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Improved double kernel local linear quantile regression

M C Jones is at Department of Statistics, The Open University, UK

Keming Yu

KemingYu is at Department of Mathematical Sciences, Brunel University, UK. E-mail: keming.yu{at}brunel.ac.uk

As sample quantiles can be obtained as maximum likelihood estimates of location parameters in suitable asymmetric Laplace distributions, so kernel estimates of quantiles can be obtained as maximum likelihood estimates of location parameters in a general class of distributions with simple exponential tails. In this paper, this observation is applied to kernel quantile regression. In doing so, a new double kernel local linear quantile regression estimator is obtained which proves to be consistently superior in performance to the earlier double kernel local linear quantile regression estimator proposed by the authors. It is also straightforward to compute and more readily affords a first derivative estimate. An alternative method of selection for one of the two bandwidths involved also arises naturally but proves not to be so consistently successful.

Key Words: asymmetric Laplace distribution • bandwidth selection • exponential tails • maximum likelihood

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Statistical Modelling, Vol. 7, No. 4, 377-389 (2007)
DOI: 10.1177/1471082X0700700407


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This Article Abstract Free Full Text (Free PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in Web of Science Alert me to new issues of the journal Add to Saved Citations Download to citation manager Add to My Marked Citations Citing Articles Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Jones, M C Articles by Yu, K. Search for Related Content Social Bookmarking                         What's this?