qrcm

Parametric modeling of quantile regression coefficient functions. Estimating the conditional quantiles of outcome variables of interest is frequent in many research areas, and quantile regression is foremost among the utilized methods. The coefficients of a quantile regression model depend on the order of the quantile being estimated. For example, the coefficients for the median are generally different from those of the 10th centile. In this article, we describe an approach to modeling the regression coefficients as parametric functions of the order of the quantile. This approach may have advantages in terms of parsimony, efficiency, and may expand the potential of statistical modeling. Goodness-of-fit measures and testing procedures are discussed, and the results of a simulation study are presented. We apply the method to analyze the data that motivated this work. The described method is implemented in the qrcm R package.


References in zbMATH (referenced in 13 articles )

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  1. Hou, Yanxi: A two-stage model for high-risk prediction in insurance ratemaking: asymptotics and inference (2022)
  2. Lv, Jing; Li, Jialiang: High-dimensional varying index coefficient quantile regression model (2022)
  3. Sottile, Gianluca; Frumento, Paolo: Robust estimation and regression with parametric quantile functions (2022)
  4. Baione, Fabio; Biancalana, Davide: An application of parametric quantile regression to extend the two-stage quantile regression for ratemaking (2021)
  5. Frumento, Paolo; Bottai, Matteo; Fernández-Val, Iván: Parametric modeling of quantile regression coefficient functions with longitudinal data (2021)
  6. Frumento, Paolo; Salvati, Nicola: Parametric modeling of quantile regression coefficient functions with count data (2021)
  7. Hsu, Chih-Yuan; Wen, Chi-Chung; Chen, Yi-Hau: Quantile function regression analysis for interval censored data, with application to salary survey data (2021)
  8. Maruotti, Antonello; Petrella, Lea; Sposito, Luca: Hidden semi-Markov-switching quantile regression for time series (2021)
  9. Hsu, Chih-Yuan; Chen, Yi-Hau; Yu, Ruoh-Rong; Hung, Tsung-Wei: Assessing wage status transition and stagnation using quantile transition regression (2020)
  10. Sottile, Gianluca; Frumento, Paolo; Chiodi, Marcello; Bottai, Matteo: A penalized approach to covariate selection through quantile regression coefficient models (2020)
  11. Sottile, Gianluca; Adelfio, Giada: Clusters of effects curves in quantile regression models (2019)
  12. Frumento, Paolo; Bottai, Matteo: Parametric modeling of quantile regression coefficient functions with censored and truncated data (2017)
  13. Frumento, Paolo; Bottai, Matteo: Parametric modeling of quantile regression coefficient functions (2016)