M-estimation, convexity and quantiles
نویسندگان
چکیده
منابع مشابه
On M-estimators of Approximate Quantiles and Approximate Conditional Quantiles
M-estimators introduced in Huber (1964) provide a class of robust estimators of a center of symmetry of a symmetric probability distribution which also have very high eeciency at the model. However it is not clear what they do estimate when the probability distributions are nonsymmetric. In this paper we rst show that in the case of arbitrary, not necessarily symmetric probabilty distributions,...
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Quantiles are convenient measures of the entire range of values of simulation outputs. However, unlike the mean and standard deviation, the observations have to be stored since calculation of quantiles requires several passes through the data. Thus, quantile estimation(QE) requires a large amount of computer storage and computation time. Several approaches for estimating quantiles in RS(regener...
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Sydney, NSW 2109, Australia Abstract This paper explores a class of robust estimators of normal quantiles filling the gap between maximum likelihood estimators and empirical quantiles. Our estimators are linear combinations of M-estimators. Their asymptotic variances can be arbitrarily close to variances of the maximum likelihood estimators. Compared with empirical quantiles, the new estimators...
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The estimation of extreme conditional quantiles is an important issue in different scientific disciplines. Up to now, the extreme value literature focused mainly on estimation procedures based on i.i.d. samples. On the other hand, quantile regression based procedures work well for estimation within the data range i.e. the estimation of nonextreme quantiles but break down when main interest is i...
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In 1981 Rubin introduced the Bayesian bootstrap and argued that it was the natural Bayesian analogue to the usual bootstrap. We show here that when estimating a population quantile in a nonparametric problem it yields estimators that are often preferred to the natural naive estimators based on the order statistic. AMS 1980 Subject Classification: 62G05, 62C15 and 62G30
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ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 1997
ISSN: 0090-5364
DOI: 10.1214/aos/1031833659