
An Econometric View of Algorithmic Subsampling
Datasets that are terabytes in size are increasingly common, but compute...
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An Econometric Perspective of Algorithmic Sampling
Datasets that are terabytes in size are increasingly common, but compute...
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Provable More Data Hurt in High Dimensional Least Squares Estimator
This paper investigates the finitesample prediction risk of the highdi...
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Learning Entangled SingleSample Distributions via Iterative Trimming
In the setting of entangled singlesample distributions, the goal is to ...
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Finite sample deviation and variance bounds for first order autoregressive processes
In this paper, we study finitesample properties of the least squares es...
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Sketched MinDist
We consider sketch vectors of geometric objects J through the function ...
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On the Subbagging Estimation for Massive Data
This article introduces subbagging (subsample aggregating) estimation ap...
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Sketching for TwoStage Least Squares Estimation
When there is so much data that they become a computation burden, it is not uncommon to compute quantities of interest using a sketch of data of size m instead of the full sample of size n. This paper investigates the implications for twostage least squares (2SLS) estimation when the sketches are obtained by a computationally efficient method known as CountSketch. We obtain three results. First, we establish conditions under which given the full sample, a sketched 2SLS estimate can be arbitrarily close to the fullsample 2SLS estimate with high probability. Second, we give conditions under which the sketched 2SLS estimator converges in probability to the true parameter at a rate of m^1/2 and is asymptotically normal. Third, we show that the asymptotic variance can be consistently estimated using the sketched sample and suggest methods for determining an inferenceconscious sketch size m. The sketched 2SLS estimator is used to estimate returns to education.
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