This paper proposes a new distribution named the negative binomial-weighted Lindley. The study uses the maximum likelihood estimation to estimate the parameters of the proposed distribution and compares the performance of the new method with other distributions. | The negative binomial-weighted Lindley distribution Decision Science Letters 8 2019 317 322 Contents lists available at GrowingScience Decision Science Letters homepage dsl The negative binomial-weighted Lindley distribution Sunthree Denthet and Pramoch Promin College of Industrial Technology King Mongkut s University of Technology North Thailand CHRONICLE ABSTRACT Article history This paper proposes a new distribution named the negative binomial-weighted Lindley. The Received October 9 2018 study uses the maximum likelihood estimation to estimate the parameters of the proposed Received in revised format distribution and compares the performance of the new method with other distributions. The study October 10 2018 finds that the negative binomial-weighted Lindley distribution obtained by the mixing the Accepted November 11 2018 Available online negative binomial distribution with the weighted Lindley distribution is another mixed negative November 11 2018 binomial distribution and may provide an appropriate fit for data estimation with overdispersion. Keywords Some characteristics of the proposed distribution such as mean and variance are also derived. Count data analysis Mixed negative binomial distribution Weighted Lindley distribution 2018 by the authors licensee Growing Science Canada. 1. Introduction A count data distribution is only non-negative integers in its domain. We typically use the count data distribution to model the number of occurrences of a certain event. The Poisson and negative binomial NB distributions are the count data distribution for examples. The standard distribution for modeling count data has been the Poisson distribution which is a proper model for counting the number of occurrences over a time interval at random when not many occurrences are observed within a short period of time. They occur at a constant rate through time and one occurrence of the phenomenon does not alter the probability of any future .
