RP-Department of Management Science

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Browsing RP-Department of Management Science by Subject "Asymptotic normality"

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    Estimation of critical streamflow discharge level using nonparametric quantile regression model
    (Global Journal of Advanced Research, 2016-04-30) Kiarie, F.
    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.
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    Parametric and non-parametric model performance differences in estimation of critical stream flow discharge levels
    (International Journal of Science and Research (IJSR), 2013) Kiarie, F. K
    Nonparametric estimation based on quantile regression methodology of Koenker and Basset (1978) and conventional parametric regression approaches were applied to a river regime to estimate volatility in streamflow discharge levels. Consistency and asymptotic normality properties of estimators obtained from both approaches were given. From the study results non-parametric quantile regression approach yielded better results than other methods. Other than for boundary effects which require boundary modifications, the model validation results implied good performance of the nonparametric model in estimating critical streamflow discharge levels.