Compares post-selection estimators for the maximum observed Sharpe ratio using simulations and finds James-Stein shrinkage yields lowest bias and RMSE across tested parameters.
Exact post-selection inference, with application to the lasso
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abstract
We develop a general approach to valid inference after model selection. At the core of our framework is a result that characterizes the distribution of a post-selection estimator conditioned on the selection event. We specialize the approach to model selection by the lasso to form valid confidence intervals for the selected coefficients and test whether all relevant variables have been included in the model.
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Post Selection Estimation of Sharpe Ratios
Compares post-selection estimators for the maximum observed Sharpe ratio using simulations and finds James-Stein shrinkage yields lowest bias and RMSE across tested parameters.