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Optimal data collection for randomized control trials

Carneiro, Pedro; Lee, Sokbae; Wilhelm, Daniel - 2017

tuning parameters. In two empirical applications, we show that our procedure can lead to reductions of up to 58% in the costs …

Persistent link: https://www.econbiz.de/10011758346

Optimal data collection for randomized control trials

Carneiro, Pedro; Lee, Sokbae; Wilhelm, Daniel - 2017

tuning parameters. In two empirical applications, we show that our procedure can lead to reductions of up to 58% in the costs …

Persistent link: https://www.econbiz.de/10011626202

Optimal data collection for randomized control trials

Carneiro, Pedro; Lee, Sokbae; Wilhelm, Daniel - 2016

to 58%, measured either in terms of reductions in data collection costs or in terms of improvements in the precision of …

Persistent link: https://www.econbiz.de/10011458877

A nonlinear principal component decomposition

Gunsilius, Florian; Schennach, Susanne M. - 2017 - This version: March 27, 2017

The aim of this paper is to introduce a practical nonlinear generalization of PCA that captures nonlinear forms of dependence and delivers truly independent factors. The output of the method is a low-dimensional curvilinear coordinate system that tracks the important features of the data. The...

Persistent link: https://www.econbiz.de/10011627143