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- W1987133051 abstract "Abstract Quantile regression is an increasingly popular method for estimating the quantiles of a distribution conditional on the values of covariates. Regression quantiles are robust against the influence of outliers and, taken several at a time, they give a more complete picture of the conditional distribution than a single estimate of the center. This article first presents an iterative algorithm for finding sample quantiles without sorting and then explores a generalization of the algorithm to nonlinear quantile regression. Our quantile regression algorithm is termed an MM, or majorize—minimize, algorithm because it entails majorizing the objective function by a quadratic function followed by minimizing that quadratic. The algorithm is conceptually simple and easy to code, and our numerical tests suggest that it is computationally competitive with a recent interior point algorithm for most problems." @default.
- W1987133051 created "2016-06-24" @default.
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- W1987133051 date "2000-03-01" @default.
- W1987133051 modified "2023-09-29" @default.
- W1987133051 title "Quantile Regression via an MM Algorithm" @default.
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- W1987133051 doi "https://doi.org/10.1080/10618600.2000.10474866" @default.
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