THE WEIBULL INVERSE POWER LOMAX DISTRIBUTION WITH ITS APPLICATIONS TO RELIABILITY DATA

O. I. Oseghale; P. A. Azor

O. I. Oseghale Department of Mathematics and Statistics, Federal University Otuoke, Bayelsa State.
P. A. Azor Department of Mathematics and Statistics, Federal University Otuoke, Bayelsa State.

Keywords: By compounding the Weibull-g family with the Inverse Power Lomax distribution, we were able to create a useful lifetime model known as the Weibull Inverse Power Lomax distribution (WIPLD). Its statistical features were derived using the mixture representation. Properties, namely, quantile, density, reliability, and hazard functions were identified as statistical features. Additional metrics that are determined include the mean, median, moments, incomplete moments, characteristic function, Bonferroni curve, Lorenz curve, stress-strength reliability, Rényi entropy, moment generating function, order statistics, and Tsallis entropy. To estimate the model's parameters, the maximum likelihood technique is applied. WIPLD is superior in terms of application and usability in modeling real-life data, as demonstrated by using two lifetime data sets. The study's information criteria are used to determine the model's goodness-of-fit, and the findings indicate that the model provides the best fit for the actual data sets. Keywords: Weibull Inverse Power Lomax distribution; incomplete moments; Tsallis entropy; Moments.

Abstract

By compounding the Weibull-g family with the Inverse Power Lomax distribution, we were able to create a useful lifetime model known as the Weibull Inverse Power Lomax distribution (WIPLD). Its statistical features were derived using the mixture representation. Properties, namely, quantile, density, reliability, and hazard functions were identified as statistical features. Additional metrics that are determined include the mean, median, moments, incomplete moments, characteristic function, Bonferroni curve, Lorenz curve, stress-strength reliability, Rényi entropy, moment generating function, order statistics, and Tsallis entropy. To estimate the model's parameters, the maximum likelihood technique is applied. WIPLD is superior in terms of application and usability in modeling real-life data, as demonstrated by using two lifetime data sets. The study's information criteria are used to determine the model's goodness-of-fit, and the findings indicate that the model provides the best fit for the actual data sets.

Keywords: Weibull Inverse Power Lomax distribution; incomplete moments; Tsallis entropy; Moments.

Author Biographies

O. I. Oseghale, Department of Mathematics and Statistics, Federal University Otuoke, Bayelsa State.

Department of Mathematics and Statistics, Federal University Otuoke, Bayelsa State.

P. A. Azor, Department of Mathematics and Statistics, Federal University Otuoke, Bayelsa State.

Department of Mathematics and Statistics, Federal University Otuoke, Bayelsa State.