Efficient Quantile Regression for Heteroscedastic Models
نویسندگان
چکیده
Quantile regression provides estimates of a range of conditional quantiles. This stands in contrast to traditional regression techniques, which focus on a single conditional mean function. Lee et al. (2012) proposed efficient quantile regression by rounding the sharp corner of the loss. The main modification generally involves an asymmetric l2 adjustment of the loss function around zero. We extend the idea of l2 adjusted quantile regression to linear heterogeneous models. The l2 adjustment is constructed to diminish as sample size grows. Conditions to retain consistency properties are also provided.
منابع مشابه
Robust Estimation of Conditional Mean by the Linear Combination of Quantile Regressions
In this paper, we propose a new robust estimator for regression problems in the form of the linear combination of quantile regressions. The proposed robust regression estimator is helpful for the conditional mean estimation especially when the error distribution is asymmetric or/and heteroscedastic, where conventional robust regressions yield considerable bias to the conditional mean. First we ...
متن کاملVariable Selection in Single Index Quantile Regression for Heteroscedastic Data
Quantile regression (QR) has become a popular method of data analysis, especially when the error term is heteroscedastic, due to its relevance in many scientific studies. The ubiquity of high dimensional data has led to a number of variable selection methods for linear/nonlinear QR models and, recently, for the single index quantile regression (SIQR) model. We propose a new algorithm for simult...
متن کاملConstructing inverse probability weights for continuous exposures: a comparison of methods.
Inverse probability-weighted marginal structural models with binary exposures are common in epidemiology. Constructing inverse probability weights for a continuous exposure can be complicated by the presence of outliers, and the need to identify a parametric form for the exposure and account for nonconstant exposure variance. We explored the performance of various methods to construct inverse p...
متن کاملFinite Sample Properties of Quantile Interrupted Time Series Analysis: A Simulation Study
Interrupted Time Series (ITS) analysis represents a powerful quasi-experime-ntal design in which a discontinuity is enforced at a specific intervention point in a time series, and separate regression functions are fitted before and after the intervention point. Segmented linear/quantile regression can be used in ITS designs to isolate intervention effects by estimating the sudden/level change (...
متن کاملFlexible parametric quantile regression model
This article introduces regression quantile models using both RS and FKML generalised lambda distributions (GLD) and demonstrates the versatility of proposed models for a range of linear/non linear and heteroscedastic/homoscedastic empirical data. Owing to the rich shapes of GLDs, GLD quantile regression is a competitive flexible model compared to standard quantile regression. The proposed meth...
متن کامل