The \(k\)th power expectile regression
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Publication:2046477
DOI10.1007/s10463-019-00738-yzbMath1479.62025OpenAlexW2996294044MaRDI QIDQ2046477
Fu-ming Lin, Yong Zhou, Yingying Jiang
Publication date: 18 August 2021
Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10463-019-00738-y
Applications of statistics to economics (62P20) Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20)
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Cites Work
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- Maximum Likelihood Specification Testing and Conditional Moment Tests
- Asymmetric Least Squares Estimation and Testing
- Asymptotic normality of conditional density estimation with left-truncated and dependent data
- Assessing value at risk with CARE, the conditional autoregressive expectile models
- Expectile asymptotics
- High-dimensional generalizations of asymmetric least squares regression and their applications
- Semiparametric quantile regression estimation in dynamic models with partially varying coefficients
- On M-processes and M-estimation
- Quantile regression with varying coefficients
- Asymptotic behavior of M estimators of p regression parameters when \(p^ 2/n\) is large. II: Normal approximation
- Conditional \(L_ p\)-quantiles and their application to the testing of symmetry in non-parametric regression
- Limiting behavior of \(M\)-estimators of regression coefficients in high dimensional linear models. I: Scale-dependent case. II: Scale-invariant case
- A general Bahadur representation of \(M\)-estimators and its application to linear regression with nonstochastic designs
- On parameters of increasing dimensions
- An SVM-like approach for expectile regression
- M-quantiles
- Robust Tests for Heteroscedasticity Based on Regression Quantiles
- Regression Quantiles
- Asymmetric least squares regression estimation: A nonparametric approach∗
- Of Quantiles and Expectiles: Consistent Scoring Functions, Choquet Representations and Forecast Rankings
- Nonparametric Quantile Estimations for Dynamic Smooth Coefficient Models
- A Useful Convergence Theorem for Probability Distributions
- Asymptotic distribution of regression M-estimators
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