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Inference on a Distribution from Noisy Draws

Koen Jochmans and Martin Weidner

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Abstract: We consider a situation where the distribution of a random variable is being estimated by the empirical distribution of noisy measurements of that variable. This is common practice in, for example, teacher value-added models and other fixed-effect models for panel data. We use an asymptotic embedding where the noise shrinks with the sample size to calculate the leading bias in the empirical distribution arising from the presence of noise. The leading bias in the empirical quantile function is equally obtained. These calculations are new in the literature, where only results on smooth functionals such as the mean and variance have been derived. Given a closed-form expression for the bias, bias-corrected estimator of the distribution function and quantile function can be constructed. We provide both analytical and jackknife corrections that recenter the limit distribution and yield confidence intervals with correct coverage in large samples. These corrections are non-parametric and easy to implement. Our approach can be connected to corrections for selection bias and shrinkage estimation and is to be contrasted with deconvolution. Simulation results confirm the much-improved sampling behavior of the corrected estimators.

Date: 2018-03, Revised 2019-09
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Working Paper: Inference on a distribution from noisy draws (2019) Downloads
Working Paper: Inference on a distribution from noisy draws (2019) Downloads
Working Paper: Inference on a distribution from noisy draws (2018) Downloads
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