02406cam a2200289 i 450000100090000000300080000900500170001700800410003401000170007502000240009204000370011604200080015305000250016108200170018624500770020326000380028030000360031836500150035449000640036950400510043352015080048465000250199270000250201770000310204270000210207370000220209419485625BD-DhUL20220417121213.0170208s2018    flua     b    001 0 eng    a  2017005283  a9780367657574 (pbk)  aDLCbengcDLCerdadDLCdBD-DhUL  apcc00aQA278.2b.H3738 201800a519.536bHAN10aHandbook of quantile regression / cedited by Roger Koenker ... [et al.]  aBoca Raton : bCRC Press, c2018.  axix, 463 p. : bill. ; c27 cm.  aGBPb42.990 aChapman & Hall/CRC handbooks of modern statistical methods.  aIncludes bibliographical references and index.  aQuantile 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.--cProvided by Publisher. 0aRegression analysis.1 aKoenker, Roger.eed.1 aChernozhukov, Victor.eed.1 aHe, Xuming.eed.1 aPeng, Limin.eed.