nicolas
作者单位：University of California System; University of California San Diego; INRAE
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作者：Gu, Yuwen; Zou, Hui
作者单位：University of Minnesota System; University of Minnesota Twin Cities
摘要：Asymmetric least squares regression is an important method that has wide applications in statistics, econometrics and finance. The existing work on asymmetric least squares only considers the traditional low dimension and large sample setting. In this paper, we systematically study the Sparse Asymmetric LEast Squares (SALES) regression under high dimensions where the penalty functions include the Lasso and nonconvex penalties. We develop a unified efficient algorithm for fitting SALES and esta...
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作者：Shpitser, Ilya; Tchetgen, Eric Tchetgen
作者单位：Johns Hopkins University; Harvard University; Harvard T.H. Chan School of Public Health
摘要：Identifying causal parameters from observational data is fraught with subtleties due to the issues of selection bias and confounding. In addition, more complex questions of interest, such as effects of treatment on the treated and mediated effects may not always be identified even in data where treatment assignment is known and under investigator control, or may be identified under one causal model but not another. Increasingly complex effects of interest, coupled with a diversity of causal mo...
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作者：Zhao, Tianqi; Cheng, Guang; Liu, Han
作者单位：Princeton University; Purdue University System; Purdue University
摘要：We consider a partially linear framework for modeling massive heterogeneous data. The major goal is to extract common features across all subpopulations while exploring heterogeneity of each subpopulation. In particular, we propose an aggregation type estimator for the commonality parameter that possesses the (nonasymptotic) minimax optimal bound and asymptotic distribution as if there were no heterogeneity. This oracle result holds when the number of subpopulations does not grow too fast. A p...
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作者：Bull, Adam D.
作者单位：University of Cambridge
摘要：In quantitative finance, we often model asset prices as semimartingales, with drift, diffusion and jump components. The jump activity index measures the strength of the jumps at high frequencies, and is of interest both in model selection and fitting, and in volatility estimation. In this paper, we give a novel estimate of the jump activity, together with corresponding confidence intervals. Our estimate improves upon previous work, achieving near-optimal rates of convergence, and good finite-s...
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作者：Dong, Chaohua; Gao, Jiti; Tjostheim, Dag
作者单位：Southwestern University of Finance & Economics - China; Monash University; University of Bergen
摘要：Estimation mainly for two classes of popular models, single-index and partially linear single-index models, is studied in this paper. Such models feature nonstationarity. Orthogonal series expansion is used to approximate the unknown integrable link functions in the models and a profile approach is used to derive the estimators. The findings include the dual rate of convergence of the estimators for the single-index models and a trio of convergence rates for the partially linear single-index m...
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作者：Drees, Holger; Rootzen, Holger
作者单位：University of Hamburg; Chalmers University of Technology; University of Gothenburg
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作者：Yang, Yun; Dunson, David B.
作者单位：University of California System; University of California Berkeley; Duke University
摘要：There is increasing interest in the problem of nonparametric regression with high-dimensional predictors. When the number of predictors D is large, one encounters a daunting problem in attempting to estimate a D-dimensional surface based on limited data. Fortunately, in many applications, the support of the data is concentrated on a d-dimensional subspace with d << D. Manifold learning attempts to estimate this subspace. Our focus is on developing computationally tractable and theoretically su...
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作者：Le, Can M.; Levina, Elizaveta; Vershynin, Roman
作者单位：University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
摘要：Community detection is one of the fundamental problems of network analysis, for which a number of methods have been proposed. Most model-based or criteria-based methods have to solve an optimization problem over a discrete set of labels to find communities, which is computationally infeasible. Some fast spectral algorithms have been proposed for specific methods or models, but only on a case-by-case basis. Here, we propose a general approach for maximizing a function of a network adjacency mat...
