2015

最新文章

• Conditional inferential models: combining information for prior-free probabilistic inference AI翻译
  作者：Martin, Ryan; Liu, Chuanhai
  作者单位：University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; Purdue University System; Purdue University
  摘要：The inferential model (IM) framework provides valid prior-free probabilistic inference by focusing on predicting unobserved auxiliary variables. But, efficient IM-based inference can be challenging when the auxiliary variable is of higher dimension than the parameter. Here we show that features of the auxiliary variable are often fully observed and, in such cases, a simultaneous dimension reduction and information aggregation can be achieved by conditioning. This proposed conditioning strategy...
• Sparse additive regression on a regular lattice AI翻译
  作者：Abramovich, Felix; Lahav, Tal
  作者单位：Tel Aviv University
  摘要：We consider estimation in a sparse additive regression model with the design points on a regular lattice. We establish the minimax convergence rates over Sobolev classes and propose a Fourier-based rate optimal estimator which is adaptive to the unknown sparsity and smoothness of the response function. The estimator is derived within a Bayesian formalism but can be naturally viewed as a penalized maximum likelihood estimator with the complexity penalties on the number of non-zero univariate ad...
• Causal inference from 2K factorial designs by using potential outcomes AI翻译
  作者：Dasgupta, Tirthankar; Pillai, Natesh S.; Rubin, Donald B.
  作者单位：Harvard University
  摘要：A framework for causal inference from two-level factorial designs is proposed, which uses potential outcomes to define causal effects. The paper explores the effect of non-additivity of unit level treatment effects on Neyman's repeated sampling approach for estimation of causal effects and on Fisher's randomization tests on sharp null hypotheses in these designs. The framework allows for statistical inference from a finite population, permits definition and estimation of estimands other than a...
• A convex pseudolikelihood framework for high dimensional partial correlation estimation with convergence guarantees AI翻译
  作者：Khare, Kshitij; Oh, Sang-Yun; Rajaratnam, Bala
  作者单位：State University System of Florida; University of Florida; Stanford University
  摘要：Sparse high dimensional graphical model selection is a topic of much interest in modern day statistics. A popular approach is to apply l(1)-penalties to either parametric likelihoods, or regularized regression/pseudolikelihoods, with the latter having the distinct advantage that they do not explicitly assume Gaussianity. As none of the popular methods proposed for solving pseudolikelihood-based objective functions have provable convergence guarantees, it is not clear whether corresponding esti...
• Determinantal point process models and statistical inference AI翻译
  作者：Lavancier, Frederic; Moller, Jesper; Rubak, Ege
  作者单位：Nantes Universite; Aalborg University
  摘要：Statistical models and methods for determinantal point processes (DPPs) seem largely unexplored. We demonstrate that DPPs provide useful models for the description of spatial point pattern data sets where nearby points repel each other. Such data are usually modelled by Gibbs point processes, where the likelihood and moment expressions are intractable and simulations are time consuming. We exploit the appealing probabilistic properties of DPPs to develop parametric models, where the likelihood...
• Sequential sufficient dimension reduction for large p, small n problems AI翻译
  作者：Yin, Xiangrong; Hilafu, Haileab
  作者单位：University of Kentucky; University of Tennessee System; University of Tennessee Knoxville
  摘要：We propose a new and simple framework for dimension reduction in the large p, small n setting. The framework decomposes the data into pieces, thereby enabling existing approaches for n>p to be adapted to n
• Asymptotic permutation tests in general factorial designs AI翻译
  作者：Pauly, Markus; Brunner, Edgar; Konietschke, Frank
  作者单位：Heinrich Heine University Dusseldorf; University of Gottingen; UNIVERSITY GOTTINGEN HOSPITAL
  摘要：In general factorial designs where no homoscedasticity or a particular error distribution is assumed, the well-known Wald-type statistic is a simple asymptotically valid procedure. However, it is well known that it suffers from a poor finite sample approximation since the convergence to its (2) limit distribution is quite slow. This becomes even worse with an increasing number of factor levels. The aim of the paper is to improve the small sample behaviour of the Wald-type statistic, maintainin...
• Regression analysis of sparse asynchronous longitudinal data AI翻译
  作者：Cao, Hongyuan; Zeng, Donglin; Fine, Jason P.
  作者单位：University of Chicago; University of North Carolina; University of North Carolina Chapel Hill
  摘要：We consider estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent responses and covariates are observed intermittently within subjects. Unlike with synchronous data, where the response and covariates are observed at the same time point, with asynchronous data, the observation times are mismatched. Simple kernel-weighted estimating equations are proposed for generalized linear models with either time invariant or time-dependent coefficients unde...
• Estimation of extreme quantiles for functions of dependent random variables AI翻译
  作者：Gong, Jinguo; Li, Yadong; Peng, Liang; Yao, Qiwei
  作者单位：Southwestern University of Finance & Economics - China; Barclays; University System of Georgia; Georgia State University; University of London; London School Economics & Political Science; Peking University
  摘要：We propose a new method for estimating the extreme quantiles for a function of several dependent random variables. In contrast with the conventional approach based on extreme value theory, we do not impose the condition that the tail of the underlying distribution admits an approximate parametric form, and, furthermore, our estimation makes use of the full observed data. The method proposed is semiparametric as no parametric forms are assumed on the marginal distributions. But we select approp...
• Causal mediation analysis in the multilevel intervention and multicomponent mediator case AI翻译
  作者：Zheng, Cheng; Zhou, Xiao-Hua
  作者单位：University of Washington; University of Washington Seattle; US Department of Veterans Affairs; Veterans Health Administration (VHA); Vet Affairs Puget Sound Health Care System
  摘要：Mediation analysis is an important tool in social and medical sciences as it helps to understand why an intervention works. The commonly used approach, given by Baron and Kenny, requires the strong assumption sequential ignorability' to yield causal interpretation. Ten Have and his colleagues proposed a rank preserving model to relax this assumption. However, the rank preserving model is restricted to the case with binary intervention and single mediator and needs another strong assumption ran...