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Odd Half Logistic Chen Distribution for Analyzing Air Quality Data in Kathmandu, Nepal |
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PP: 45-58 |
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doi:10.18576/jsap/140104
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Author(s) |
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Ramesh Prasad Tharu,
Govinda Prasad Dhungana,
Ramesh Kumar Joshi,
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Abstract |
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| This research paper presents a novel statistical model, namely the Odd Half Logistic Chen Distribution, which combines the continuous Chen distribution with the half logistic-G family of distribution. Where,α and λ are scale parameters and β is the shape parameter. The model exhibits unimodal characteristics with a negative skewness, while the hazard rate function displays an increasing J-shaped pattern. The study derives explicit expressions for various important statistical functions, including the reliability/survival function, hazard rate function, revised hazard rate function, cumulative hazard rate function, quantile function, mode function, and order statistics. The maximum likelihood estimation method is employed to estimate the model parameters and a simulation study confirms the efficiency of the approach as the sample size increases, indicated by the decreasing mean squared errors of the individual parameters. Furthermore, the proposed model is applied to a real-world air quality data analysis in Kathmandu Valley. The finding reveals that residents of the area experience only seven days of fresh air per month, highlighting the severity of the air pollution problem. The model is validated through graphical techniques such as P-P plot, Q-Q plot, estimated cumulative distribution function (CDF) with empirical distribution and numerical tests including the Kolmogorov-Smirnov (KS) test, Anderson Darling test and Crame ́r–von Mises test. The parameter estimation, model validation and statistical analysis are performed using R programming. Therefore, the proposed model emerges as a promising alternative for predicting air quality data and performing reliability analysis in various domains.
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