Introduces random smoothing to produce asymptotically normal estimators and Wald confidence regions for linear regression with jointly stationary-ergodic errors without long-run variance estimation.
APACrefauthors \ 1997
2 Pith papers cite this work. Polarity classification is still indexing.
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The deep SPAR model shows concurrent floods and droughts becoming more likely in the Upper Danube by 2100 under high emissions, with changes in the dependence between catchments contributing substantially to the increase.
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New Confidence Regions for Linear Regression Parameters with Stationary-Ergodic Dependent Errors
Introduces random smoothing to produce asymptotically normal estimators and Wald confidence regions for linear regression with jointly stationary-ergodic errors without long-run variance estimation.
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Exploring climate change effects on concurrent floods and concurrent droughts via statistical deep learning
The deep SPAR model shows concurrent floods and droughts becoming more likely in the Upper Danube by 2100 under high emissions, with changes in the dependence between catchments contributing substantially to the increase.