Robust and efficient estimation with weighted composite quantile regression
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Publication:1619607
DOI10.1016/j.physa.2016.03.056zbMath1400.62106OpenAlexW2315344928MaRDI QIDQ1619607
Tian Xia, Jingzhi Li, Wanfeng Yan, Xue-Jun Jiang
Publication date: 13 November 2018
Published in: Physica A (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.physa.2016.03.056
Asymptotic properties of parametric estimators (62F12) Nonparametric robustness (62G35) General nonlinear regression (62J02)
Related Items (5)
Spatial quantile estimation of multivariate threshold time series models ⋮ Local influence analysis for quasi-likelihood nonlinear models with random effects ⋮ Composite quantile regression for massive datasets ⋮ Distributed penalized modal regression for massive data ⋮ Adaptive quantile regressions for massive datasets
Cites Work
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- A modification of Karmarkar's linear programming algorithm
- Composite quantile regression and the oracle model selection theory
- Asymptotic theory of nonlinear least squares estimation
- On average derivative quantile regression
- Least absolute deviation estimation for regression with ARMA errors
- Limiting distributions for \(L_1\) regression estimators under general conditions
- An interior point algorithm for nonlinear quantile regression
- A note on recent proposals for computing \(l_ 1\) estimates
- Least absolute deviations estimation for ARCH and GARCH models
- Empirical likelihood and quantile regression in longitudinal data analysis
- Conditional Heteroskedasticity in Asset Returns: A New Approach
- Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
- Regression Quantiles
- Weighted composite quantile regression estimation of DTARCH models
- Asymptotic Properties of Non-Linear Least Squares Estimators
- On an algorithm for discrete nonlinear L1 approximation
- Robust modelling of DTARCH models
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