Koenker, Roger. ed. Chernozhukov, Victor. ed. He, Xuming. ed. Peng, Limin. ed. text bibliography flu Boca Raton CRC Press 2018 monographic eng

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xix, 463 p. : ill. ; 27 cm. Quantile regression constitutes an ensemble of statistical techniques intended to estimate and draw inferences about conditional quantile functions. Median regression, as introduced in the 18th century by Boscovich and Laplace, is a special case. In contrast to conventional mean regression that minimizes sums of squared residuals, median regression minimizes sums of absolute residuals; quantile regression simply replaces symmetric absolute loss by asymmetric linear loss. Since its introduction in the 1970's by Koenker and Bassett, quantile regression has been gradually extended to a wide variety of data analytic settings including time series, survival analysis, and longitudinal data. By focusing attention on local slices of the conditional distribution of response variables it is capable of providing a more complete, more nuanced view of heterogeneous covariate effects. Applications of quantile regression can now be found throughout the sciences, including astrophysics, chemistry, ecology, economics, finance, genomics, medicine, and meteorology. Software for quantile regression is now widely available in all the major statistical computing environments. The objective of this volume is to provide a comprehensive review of recent developments of quantile regression methodology illustrating its applicability in a wide range of scientific settings. The intended audience of the volume is researchers and graduate students across a diverse set of disciplines.-- edited by Roger Koenker ... [et al.] Includes bibliographical references and index. Regression analysis QA278.2 .H3738 2018 519.536 HAN 9780367657574 (pbk) 2017005283 DLC 170208 20220417121213.0 19485625 eng