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作者：JOE, H
作者单位：University of British Columbia
摘要：Given independent and identically distributed random variables from an unknown distribution F, we want to estimate quantiles of FN. Using asymptotic extreme-value theory, two parametric approximations to FN are considered and approximate maximum likelihood estimates are obtained. Conditions for consistency and asymptotic normality of these estimates are given, and properties of the estimates are obtained through a small simulation study.
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作者：LEURGANS, S
作者单位：University System of Ohio; Ohio State University
摘要：Estimators for the linear model in the presence of censoring are available. A new extension of the least-squares estimator to censored data is equivalent to applying the ordinary least-squares estimator to synthetic times, time constructed by magnifying the gaps between successive order statistics. Undr suitable regularity conditions, the synthetic data estimator is Fisher consistent and asymptotically normal. Examples facilitate comparison of the synthetic data estimator with estimators propo...
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作者：MORTON, R
作者单位：Commonwealth Scientific & Industrial Research Organisation (CSIRO)
摘要：We consider a scaled Poisson generalized linear model with random multiplicative errors associated with each stratum of a nested block structure. The normal equations are obtained. The quasi-likelihood is shown to be the product of a sequence of negative binomial quasi-likelihoods using weighted totals and correcting for margins. This is achieved by extending the definition of the quasi-likelihood which now includes a vector of weights. The usual asymptotic results still hold. Data on trap cat...
