Annals of Data Science https://doi.org/10.1007/s40745-023-00492-2 Shrinkage Estimation for Location and Scale Parameters of Logistic Distribution Under Record Values Shubham Gupta 1 · Gajendra K. Vishwakarma 1 · A. M. Elsawah 2,3,4 Received: 13 November 2022 / Revised: 18 July 2023 / Accepted: 26 July 2023 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023 Abstract Logistic distribution (LogDis) is frequently used in many different applications, such as logistic regression, logit models, classification, neural networks, physical sciences, sports modeling, finance and health and disease studies. For instance, the distribution function of the LogDis has the same functional form as the derivative of the Fermi function that can be used to set the relative weight of various electron energies in their contributions to electron transport. The LogDis has wider tails than a normal distri- bution (NorDis), so it is more consistent with the underlying data and provides better insight into the likelihood of extreme events. For this reason the United States Chess Federation has switched its formula for calculating chess ratings from the NorDis to the LogDis. The outcomes of many real-life experiments are sequences of record- breaking data sets, where only observations that exceed (or only those that fall below) the current extreme value are recorded. The practice demonstrated that the widely used estimators of the scale and location parameters of logistic record values, such as the best linear unbiased estimators (BLUEs), have some defects. This paper investi- gates the shrinkage estimators of the location and scale parameters for logistic record values using prior information about their BLUEs. Theoretical and computational B Gajendra K. Vishwakarma vishwagk@rediffmail.com Shubham Gupta shbm.asiwan@gmail.com A. M. Elsawah amelsawah@uic.edu.cn 1 Department of Mathematics & Computing, Indian Institute of Technology, Dhanbad 826004, India 2 Department of Statistics and Data Science, Faculty of Science and Technology, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai 519087, China 3 Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College, Zhuhai 519087, China 4 Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt 123 Content courtesy of Springer Nature, terms of use apply. Rights reserved.