1992

最新文章

• ON CONSISTENCY OF A CLASS OF ESTIMATORS FOR EXPONENTIAL-FAMILIES OF MARKOV RANDOM-FIELDS ON THE LATTICE AI翻译
  作者：COMETS, F
  作者单位：Universite Paris Cite; Centre National de la Recherche Scientifique (CNRS)
  摘要：We prove strong consistency of a class of maximum objective estimators for exponential parametric families of Markov random fields on Z(d), including both maximum likelihood and pseudolikelihood estimators, using large deviation estimates. We also obtain the optimality property for the maximum likelihood estimator in the sense of Bahadur.
• TESTING EXPONENTIALITY AGAINST IDMRL DISTRIBUTIONS WITH UNKNOWN CHANGE POINT AI翻译
  作者：HAWKINS, DL; KOCHAR, S; LOADER, C
  作者单位：Indian Statistical Institute; Indian Statistical Institute Delhi; AT&T; Nokia Corporation; Nokia Bell Labs
  摘要：Guess, Hollander and Proschan proposed tests for exponentiality versus IDMRL (increasing initially and then decreasing mean residual life) distributions when the change point, or corresponding quantile, is known. In this paper we propose two tests which do not require such knowledge of the change point. The tests are based on estimates of functionals of the cdf which discriminate between the exponential and IDMRL families.
• BIAS ROBUST ESTIMATION IN ORTHOGONAL REGRESSION AI翻译
  作者：ZAMAR, RH
  摘要：Orthogonal regression M-estimates are considered from a bias robust point of view. Their maximum bias over epsilon-contamination neighborhoods is characterized, and maximum bias curves are computed. The most bias robust orthogonal regression M-estimate is derived and shown to be a ''mode type'' estimate; for instance, in the two-dimensional case this estimate can be computed by locating a strip of fixed width covering the maximum number of data points. It will be shown that, although orthogona...
• ASYMPTOTIC NORMALITY OF THE SYNTHETIC DATA REGRESSION ESTIMATOR FOR CENSORED SURVIVAL-DATA AI翻译
  作者：ZHOU, M
  摘要：This article studies the large sample behavior of the censored data least squares estimator derived from the synthetic data method proposed by Leurgans and Zheng. The asymptotic distributions are derived by representing the estimator as a martingale plus a higher-order remainder term. Recently developed counting process techniques are used. The results are then compared to the censored regression estimator of Koul, Susarla and Van Ryzin.
• SMOOTHING IN ADAPTIVE ESTIMATION AI翻译
  作者：FARAWAY, JJ
  摘要：An adaptive maximum likelihood estimator based on the estimation of the log-density by B-splines is introduced. A data-driven method of selecting the smoothing parameter involved in the consequent density estimation is demonstrated. A Monte Carlo study is conducted to evaluate the small sample performance of the estimator in a location and a regression problem. The adaptive estimator is seen to compare favorably to some standard estimates. We show that the estimator is asymptotically efficient.
• ROBUST DIRECTION ESTIMATION AI翻译
  作者：HE, XM; SIMPSON, DG
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign
  摘要：We relate various measures of the stability of estimates in general parametric families and consider their application to direction estimates on spheres. We show that constructions such as the SB-robustness of Ko and Guttorp and the information-standardized gross-error sensitivity of Hampel, Ronchetti, Rousseeuw and Stahel fit into a general framework in which one measures the effect of model contamination by the Kullback-Leibler discrepancy. We also define a breakdown point appropriate for a ...
• ON PREDICTIVE LEAST-SQUARES PRINCIPLES AI翻译
  作者：WEI, CZ
  作者单位：Academia Sinica - Taiwan
  摘要：Recently, Rissanen proposed a new model selection criterion PLS that selects the model that minimizes the accumulated squares of prediction errors. Usually, the information-based criteria, such as AIC and BIC, select the model that minimizes a loss function which can be expressed as a sum of two terms. One measures the goodness of fit and the other penalizes the complexity of the selected model. In this paper we provide such an interpretation for PLS. Using this relationship, we give sufficien...
• THE ASYMPTOTICS OF ROUSSEEUW MINIMUM VOLUME ELLIPSOID ESTIMATOR AI翻译
  作者：DAVIES, L
  摘要：Rousseeuw's minimum volume estimator for multivariate location and dispersion parameters has the highest possible breakdown point for an affine equivariant estimator. In this paper we establish that it satisfies a local Holder condition of order 1/2 and converges weakly at the rate of n-1/3 to a non-Gaussian distribution.
• ASYMPTOTIC COMPARISON OF CRAMER-VONMISES AND NONPARAMETRIC FUNCTION ESTIMATION TECHNIQUES FOR TESTING GOODNESS-OF-FIT AI翻译
  作者：EUBANK, RL; LARICCIA, VN
  作者单位：University of Delaware
  摘要：Two new statistics for testing goodness-of-fit are derived from the viewpoint of nonparametric density estimation. These statistics are closely related to the Neyman smooth and Cramer-von Mises statistics but are shown to have superior properties both through asymptotic and small sample analyses. Comparison of the proposed tests with the Cramer-von Mises statistic requires the development of a novel technique for comparing tests that are capable of detecting local alternatives converging to th...
• INVARIANT DIRECTIONAL ORDERINGS AI翻译
  作者：LEHMANN, EL; ROJO, J
  作者单位：University of Texas System; University of Texas El Paso
  摘要：Statistical concepts of order permeate the theory and practice of statistics. The present paper is concerned with a large class of directional orderings of univariate distributions. (What do we mean by saying that a random variable Y is larger than another random variable X?) Attention is restricted to preorders that are invariant under monotone transformations; this includes orderings such as monotone likelihood ratio, hazard ordering, and stochastic ordering. Simple characterizations of thes...