<![CDATA[This study aims to estimate and comparatively evaluate the performance of four entropy measures—Havrda–Charvat, Kapur, Verma, and Mathai–Haubold—in modeling newborn weight. A quantitative approach was adopted through analytical derivations and Monte Carlo simulation techniques. The performance of each entropy measure was assessed across varying sample sizes using bias, mean squared error (MSE), and root mean squared error (RMSE) as evaluation criteria. The findings indicate that the Havrda–Charvat entropy measure demonstrates superior accuracy, consistency, and convergence toward the true entropy values, thereby exhibiting robust performance under the entropy-transformed exponential distribution (ETED). These results contribute to the theoretical development of entropy-based modeling by extending current understanding of estimator performance within ETED and providing comparative evidence on the suitability of alternative entropy measures for newborn weight modeling.]]>
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