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作者：QIN, J
摘要：It is well known that we can use the likelihood ratio statistic to test hypotheses and to construct confidence intervals in full parametric models. Recently, Owen introduced the empirical likelihood method in nonparametric models. In this paper, we generalize his results to biased sample problems. A Wilks theorem leading to a likelihood ratio confidence interval for the mean is given. Some extensions, discussion and simulations are presented.
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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：SHAO, J
摘要：In statistical applications the unknown parameter of interest can frequently be defined as a functional theta = T(F), where F is an unknown population. Statistical inferences about 0 are usually made based on the statistic T(F(n)), where F(n) is the empirical distribution. Assessing T(F(n)) (as an estimator of theta) or making large sample inferences usually requires a consistent estimator of the asymptotic variance of T(F(n)). Asymptotic behaviour of the jackknife variance estimator is closel...
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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：KIM, JH
摘要：We consider general chi-square goodness-of-fit test statistics for randomly censored data-call these generalized Pearson statistics-which are nonnegative definite quadratic forms in the cell frequencies obtained from the product-limit estimator, allowing random cells and general estimators of nuisance parameters. This class of statistics generalizes the class studied by Moore and Spruill in the no censoring case. The large sample behavior of these statistics under the null hypothesis and local...
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作者：STUTE, W
摘要：In the left-truncation model, one observes data (X(i), Y(i)) only when Y(i) less-than-or-equal-to X(i). Let F denote the marginal d.f. of X(i), the variable of interest. The nonparametric MLE F(n) of F aims at reconstructing F from truncated data. In this paper an almost sure representation of F(n). is derived with improved error bounds on the one hand and under weaker distributional assumptions on the other hand.
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作者：LESLIE, J; VANEEDEN, C
作者单位：University of London; Birkbeck University London; University of British Columbia; University of Quebec; University of Quebec Montreal
摘要：Dufour gives a conjecture concerning a characterization of the exponential distribution based on type 2 right censored samples. This conjecture, if true, generalizes the characterization based on complete samples of Seshadri, Csorgo and Stephens (1969) and Dufour, Maag and van Eeden (1984). In this paper it is shown that Dufour's conjecture is true if the number of censored observations is no larger than (1/3)n - 1, where n is the sample size. The result has implications for testing fit of cen...
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作者：ZHANG, P
摘要：A natural extension of the simple leave-one-out cross validation (CV) method is to allow the deletion of more than one observations. In this article, several notions of the multifold cross validation (MCV) method have been discussed. In the context of variable selection under a linear regression model, we show that the delete-d MCV criterion is asymptotically equivalent to the well known FPE criterion. Two computationally more feasible methods, the r-fold cross validation and the repeated lear...
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作者：BOLTHAUSEN, E; GOTZE, F
作者单位：University of Bielefeld
摘要：A Berry-Esseen theorem for the rate of convergence of general nonlinear multivariate sampling statistics with normal limit distribution is derived via a multivariate extension of Stein's method. The result generalizes in particular Previous results of Bolthausen for one-dimensional linear rank statistics, one-dimensional results of van Zwet and Friedrich for general functions of independent random elements and provides convergence bounds for general multivariate sampling statistics without res...
