著者: Jason D. Lee , Dennis L. Sun , Yuekai Sun , Jonathan E. Taylor - The Annals of Statistics 2016 被引用: 486
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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A note on data-splitting for the evaluation of… A note on data-splitting for the evaluation of significance levels Effects of Model Selection on Inference Effects of Model Selection on Inference Regression Shrinkage and Selection Via the Lasso Regression Shrinkage and Selection Via the Lasso Least angle regression Least angle regression MODEL SELECTION AND INFERENCE: FACTS AND… MODEL SELECTION AND INFERENCE: FACTS AND FICTION Regularization and Variable Selection Via… Regularization and Variable Selection Via the Elastic Net False Discovery Rate–Adjusted Multiple… False Discovery Rate–Adjusted Multiple Confidence Intervals for Selected Parameters Valid post-selection inference Valid post-selection inference The lasso problem and uniqueness The lasso problem and uniqueness Confidence Intervals for Low Dimensional… Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models A significance test for the lasso A significance test for the lasso Confidence Intervals and Hypothesis Testing for… Confidence Intervals and Hypothesis Testing for High-Dimensional Regression Exact Post-Selection Inference for Sequentia… Exact Post-Selection Inference for Sequential Regression Procedures How Much Does Your Data Exploration Overfit?… How Much Does Your Data Exploration Overfit? Controlling Bias via Information Usage Asymptotic post-selection inferenc… Asymptotic post-selection inference for the Akaike information criterion Bootstrapping and sample splitting for… Bootstrapping and sample splitting for high-dimensional, assumption-lean inference Postselection Inference in Structural Equation… Postselection Inference in Structural Equation Modeling Post-Selection Inference Post-Selection Inference The terminating-random experiments selector… The terminating-random experiments selector: Fast high-dimensional variable selection with false discovery rate control Machine Learning Advances for Time Serie… Machine Learning Advances for Time Series Forecasting Testing for a Change in Mean after Changepoint… Testing for a Change in Mean after Changepoint Detection Approximate Selective Inference via Maximum… Approximate Selective Inference via Maximum Likelihood Selective Inference for Hierarchical Clustering Selective Inference for Hierarchical Clustering Is Seeing Believing? A Practitioner's… Is Seeing Believing? A Practitioner's Perspective on High-Dimensional Statistical Inference in Cancer Genomics Studies Exact post-selection inference, with… Exact post-selection inference, with application to the lasso 過去の参考文献 中心の論文 この論文を引用する論文 古い 新しい ノードをクリックするとフォーカスを固定、空白をクリックすると本論文に戻ります。ホバーで一時的にプレビューできます。各ノードのページはタイトルから開けます。
