A Bimodal Extension of the Log-Normal Distribution on the Real Line with an Application to DNA Microarray Data

Mai F. Alfahad, Mohamed E. Ghitany (), Ahmad N. Alothman and Saralees Nadarajah
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Mai F. Alfahad: Department of Statistics and Operations Research, Faculty of Science, Kuwait University, Kuwait City 13060, Kuwait
Mohamed E. Ghitany: Department of Statistics and Operations Research, Faculty of Science, Kuwait University, Kuwait City 13060, Kuwait
Ahmad N. Alothman: Department of Statistics and Operations Research, Faculty of Science, Kuwait University, Kuwait City 13060, Kuwait
Saralees Nadarajah: Department of Mathematics, University of Manchester, Manchester M13 9PL, UK

Mathematics, 2023, vol. 11, issue 15, 1-17

Abstract: A bimodal double log-normal distribution on the real line is proposed using the random sign mixture transform. Its associated statistical inferences are derived. Its parameters are estimated by the maximum likelihood method. The performance of the estimators and the corresponding confidence intervals is checked by simulation studies. Application of the proposed distribution to a real data set from a DNA microarray is presented.

Keywords: bimodality; log-normal distribution; maximum likelihood estimation; Monte Carlo simulations (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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