The Annals of Statistics

最新文章

• A simple smooth backfitting method for additive models AI翻译
  作者：Mammen, Enno; Park, Byeong U.
  作者单位：University of Mannheim; Seoul National University (SNU)
  摘要：In this paper a new smooth backfitting estimate is proposed for additive regression models. The estimate has the simple structure of Nadaraya-Watson smooth backfitting but at the same time achieves the oracle property of local linear smooth backfitting. Each component is estimated with the same asymptotic accuracy as if the other components were known.
• A multivariate empirical Bayes statistic for replicated microarray time course data AI翻译
  作者：Tai, Yu Chuan; Speed, Terence P.
  作者单位：University of California System; University of California Berkeley
  摘要：In this paper we derive one- and two-sample multivariate empirical Bayes statistics (the MB-statistics) to rank genes in order of interest from longitudinal replicated developmental microarray time course experiments. We first use conjugate priors to develop our one-sample multivariate empirical Bayes framework for the null hypothesis that the expected temporal profile stays at 0. This leads to our one-sample MB-statistic and a one-sample T-2-statistic, a variant of the one-sample Hotelling T-...
• Local partial-likelihood estimation for lifetime data AI翻译
  作者：Fan, Jianqing; Lin, Huazhen; Zhou, Yong
  作者单位：Chinese University of Hong Kong; Princeton University; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Sichuan University
  摘要：This paper considers a proportional hazards model, which allows one to examine the extent to which covariates interact nonlinearly with an exposure variable, for analysis of lifetime data. A local partial-likelihood technique is proposed to estimate nonlinear interactions. Asymptotic normality of the proposed estimator is established. The baseline hazard function, the bias and the variance of the local likelihood estimator are consistently estimated. In addition, a one-step local partial-likel...
• A general asymptotic scheme for inference under order restrictions AI翻译
  作者：Anevski, D.; Hossjer, O.
  作者单位：Chalmers University of Technology; University of Gothenburg; Lund University
  摘要：Limit distributions for the greatest convex minorant and its derivative are considered for a general class of stochastic processes including partial sum processes and empirical processes, for independent, weakly dependent and long range dependent data. The results are applied to isotonic regression, isotonic regression after kernel smoothing, estimation of convex regression functions, and estimation of monotone and convex density functions. Various pointwise limit distributions are obtained, a...
• Discussion of equi-energy sampler by Kou, Zhou and Wong AI翻译
  作者：Chen, Ming-Hui; Kim, Sungduk
  作者单位：University of Connecticut
• On the Benjamini-Hochberg method AI翻译
  作者：Ferreira, J. A.; Zwinderman, A. H.
  作者单位：University of Amsterdam; Academic Medical Center Amsterdam
  摘要：We investigate the properties of the Benjamini-Hochberg method for multiple testing and of a variant of Storey's generalization of it, extending and complementing the asymptotic and exact results available in the literature. Results are obtained under two different sets of assumptions and include asymptotic and exact expressions and bounds for the proportion of rejections, the proportion of incorrect rejections out of all rejections and two other proportions used to quantify the efficacy of th...
• Estimation in semiparametric spatial regression AI翻译
  作者：Gao, Jiti; Lu, Zudi; Tjostheim, Dag
  作者单位：University of Western Australia; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; University of London; London School Economics & Political Science; University of Bergen
  摘要：Nonparametric methods have been very popular in the last couple of decades in time series and regression, but no such development has taken place for spatial models. A rather obvious reason for this is the curse of dimensionality. For spatial data on a grid evaluating the conditional mean given its closest neighbors requires a four-dimensional nonparametric regression. In this paper a serniparametric spatial regression approach is proposed to avoid this problem. An estimation procedure based o...
• Discussion:: Local Rademacher complexities and oracle inequalities in risk minimization AI翻译
  作者：Clemencon, Stephan; Lugosi, Gabor; Vayatis, Nicolas
  作者单位：Universite Paris Nanterre; ICREA; Pompeu Fabra University; Pompeu Fabra University; Universite Paris Cite; Sorbonne Universite
• Asymptotic minimaxity of false discovery rate thresholding for sparse exponential data AI翻译
  作者：Donoho, David; Jin, Jiashun
  作者单位：Stanford University; Purdue University System; Purdue University
  摘要：We apply FDR thresholding to a non-Gaussian vector whose coordinates X-i, i = 1,..., n, are independent exponential with individual means mu(i). The vector mu = (mu(i)) is thought to be sparse, with most coordinates 1 but a small fraction significantly larger than 1; roughly, most coordinates are simply 'noise,' but a small fraction contain 'signal.' We measure risk by percoordinate mean-squared error in recovering log(mu(i)), and study minimax estimation over parameter spaces defined by const...
• Quasi-maximum-likelihood estimation in conditionally heteroscedastic time series: A stochastic recurrence equations approach AI翻译
  作者：Straumann, Daniel; Mikosch, Thomas
  作者单位：University of Copenhagen
  摘要：This paper studies the quasi-maximum-likelihood estimator (QMLE) in a general conditionally heteroscedastic time series model of multiplicative form X-t = sigma(t)Z(t), where the unobservable volatility sigma(t) is a parametric function of (Xt-1,..., Xt-p, sigma(t-1),..., sigma(t-q)) for some p, q >= 0, and (Z(t)) is standardized i.i.d. noise. We assume that these models are solutions to stochastic recurrence equations which satisfy a contraction (random Lipschitz coefficient) property. These ...