High-Confident Nonparametric Fixed-Width Uncertainty Intervals and Applications to Projected High-Dimensional Data and Common Mean Estimation

TL;DR

This paper develops nonparametric two-stage methods for constructing fixed-width confidence intervals, ensuring high confidence and efficiency, especially useful for high-dimensional, distributed big data analysis.

Contribution

It introduces a novel high-confident asymptotic framework and demonstrates the validity and efficiency of the procedures for high-dimensional and distributed data settings.

Findings

Procedures achieve consistency and efficiency under both classical and high-confidence asymptotics.

Validated through simulations and real data analysis.

Applicable to high-dimensional projections and order-constrained mean estimation.

Abstract

Nonparametric two-stage procedures to construct fixed-width confidence intervals are studied to quantify uncertainty. It is shown that the validity of the random central limit theorem (RCLT) accompanied by a consistent and asymptotically unbiased estimator of the asymptotic variance already guarantees consistency and first as well as second order efficiency of the two-stage procedures. This holds under the common asymptotics where the length of the confidence interval tends to $0$ as well as under the novel proposed high-confident asymptotics where the confidence level tends to $1$. The approach is motivated by and applicable to data analysis from distributed big data with non-negligible costs of data queries. The following problems are discussed: Fixed-width intervals for a the mean, for a projection when observing high-dimensional data and for the common mean when using nonlinear common mean estimators under order constraints. The procedures are investigated by simulations and illustrated by a real data analysis.