Estimation of critical streamflow discharge level using nonparametric quantile  regression model

dc.contributor.author	Kiarie, F.
dc.date.accessioned	2016-05-16T13:42:56Z
dc.date.available	2016-05-16T13:42:56Z
dc.date.issued	2016-04-30
dc.description.abstract	Various parametric models have been designed to analyze volatility in river flow time series data. For maximum likelihood estimation these parametric methods assumes a known conditional distribution. This paper considers the problem of nonparametric estimation of critical streamflow discharge levels of a river regime based on quantile regression methodology of Koenker and Basset (1978).In particular, the paper demonstrates the use of kernel estimators for conditional quantiles resulting from a kernel estimation of conditional distribution function. It is finally proved that the estimate of the nonparametric quantile function is consistent and asymptotically normally distributed and under suitable conditions, the estimator converges uniformly with an appropriate rate.	en_US
dc.identifier.citation	Vol-3, Issue-4 PP. 303-308	en_US
dc.identifier.issn	2394-5788
dc.identifier.uri	http://ir-library.ku.ac.ke/handle/123456789/14713
dc.language.iso	en	en_US
dc.publisher	Global Journal of Advanced Research	en_US
dc.subject	Conditional quantile	en_US
dc.subject	Kernel estimate	en_US
dc.subject	Quantileautoregression	en_US
dc.subject	Consistency	en_US
dc.subject	Asymptotic normality	en_US
dc.subject	Critical discharge level.	en_US
dc.title	Estimation of critical streamflow discharge level using nonparametric quantile regression model	en_US
dc.type	Article	en_US

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