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26 records from EconBiz based on author Name
1. Handbook of quantile regression
abstractQuantile 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.--
Koenker, Roger; Chernozhukov, Victor; He, Xuming; Peng, Limin;2018
Type: Aufsatzsammlung; Beiträge
2. Quantile Regression for Survival Data
abstractQuantile regression offers a useful alternative strategy for analyzing survival data. Compared with traditional survival analysis methods, quantile regression allows for comprehensive and flexible evaluations of covariate effects on a survival outcome of interest while providing simple physical interpretations on the time scale. Moreover, many quantile regression methods enjoy easy and stable computation. These appealing features make quantile regression a valuable practical tool for delivering in-depth analyses of survival data. This article provides a review of a comprehensive set of statistical methods for performing quantile regression with different types of survival data. The review covers various survival scenarios, including randomly censored data, data subject to left truncation or censoring, competing risks and semicompeting risks data, and recurrent events data. Two real-world examples are presented to illustrate the utility of quantile regression for practical survival data analyses
Peng, Limin;2021
3. Estimation of causal quantile effects with a binary instrumental variable and censored data
Wei, Bo; Peng, Limin; Zhang, Mei‐Jie; Fine, Jason P.;2021
Availability: Link
Citations: 3 (based on OpenCitations)
4. Competing risks quantile regression
Peng, Limin; Fine, Jason P.;2009
Type: Aufsatz in Zeitschrift; Article in journal;
5. Survival analysis with quantile regression models
Peng, Limin; Huang, Yijian;2008
Type: Aufsatz in Zeitschrift; Article in journal;
6. Quantile regression adjusting for dependent censoring from semicompeting risks
Li, Ruosha; Peng, Limin;2015
Availability: Link
7. Varying coefficient subdistribution regression for left-truncated semi-competing risks data
Li, Ruosha; Peng, Limin;2014
Availability: Link
8. Rank estimation of accelerated lifetime models with dependent censoring
Peng, Limin; Fine, Jason P.;2006
Type: Aufsatz in Zeitschrift; Article in journal;
9. New Agreement Measures Based on Survival Processes
Guo, Ying; Li, Ruosha; Peng, Limin; Manatunga, Amita K.;2013
Availability: Link
10. Self-consistent estimation of censored quantile regression
Peng, Limin;2012
Availability: Link