Discriminating between the log-normal and generalized exponential distributions |
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| Institution: | 1. Department of Mathematics, Indian Institute of Technology Kanpur 208016, India;2. Department of Computer Science and Applied Statistics, University of New Brunswick, Saint Jonh, Canada E2L 4L5;1. Università della Calabria, 87036 Rende, Italy;2. Universidad San Francisco de Quito, EC170157 Quito, Ecuador;3. imec, 3001 Leuven, Belgium;4. NXP Semiconductors, 5656 AE Eindhoven, Netherlands;1. Department of Statistical Sciences, University of Padua, 35121 Padova, Italy;2. Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada, N2L 3G1;3. CEMSE Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia;4. Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada, L8S 4L8 |
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| Abstract: | The two-parameter generalized exponential distribution was recently introduced by Gupta and Kundu (Austral. New Zealand J. Statist. 40 (1999) 173). It is observed that the Generalized Exponential distribution can be used quite effectively to analyze skewed data set as an alternative to the more popular log-normal distribution. In this paper, we use the ratio of the maximized likelihoods in choosing between the log-normal and generalized exponential distributions. We obtain asymptotic distributions of the logarithm of the ratio of the maximized likelihoods and use them to determine the required sample size to discriminate between the two distributions for a user specified probability of correct selection and tolerance limit. |
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