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Forecasting using a large number of predictors: is Bayesian regression a valid alternative to principal components? [PDF]
This paper considers Bayesian regression with normal and doubleexponential priors as forecasting methods based on large panels of time series. We show that, empirically, these forecasts are highly correlated with principal component forecasts and that ...
De Mol, Christine +2 more
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Post-l1-penalized estimators in high-dimensional linear regression models [PDF]
In this paper we study post-penalized estimators which apply ordinary, unpenalized linear regression to the model selected by first-step penalized estimators, typically LASSO. It is well known that LASSO can estimate the regression function at nearly the
Victor Chernozhukov, Alexandre Belloni
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Using Quantile Regression for Duration Analysis [PDF]
Quantile regression methods are emerging as a popular technique in econometrics and biometrics for exploring the distribution of duration data. This paper discusses quantile regression for duration analysis allowing for a flexible specification of the ...
Wilke, Ralf A., Fitzenberger, Bernd
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A comparative study of the Lasso-type and heuristic model selection methods [PDF]
This study presents a first comparative analysis of Lasso-type (Lasso, adaptive Lasso, elastic net) and heuristic subset selection methods. Although the Lasso has shown success in many situations, it has some limitations.
Ivan Savin
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Bayesian adaptive lasso quantile regression with non-ignorable missing responses
Computational StatisticszbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ranran Chen +3 more
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Bayesian adaptive Lasso for quantile regression models with nonignorably missing response data
Communications in Statistics - Simulation and Computation, 2019Handling data with the nonignorably missing mechanism is still a challenging problem in statistics.
Dengke Xu, Niansheng Tang
openaire +4 more sources
Bayesian Lasso Binary Quantile Regression [PDF]
Computational Statistics, 2013 In this paper, a Bayesian hierarchical model for variable selection and estimation in the context of binary quantile regression is proposed. Existing approaches to variable selection in a binary classification context are sensitive to outliers ...
Keming Yu, Rahim Alhamzawi
exaly +2 more sources