Rapid penalized likelihood-based outlier detection via heteroskedasticity test
From MaRDI portal
Publication:5106847
DOI10.1080/00949655.2016.1257010OpenAlexW2549231731MaRDI QIDQ5106847
Xiuli Wang, Ping Dong, Lu Lin, Yunquan Song
Publication date: 22 April 2020
Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00949655.2016.1257010
linear modeloutlier detectiondifference convex algorithmheteroskedasticity testnonconvex penalized regression
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A Simple Test for Heteroscedasticity and Random Coefficient Variation
- A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
- The Adaptive Lasso and Its Oracle Properties
- Regularization of case-specific parameters for robustness and efficiency
- Outlier detection and robust mixture modeling using nonconvex penalized likelihood
- Regularization in statistics
- Composite quantile regression and the oracle model selection theory
- Nonconcave penalized inverse regression in single-index models with high dimensional predic\-tors
- Persistene in high-dimensional linear predictor-selection and the virtue of overparametrization
- High-dimensional graphs and variable selection with the Lasso
- Outlier Detection Using Nonconvex Penalized Regression
- Estimating Regression Models with Multiplicative Heteroscedasticity
- Testing Heteroscedasticity In Nonparametric Regression
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Tuning parameter selectors for the smoothly clipped absolute deviation method
This page was built for publication: Rapid penalized likelihood-based outlier detection via heteroskedasticity test