Mean and variance of ratios of proportions from categories of a multinomial distribution
Abstract Ratio distribution is a probability distribution representing the ratio of two random variables, each usually having a known distribution. Currently, there are results when the random variables in the ratio follow (not necessarily the same) Gaussian, Cauchy, binomial or uniform distribution...
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Online Access: | http://link.springer.com/article/10.1186/s40488-018-0083-x |
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doaj-7be8ed3db6bf49b2a3d08f6ee34bae3a2020-11-24T21:39:53ZengSpringerOpenJournal of Statistical Distributions and Applications2195-58322018-01-015112010.1186/s40488-018-0083-xMean and variance of ratios of proportions from categories of a multinomial distributionFrantisek Duris0Juraj Gazdarica1Iveta Gazdaricova2Lucia Strieskova3Jaroslav Budis4Jan Turna5Tomas Szemes6Geneton s.r.o.Comenius University, Faculty of Natural SciencesComenius University, Faculty of Natural SciencesComenius University, Faculty of Natural SciencesComenius University Faculty of Mathematics, Physics and InformaticsComenius University, Science ParkGeneton s.r.o.Abstract Ratio distribution is a probability distribution representing the ratio of two random variables, each usually having a known distribution. Currently, there are results when the random variables in the ratio follow (not necessarily the same) Gaussian, Cauchy, binomial or uniform distributions. In this paper we consider a case, where the random variables in the ratio are joint binomial components of a multinomial distribution. We derived formulae for mean and variance of this ratio distribution using a simple Taylor-series approach and also a more complex approach which uses a slight modification of the original ratio. We showed that the more complex approach yields better results with simulated data. The presented results can be directly applied in the computation of confidence intervals for ratios of multinomial proportions. AMS Subject Classification: 62E20http://link.springer.com/article/10.1186/s40488-018-0083-xMultinomial distributionRatio distributionMeanVariance |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Frantisek Duris Juraj Gazdarica Iveta Gazdaricova Lucia Strieskova Jaroslav Budis Jan Turna Tomas Szemes |
spellingShingle |
Frantisek Duris Juraj Gazdarica Iveta Gazdaricova Lucia Strieskova Jaroslav Budis Jan Turna Tomas Szemes Mean and variance of ratios of proportions from categories of a multinomial distribution Journal of Statistical Distributions and Applications Multinomial distribution Ratio distribution Mean Variance |
author_facet |
Frantisek Duris Juraj Gazdarica Iveta Gazdaricova Lucia Strieskova Jaroslav Budis Jan Turna Tomas Szemes |
author_sort |
Frantisek Duris |
title |
Mean and variance of ratios of proportions from categories of a multinomial distribution |
title_short |
Mean and variance of ratios of proportions from categories of a multinomial distribution |
title_full |
Mean and variance of ratios of proportions from categories of a multinomial distribution |
title_fullStr |
Mean and variance of ratios of proportions from categories of a multinomial distribution |
title_full_unstemmed |
Mean and variance of ratios of proportions from categories of a multinomial distribution |
title_sort |
mean and variance of ratios of proportions from categories of a multinomial distribution |
publisher |
SpringerOpen |
series |
Journal of Statistical Distributions and Applications |
issn |
2195-5832 |
publishDate |
2018-01-01 |
description |
Abstract Ratio distribution is a probability distribution representing the ratio of two random variables, each usually having a known distribution. Currently, there are results when the random variables in the ratio follow (not necessarily the same) Gaussian, Cauchy, binomial or uniform distributions. In this paper we consider a case, where the random variables in the ratio are joint binomial components of a multinomial distribution. We derived formulae for mean and variance of this ratio distribution using a simple Taylor-series approach and also a more complex approach which uses a slight modification of the original ratio. We showed that the more complex approach yields better results with simulated data. The presented results can be directly applied in the computation of confidence intervals for ratios of multinomial proportions. AMS Subject Classification: 62E20 |
topic |
Multinomial distribution Ratio distribution Mean Variance |
url |
http://link.springer.com/article/10.1186/s40488-018-0083-x |
work_keys_str_mv |
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