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Abstract:
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In recent years, studies of heavy tailed distributions have rapidly developed. For multivariate heavy tailed distributions, estimation of conditional quantiles at very high or low tails is of interest in numerous applications. Quantile regression uses an L1- loss function, and the optimal solution of linear programming for estimating coefficients of regression. This paper proposes a weighted quantile regression method on high quantile regression for certain extreme value sets. The Monte Carlo simulations show good results of the proposed weighted method. Comparisons of the proposed method and existing methods are given. The paper also investigates a CO? Emission real-world example by using the proposed weighted method.
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