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                <text>Faculty Publications</text>
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              <text>Jeevanand, E.S.; Swapna, V.; Prabhu, Sowbhagya S.; Rebello, Joseph Justin</text>
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          <name>Title</name>
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              <text>SHRINKAGE ESTIMATION OF THE TTT OF THE LOMAX DISTRIBUTION UNDER PROGRESSIVE TYPE II CENSORING</text>
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          <name>Date</name>
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              <text>01-01-2026</text>
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            <elementText elementTextId="222041">
              <text>Far East Journal of Theoretical Statistics;Volume;70;Issue;1;pp.1-16</text>
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              <text>&lt;a href="https://doi.org/10.17654/0972086326001" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.17654/0972086326001&lt;/a&gt; &lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/pages/publications/105033242167?origin=resultslist" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/pages/publications/105033242167?origin=resultslist&lt;/a&gt;</text>
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              <text>Jeevanand E.S., Department of Statistics and Data Science, Christ University, Karnataka, Bengaluru, 560029, India; Swapna V., Department of Statistics and Data Science, Christ University, Karnataka, Bengaluru, 560029, India; Prabhu S.S., Department of Statistics, Rajagiri College of Social Sciences, Kerala, India; Rebello J.J., St. Alberts College (Autonomous), Kerala, Ernakulam, 682018, India</text>
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              <text>Shrinkage estimation is a robust procedure that integrates an unbiased estimate with the current estimate using a weighted factor. Several weighted factors dynamically adjust the shrinkage factor based on the sample size and censoring proportion, ensuring that the method tailors the shrinkage to the available data rather than applying a constant factor. Despite certain real-world application limitations, the Lomax distribution exhibits flexibility as a tail-behaviour modelling technique, making it particularly suitable for reliability and survival analysis. The present study focuses on estimating the shape parameter of the Lomax distribution using PTII censored sampling within the framework of the shrinkage estimator method. Shrinkage estimators are derived based on constant and shrinkage weight factors under different weight functions. These depend on the base estimates sample size, bias, and variance to shrink the current estimate. The performance of these estimators is rigorously evaluated through a Monte Carlo simulation study.  2026 Pushpa Publishing House, Prayagraj, India.</text>
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              <text>Lomax distribution; Progressive Type II (PTII) censoring; shrinkage estimation; Time to Test (TTT)</text>
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          <name>Publisher</name>
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              <text>Pushpa Publishing House</text>
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              <text>ISSN: 9720863;</text>
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              <text>All Open Access; Hybrid Gold Open Access</text>
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