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What is Quantile Regression about?

Quantile Regression (QR), proposed by Koenker and Bassett in 1978, extends classical least squares estimation to model different conditional quantile functions, providing a more comprehensive view of the data distribution. QR is particularly useful for analyzing the impact of explanatory variables across various quantiles, making it robust to outliers and applicable to non-standard distributions. The document also discusses the theoretical background, advantages, and practical applications of QR, including

Author

imrozzahandipto

Language

EN