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作者：LAI, TL; YING, ZL
作者单位：University of Illinois System; University of Illinois Urbana-Champaign
摘要：Buckley and James proposed an extension of the classical least squares estimator to the censored regression model. It has been found in some empirical and Monte Carlo studies that their approach provides satisfactory results and seems to be superior to other extensions of the least squares estimator in the literature. To develop a complete asymptotic theory for this approach, we introduce herein a slight modification of the Buckley-James estimator to get around the difficulties caused by the i...
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作者：MARDEN, JI
摘要：We introduce new criteria for evaluating test statistics based on the p-values of the statistics. Given a set of test statistics, a good statistic is one which is robust in being reasonably sensitive to all departures from the null implied by that set. We present a constructive approach to finding the optimal statistic. We apply the criteria to two-sided problems; combining independent tests; testing that the mean of a spherical normal distribution is 0, and extensions to other spherically sym...
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作者：MENENDEZ, JA; SALVADOR, B
摘要：The first anomaly in the L.R.T. for testing restricted hypotheses was observed by Warrack and Robertson. They found the L.R.T. for testing an order restriction in a normal model to be dominated by a different test. In this paper we deal with a more general situation in which the L.R.T. for testing a face of an acute cone is dominated by a different test that does not take into account some of the information in the model.
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作者：YU, QQ; CHOW, MS
作者单位：Northeastern University; Sun Yat Sen University
摘要：Consider the problem of continuous invariant estimation of a distribution function with the weighted Cramer-von Mises loss. The minimaxity of the empirical distribution function, which is also the best invariant estimator, is proved for any sample size. This solves a long-standing conjecture.
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作者：BUJA, A; DUFFY, D; HASTIE, T; TIBSHIRANI, R
作者单位：Nokia Corporation; Nokia Bell Labs; AT&T; University of Toronto
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作者：ZHANG, P
摘要：In a nonparametric regression setup where the covariates are continuous, the problem of estimating the number of covariates will be discussed in this paper. The kernel method is used to estimate the regression function and the selection criterion is based on minimizing the cross-validation estimate of the mean squared prediction error. We consider choosing both the bandwidth and the number of covariates based on the data. Unlike the case of linear regression, it turns out that the selection is...
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作者：BROWN, LD; LOW, MG
作者单位：University of California System; University of California Berkeley
摘要：This paper compares three methods for producing lower bounds on the minimax risk under quadratic loss. The first uses the bounds from Brown and Gajek. The second method also uses the information inequality and results in bounds which are always at least as good as those form the first method. The third method is the hardest-linear-family method described by Donoho and Liu. These methods are applied in four examples, the last of which relates to a frequently considered problem in nonparametric ...
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作者：EATON, ML; TYLER, DE
作者单位：Rutgers University System; Rutgers University New Brunswick
摘要：A relatively obscure eigenvalue inequality due to Wielandt is used to give a simple derivation of the asymptotic distribution of the eigenvalues of a random symmetric matrix. The asymptotic distributions are obtained under a fairly general setting. An application of the general theory to the bootstrap distribution of the eigenvalues of the sample covariance matrix is given.
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作者：GOLUBEV, GK; HASMINSKII, RZ
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作者：BHANSALI, RJ; PAPANGELOU, F
作者单位：University of Manchester
摘要：Given a realization of T consecutive observations of a stationary autoregressive process of unknown, possibly infinite, order m, it is assumed that a process of arbitrary finite order p is fitted by least squares. Under appropriate conditions it is known that the estimators of the autoregressive coefficients are asymptotically normal. The question considered here is whether the moments of the (scaled) estimators converge, as T --> infinity, to the moments of their asymptotic distribution. We e...
