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Common high-dimensional methods for prediction rely on having either a sparse signal model, a model in which most parameters are zero and there are a small number of non-zero parameters that are large in magnitude, or a dense signal model, a model with no large parameters and very many small...
Persistent link: https://www.econbiz.de/10010477564
Common high-dimensional methods for prediction rely on having either a sparse signal model, a model in which most parameters are zero and there are a small number of non-zero parameters that are large in magnitude, or a dense signal model, a model with no large parameters and very many small...
Persistent link: https://www.econbiz.de/10011337679
-parametric and easy to implement. Our approach can be connected to corrections for selection bias and shrinkage estimation and is to …
Persistent link: https://www.econbiz.de/10012063831
for selection bias and shrinkage estimation and is to be contrasted with deconvolution. Simulation results confirm the …
Persistent link: https://www.econbiz.de/10012792731
Empirical research typically involves a robustness-efficiency tradeoff. A researcher seeking to estimate a scalar parameter can invoke strong assumptions to motivate a restricted estimator that is precise but may be heavily biased, or they can relax some of these assumptions to motivate a more...
Persistent link: https://www.econbiz.de/10015073234