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An Efficient Estimation Model for Environmental Data: Theory, Simulation, and an Air Quality Application | ||
| Caspian Journal of Mathematical Sciences | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 18 فروردین 1405 | ||
| نوع مقاله: Research Articles | ||
| شناسه دیجیتال (DOI): 10.22080/cjms.2026.30984.1805 | ||
| نویسنده | ||
| khalid ul islam rather* | ||
| Sher-e-Kashmir univeristy of agricultural sciences and technology jammu | ||
| تاریخ دریافت: 12 دی 1404، تاریخ بازنگری: 22 بهمن 1404، تاریخ پذیرش: 26 اسفند 1404 | ||
| چکیده | ||
| This study introduces a new statistical modeling approach for efficient parameter estimation and evaluates its performance in comparison with several existing estimation models. The proposed estimator is developed to overcome efficiency limitations commonly observed in classical methods, particularly in environmental data analysis. Theoretical properties of the proposed model, including unbiasedness, consistency, and asymptotic efficiency, are established under mild regularity conditions. A comprehensive simulation study is conducted to assess the finite-sample behavior of the estimator across different sample sizes and parameter configurations. The results demonstrate that the proposed model consistently achieves lower bias and mean squared error relative to competing estimators. To illustrate its practical applicability, the methodology is applied to a real air quality dataset obtained from the UCI Machine Learning Repository, involving hourly pollution and meteorological measurements. The empirical findings support the theoretical results and confirm the superior efficiency and robustness of the proposed estimation model for environmental data modeling. | ||
| کلیدواژهها | ||
| Efficient estimation؛ Statistical modeling؛ Environmental data analysis؛ Air quality modeling؛ Simulation study | ||
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آمار تعداد مشاهده مقاله: 13 |
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