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作者：MARTIN, RD; ZAMAR, RH
作者单位：University of British Columbia
摘要：In this paper we consider the problem of robust estimation of the scale of the location residuals when the underlying distribution of the data belongs to a contamination neighborhood of a parametric location-scale family. We define the class of M-estimates of scale with general location, and show that under certain regularity assumptions, these scale estimates converge to their asymptotic functionals uniformly with respect to the underlying distribution, and with respect to the M-estimate defi...
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作者：BJERVE, S; DOKSUM, K
作者单位：University of California System; University of California Berkeley
摘要：For.experiments where the strength of association between a response variable Y and a covariate X is different over different regions of values for the covariate X, we propose local nonparametric dependence functions which measure the strength of association between Y and X as a function of X = x. Our dependence functions are extensions of Galton's idea of strength of co-relation from the bivariate normal case to the nonparametric case. In particular, a dependence function is obtained by expre...
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作者：KHMALADZE, EV
作者单位：Russian Academy of Sciences; Steklov Mathematical Institute of the Russian Academy of Sciences
摘要：This paper is mainly devoted to the following statistical problem: in the case of random variables of any finite dimension and both simple or parametric hypotheses, how to construct convenient ''empirical'' processes which could provide the basis for goodness of fit tests-more or less in the same way as the uniform empirical process does in the case of simple hypothesis and scalar random variables. The solution of this problem is connected here with the theory of multiparameter martingales and...
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作者：KUNSCH, H; BERAN, J; HAMPEL, F
作者单位：University of Zurich; Texas A&M University System; Texas A&M University College Station
摘要：The background of the paper is the empirical observation from a variety of subject areas that long-range correlations appear to be much more frequent than has been previously assumed. This includes high-quality measurement series which are commonly treated as prototypes of ''i.i.d.'' observations. Evidence is briefly cited in the paper. It has already been shown elsewhere that long-range dependence leads to results that can be qualitatively different from those obtained under short-range depen...
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作者：FAN, JQ
摘要：In this paper, a method for finding global minimax lower bounds is introduced. The idea is to adjust automatically the direction of a local one-dimensional subproblem at each location to the nearly hardest one, and to use locally the difficulty of the one-dimensional subproblem. This method has the advantages of being easily implemented and understood. The lower bound is then applied to nonparametric deconvolution to obtain the optimal rates of convergence for estimating a whole function. Othe...
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作者：CHEN, JH; SHAO, J
作者单位：University of Ottawa
摘要：In a heteroscedastic linear model, we establish the asymptotic normality of the iterative weighted least squares estimators with weights constructed by using the within-group residuals obtained from the previous model fitting. An adaptive procedure is proposed which ensures that the iterative process stops after a finite number of iterations and produces an estimator asymptotically equivalent to the best estimator that can be obtained by using the iterative procedure. Theoretical and empirical...
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作者：HOSOYA, Y; TANIGUCHI, M
作者单位：University of Osaka
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作者：ANDERSON, TW
摘要：The spectral distribution function of a stationary stochastic process standardized by dividing by the variance of the process is a linear function of the autocorrelations. The integral of the sample standardized spectral density (periodogram) is a similar linear function of the autocorrelations. As the sample size increases, the difference of these two functions multiplied by the square root of the sample size converges weakly to a Gaussian stochastic process with a continuous time parameter. ...
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作者：YU, B
摘要：This paper investigates the density estimation problem in the L(infinity) norm for dependent data. It is shown that the iid optimal minimax rates are also optimal for smooth classes of stationary sequences satisfying certain beta-mixing (or absolutely regular) conditions. Moreover, for given beta-mixing coefficients, bounds on uniform convergence rates of kernel estimators are computed in terms of the mixing coefficients. The rates and the bounds obtained are not only for estimating the densit...
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作者：HORVATH, L
摘要：We compute the asymptotic distribution of the maximum likelihood ratio test when we want to check whether the parameters of normal observations have changed at an unknown point. The proof is based on the limit distribution of the largest deviation between a d-dimensional Ornstein-Uhlenbeck process and the origin.
