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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...
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作者：Chen, Ming-Hui; Kim, Sungduk
作者单位：University of Connecticut
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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...
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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...
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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
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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...
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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 ...
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作者：Inoue, Akihiko; Kasahara, Yukio
作者单位：Hokkaido University
摘要：We consider the finite-past predictor coefficients of stationary time series, and establish an explicit representation for them, in terms of the MA and AR coefficients. The proof is based on the alternate applications of projection operators associated with the infinite past and the infinite future. Applying the result to long memory processes, we give the rate of convergence of the finite predictor coefficients and prove an inequality of Baxter-type.
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作者：van de Geer, Sara
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
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作者：Lahiri, S. N.; Zhu, Jun
作者单位：Iowa State University; University of Wisconsin System; University of Wisconsin Madison
摘要：In this paper we consider the problem of bootstrapping a class of spatial regression models when the sampling sites are generated by a (possibly nonuniform) stochastic design and are irregularly spaced. It is shown that the natural extension of the existing block bootstrap methods for grid spatial data does not work for irregularly spaced spatial data under nonuniform stochastic designs. A variant of the blocking mechanism is proposed. It is shown that the proposed block bootstrap method provi...
