Virtually Adjusted Naturally Constrained Entropy (VANCE) Estimator via TRUMP Survey Methodology

Thomas M. Menino Convention & Exhibition Center 

We propose what we call a Virtually Adjusted Naturally Constrained Entropy (VANCE) estimator for estimating population totals under complex sampling designs. The methodology is grounded in the principle that entropy is an intrinsic measure of information and should remain naturally constrained, while distortions arising from unequal inclusion probabilities, design effects, and population heterogeneity are accommodated through a virtual adjustment mechanism. The approach avoids direct modification of the entropy structure and achieves stabilization through controlled adjustment, thereby preserving information integrity via what we call a Unified Stabilizing Hyperbolic Adjustment (USHA). In addition, TRUMP Cuts are used as tuning devices to enhance efficiency and accuracy. Expressions for bias and mean squared error of the VANCE estimator are derived. Extensive simulation studies show that the VANCE estimator outperforms the competing estimators considered. This work continues a line of research presented at the Joint Statistical Meetings since 2017, with recent proceedings among the most viewed on Zenodo..

Empirical Log Likelihood Estimates

Complex Survey Designs

System of non-linear equations

Auxiliary Information

Tuning of Design Weights

Jackknifing and TRUMP Cuts 

Survey Research Methods Section