On the accuracy of fixed sample and fixed width confidence intervals based on the vertically weighted average

Abstract

Vertically weighted averages perform a bilateral filtering of data, in order to preserve fine details of the underlying signal, especially discontinuities such as jumps (in one dimension) or edges (in two dimensions). In homogeneous regions of the domain the procedure smoothes the data by averaging nearby data points to reduce the noise, whereas in inhomogeneous regions the neighboring points are only taken into account when their value is close to the current one. This results in a denoised reconstruction or estimate of the true signal without blurring finer details. This article addresses the lack of results about the construction and evaluation of confidence intervals based on the vertically weighted average, which is required for a proper statistical evaluation of its estimation accuracy. Based on recent results we discuss and investigate in greater detail fixed sample and fixed width (conditional) confidence intervals constructed from this estimator. The fixed width approach allows to specify explicitly the estimator’s accuracy and determines a random sample size to ensure the required coverage probability. This also fixes to some extent the inherent property of the vertically weighted average that its variability is higher in low-density regions than in high-density regions. To estimate the variances required to construct the procedures, we rely on resampling techniques, especially the bootstrap and the jackknife. Extensive Monte Carlo simulations show that, in general, the proposed confidence intervals are highly reliable in terms of their coverage probabilities for a wide range of parameter settings. The performance can be further increased by the bootstrap.