A generalized inverse trinomial distribution with application

Q Mathematics Statistical Methodology Pub Date : 2016-12-01 DOI:10.1016/j.stamet.2016.10.001
Shin Zhu Sim , Seng Huat Ong
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引用次数: 6

Abstract

This paper considers a particular generalized inverse trinomial distribution which may be regarded as the convolution of binomial and negative distributions for the statistical analysis of count data. This distribution has the flexibility to cater for under-, equi- and over-dispersion in the data. Some basic and probabilistic properties and tail approximation of the distribution have been derived. Conditions for the numerical stability of the two-term probability recurrence formula have also been examined to facilitate computation. For the purpose of statistical analysis, test of hypothesis for equi-dispersion by the score and likelihood ratio tests and simulation study of their power, parameter estimation by maximum likelihood and a probability generating function based methods have been considered. The versatility of the distribution is illustrated by its application to real biological data sets which exhibit under and over dispersion. It is shown that the distribution fits better than the well-known generalized Poisson and COM-Poisson distributions.

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广义逆三叉分布及其应用
本文考虑一种特殊的广义逆三叉分布,它可以看作是二项分布和负分布的卷积,用于计数数据的统计分析。这种分布具有灵活性,可以满足数据的低分散、均匀分散和过度分散。推导了该分布的一些基本性质和概率性质以及尾部近似。为了便于计算,还研究了两项概率递推公式的数值稳定性条件。为了进行统计分析,考虑了分数比检验和似然比检验对等离散性的假设检验及其功率的模拟研究、最大似然法参数估计和基于概率生成函数的方法。该分布的多功能性通过其在实际生物数据集上的应用来说明,这些数据集表现出过分散和过分散。结果表明,该分布比众所周知的广义泊松分布和com -泊松分布拟合得更好。
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来源期刊
Statistical Methodology
Statistical Methodology STATISTICS & PROBABILITY-
CiteScore
0.59
自引率
0.00%
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0
期刊介绍: Statistical Methodology aims to publish articles of high quality reflecting the varied facets of contemporary statistical theory as well as of significant applications. In addition to helping to stimulate research, the journal intends to bring about interactions among statisticians and scientists in other disciplines broadly interested in statistical methodology. The journal focuses on traditional areas such as statistical inference, multivariate analysis, design of experiments, sampling theory, regression analysis, re-sampling methods, time series, nonparametric statistics, etc., and also gives special emphasis to established as well as emerging applied areas.
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