Shrinkage for categorical regressors
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Publication:2024479
DOI10.1016/j.jeconom.2020.07.051zbMath1471.62416arXiv1901.01898OpenAlexW3097128783MaRDI QIDQ2024479
Jana Mareckova, Phillip Heiler
Publication date: 4 May 2021
Published in: Journal of Econometrics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1901.01898
Applications of statistics to economics (62P20) Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Ridge regression; shrinkage estimators (Lasso) (62J07)
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Cites Work
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- Sparse estimators and the oracle property, or the return of Hodges' estimator
- Jackknife model averaging
- Efficient shrinkage in parametric models
- Asymptotic optimality for \(C_ p\), \(C_ L\), cross-validation and generalized cross-validation: Discrete index set
- Estimating the dimension of a model
- Nonparametric estimation of distributions with categorical and continuous data
- Smoothing methods in statistics
- Distribution theory of the least squares averaging estimator
- Focused information criterion and model averaging for generalized additive partial linear models
- Optimal Weight Choice for Frequentist Model Average Estimators
- On uniform asymptotic risk of averaging GMM estimators
- Model Selection and Model Averaging
- NONPARAMETRIC ESTIMATION OF REGRESSION FUNCTIONS WITH DISCRETE REGRESSORS
- On nonparametric multivariate binary discrimination
- Multivariate binary discrimination by the kernel method
- Model Selection: An Integral Part of Inference
- Frequentist Model Average Estimators
- Model averaging, asymptotic risk, and regressor groups
- Sparsity and Smoothness Via the Fused Lasso
- Shrinking Towards Subspaces in Multiple Linear Regression
- Least Squares Model Averaging
- All Admissible Linear Estimates of the Mean Vector
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
- Some Comments on C P
- The Risk of James–Stein and Lasso Shrinkage
- Modelling Clustered Heterogeneity: Fixed Effects, Random Effects and Mixtures