Statistical Inference on the Cumulative Distribution Function using Judgment Post Stratification

TL;DR

This paper introduces a class of estimators for the CDF using judgment post stratification, demonstrating their efficiency, consistency, and normality, with optimal bandwidth selection and practical validation through simulations and real data.

Contribution

It develops a general framework for CDF estimation with JPS, proving asymptotic properties, optimal bandwidth, and superior performance over SRS in diverse settings.

Findings

JPS estimators are more efficient than SRS counterparts.

The estimators are strongly uniformly consistent and asymptotically normal.

Simulation and real data show significant efficiency gains with JPS.

Abstract

In this work, we discuss a general class of the estimators for the cumulative distribution function (CDF) based on judgment post stratification (JPS) sampling scheme which includes both empirical and kernel distribution functions. Specifically, we obtain the expectation of the estimators in this class and show that they are asymptotically more efficient than their competitors in simple random sampling (SRS), as long as the rankings are better than random guessing. We find a mild condition that is necessary and sufficient for them to be asymptotically unbiased. We also prove that given the same condition, the estimators in this class are strongly uniformly consistent estimators of the true CDF, and converge in distribution to a normal distribution when the sample size goes to infinity. We then focus on the kernel distribution function (KDF) in the JPS design and obtain the optimal bandwidth. We next carry out a comprehensive Monte Carlo simulation to compare the performance of the KDF in the JPS design for different choices of sample size, set size, ranking quality, parent distribution, kernel function as well as both perfect and imperfect rankings set-ups with its counterpart in SRS design. It is found that the JPS estimator dramatically improves the efficiency of the KDF as compared to its SRS competitor for a wide range of the settings. Finally, we apply the described procedure on a real dataset from medical context to show their usefulness and applicability in practice.