Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Anupama, S.; Azarudheen, S.; Gogi, Vyshali J. Title A name given to the resource A Penalized Maximum Likelihood Estimation for the Log-Logistic Distribution with Complete Data Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Communications in Computer and Information Science;Volume;2853 CCIS;pp.313-323 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-95-7292-2_24" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-95-7292-2_24</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105038784152?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038784152?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Anupama S., Department of Data Science and Statistics, CHRIST University, Bengaluru, India; Azarudheen S., Department of Data Science and Statistics, CHRIST University, Bengaluru, India; Gogi V.J., Department of Data Science and Statistics, CHRIST University, Bengaluru, India Description An account of the resource Penalized maximum likelihood estimation is specified for estimating parameters of a log-logistic distribution for complete-data situations. This approach addresses the issues of Maximum Likelihood Estimation, wherein Maximum Likelihood Estimation is often unstable when sample sizes are small, and fails with heavy-tailed or asymmetric data. By adding a ridge penalty to the log-likelihood, we derive new score equations, which are solved numerically. The performance is measured for a variety of shape and scale parameters and sample sizes, with bias and Mean Squared Error as the two main measures. The simulation experiment results indicate Penalized maximum likelihood estimation consistently achieves lower bias and Mean Square Error with small sample sizes and particularly strong improvements under skewed or heavy-tailed data. With larger sample size, the differences between Maximum Likelihood Estimation and Penalized maximum likelihood estimation decrease, as we would expect. These results suggest that Penalized maximum likelihood estimation is a viable estimation method using the log-logistic distribution, especially with small or limited datasets. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource log-logistic distribution; parameter estimation; penalized maximum likelihood estimation; ridge penalty; simulation study Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 18650929; ISBN: 978-981957291-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online