2期

最新文章

• A Mobius transformation-induced distribution on the torus AI翻译
  作者：Kato, Shogo; Pewsey, Arthur
  作者单位：Research Organization of Information & Systems (ROIS); Institute of Statistical Mathematics (ISM) - Japan; Universidad de Extremadura
  摘要：We propose a five-parameter bivariate wrapped Cauchy distribution as a unimodal model for toroidal data. It is highly tractable, displays numerous desirable properties, including marginal and conditional distributions that are all wrapped Cauchy, and arises as an appealing submodel of a six-parameter distribution obtained by applying Mobius transformation to a pre-existing bivariate circular model. Method of moments and maximum likelihood estimation of its parameters are fast, and tests for in...
• Information-theoretic optimality of observation-driven time series models for continuous responses AI翻译
  作者：Blasques, F.; Koopman, S. J.; Lucas, A.
  作者单位：Vrije Universiteit Amsterdam
  摘要：We investigate information-theoretic optimality properties of the score function of the predictive likelihood as a device for updating a real-valued time-varying parameter in a univariate observation-driven model with continuous responses. We restrict our attention to models with updates of one lag order. The results provide theoretical justification for a class of score-driven models which includes the generalized autoregressive conditional heteroskedasticity model as a special case. Our main...
• On the dependence structure of bivariate recurrent event processes: inference and estimation AI翻译
  作者：Ning, Jing; Chen, Yong; Cai, Chunyan; Huang, Xuelin; Wang, Mei-Cheng
  作者单位：University of Texas System; UTMD Anderson Cancer Center; University of Texas System; University of Texas Health Science Center Houston; University of Texas System; University of Texas Health Science Center Houston; Johns Hopkins University
  摘要：Bivariate or multivariate recurrent event processes are often encountered in longitudinal studies in which more than one type of event is of interest. There has been much research on regression analysis for such data, but little has been done to measure the dependence between recurrent event processes. We propose a time-dependent measure, termed the rate ratio, to assess the local dependence between two types of recurrent event processes. We model the rate ratio as a parametric function of tim...
• On the degrees of freedom of reduced-rank estimators in multivariate regression AI翻译
  作者：Mukherjee, A.; Chen, K.; Wang, N.; Zhu, J.
  作者单位：University of Connecticut; University of Michigan System; University of Michigan
  摘要：We study the effective degrees of freedom of a general class of reduced-rank estimators for multivariate regression in the framework of Stein's unbiased risk estimation. A finite-sample exact unbiased estimator is derived that admits a closed-form expression in terms of the thresholded singular values of the least-squares solution and hence is readily computable. The results continue to hold in the high-dimensional setting where both the predictor and the response dimensions may be larger than...
• Semiparametric exponential families for heavy-tailed data AI翻译
  作者：Fithian, William; Wager, Stefan
  作者单位：Stanford University
  摘要：We propose a semiparametric method for fitting the tail of a heavy-tailed population given a relatively small sample from that population and a larger sample from a related background population. We model the tail of the small sample as an exponential tilt of the better-observed large-sample tail, using a robust sufficient statistic motivated by extreme value theory. In particular, our method induces an estimator of the small-population mean, and we give theoretical and empirical evidence that...
• Hierarchical recognition of sparse patterns in large-scale simultaneous inference AI翻译
  作者：Sun, Wenguang; Wei, Zhi
  作者单位：University of Southern California; New Jersey Institute of Technology
  摘要：We study how to separate signals from noisy data accurately and determine the patterns of the selected signals. Controlling the inflation of false positive errors is important in large-scale simultaneous inference but has not been addressed in the pattern recognition literature. We develop a decision-theoretic framework and formulate the sparse pattern recognition problem as a simultaneous inference problem with multiple decision trees. Oracle and adaptive classifiers are proposed for maximizi...
• Effective dimension reduction for sparse functional data AI翻译
  作者：Yao, F.; Lei, E.; Wu, Y.
  作者单位：University of Toronto; North Carolina State University
  摘要：We propose a method of effective dimension reduction for functional data, emphasizing the sparse design where one observes only a few noisy and irregular measurements for some or all of the subjects. The proposed method borrows strength across the entire sample and provides a way to characterize the effective dimension reduction space, via functional cumulative slicing. Our theoretical study reveals a bias-variance trade-off associated with the regularizing truncation and decaying structures o...
• Envelopes and reduced-rank regression AI翻译
  作者：Cook, R. Dennis; Forzani, Liliana; Zhang, Xin
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; National University of the Littoral; State University System of Florida; Florida State University
  摘要：We incorporate the nascent idea of envelopes (Cook et al., Statist. Sinica 20, 927-1010) into reduced-rank regression by proposing a reduced-rank envelope model, which is a hybrid of reduced-rank and envelope regressions. The proposed model has total number of parameters no more than either of reduced-rank regression or envelope regression. The resulting estimator is at least as efficient as both existing estimators. The methodology of this paper can be adapted to other envelope models, such a...