On Positive Inflated Geometric Distribution: Properties and Applications

One-inflation in zero-truncated count data has recently found considerable attention. In this regard, zero-truncated Geometric distribution and distribution to a point mass at one are used to create a one-inflated model, namely, one-inflated zero-truncated Geometric distribution. Its reliability characteristics, generating functions, and distributional properties are investigated in detail, which includes survival function, hazard rate function, reverse hazard rate function, probability generating function, characteristic function, variance, skewness, and kurtosis. Monte Carlo simulation have been undertaken to evaluate the effectiveness of the maximum likelihood estimators. To test the compatibility of our proposed model, the baseline model and the proposed model are distinguished by using the two different test procedures. The adaptability of the suggested model is demonstrated using two real-life datasets from separate domains by taking various performance measures into consideration.

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Publication Details

Journal
Austrian Journal of Statistics
Published
2024-09-16
DOI
https://doi.org/10.17713/ajs.v53i4.1818
Citations
1
Primary Topic
Advanced Statistical Methods and Models
Type
article
Field-Weighted Citation Impact
0.71
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article

On Positive Inflated Geometric Distribution: Properties and Applications

Muneeb Ahmad Wani, Zehra Skinder, Peer Bilal Peer
1 citations
Austrian Journal of Statistics
Advanced Statistical Methods and Models
0.71
article

On Positive Inflated Geometric Distribution: Properties and Applications

Muneeb Ahmad Wani, Zehra Skinder, Peer Bilal Peer
article en
1 citations

Abstract

One-inflation in zero-truncated count data has recently found considerable attention. In this regard, zero-truncated Geometric distribution and distribution to a point mass at one are used to create a one-inflated model, namely, one-inflated zero-truncated Geometric distribution. Its reliability characteristics, generating functions, and distributional properties are investigated in detail, which includes survival function, hazard rate function, reverse hazard rate function, probability generating function, characteristic function, variance, skewness, and kurtosis. Monte Carlo simulation have been undertaken to evaluate the effectiveness of the maximum likelihood estimators. To test the compatibility of our proposed model, the baseline model and the proposed model are distinguished by using the two different test procedures. The adaptability of the suggested model is demonstrated using two real-life datasets from separate domains by taking various performance measures into consideration.

Austrian Journal of StatisticsVol. 53(4)
Islamic University of Science and Technology (IN)
Openalex Percentile: Top 26%
Advanced Statistical Methods and Models
0.71
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