Modeling life time data with new alpha power pareto transformation distribution
Abstract views: 20 / PDF downloads: 2
Keywords:
Pareto distribution, survival function, alpha power transformationAbstract
The need arose to find composite, expanded, or transformed distributions that would be more flexible in representing data using different statistical methods. This research includes a study of the proposed distribution resulting from alpha energy conversion. Using the survival function of the Pareto distribution to obtain the new distribution is the alpha power Pareto transformed (APTP) distribution. The new distribution provides more outstanding suitability than the Pareto and transformed Pareto distributions. Some statistics properties of the proposed distribution, such as quantile function, moments, and survival reliability risk and demand statistics are discussed. The maximum likelihood was used to estimate the parameter, and theactual data sets were used to see how the new distribution performs better in real life.
References
Bourguignon, M., Silva, R. B., Zea, L. M., & Cordeiro, G. M. (2013). The Kumaraswamy Pareto distribution. Journal of Statistical Theory and Applications, 12(2), 129–144.
Merovci, F., & Puka, L. (2014, January). Transmuted Pareto distribution. ProbStat Forum, 7(1), 1–11.
Tahir, M. H., Cordeiro, G. M., Alzaatreh, A., Mansoor, M., & Zubair, M. (2016). A new Weibull–Pareto distribution: Properties and applications. Communications in Statistics-Simulation and Computation, 45(10), 3548–3567.
Tahir, A., & Akhter, A. S. (2018). Transmuted new Weibull-Pareto distribution and its applications. Applications and Applied Mathematics: An International Journal (AAM), 13(1), 3.
Ihtisham, S., Khalil, A., Manzoor, S., Khan, S. A., & Ali, A. (2019). Alpha-Power Pareto distribution: Its properties and applications. PLOS ONE, 14(6), e0218027.
Lee, S., & Kim, J. H. (2019). Exponentiated generalized Pareto distribution: Properties and applications towards extreme value theory. Communications in Statistics-Theory and Methods, 48(8), 2014–2038.
Prasath, C. A. (2024). Cutting-edge developments in artificial intelligence for autonomous systems. Innovative Reviews in Engineering and Science, 1(1), 11–15.
Al-Kadim, K. A., & Mahdi, A. A. (2018). Exponentiated Transmuted Exponential Distribution. Journal of Babylon University/Pure and Applied Sciences, 26(2).
Abdullah, D. (2024). Enhancing cybersecurity in electronic communication systems: New approaches and technologies. Progress in Electronics and Communication Engineering, 1(1), 38–43.
Muralidharan, J. (2024). Optimization techniques for energy-efficient RF power amplifiers in wireless communication systems. SCCTS Journal of Embedded Systems Design and Applications, 1(1), 1–5.
Dhivya, S., Iswariyalakshmi, B., Banumathi, V., Gayathri, S., & Meyanand, R. (2017). Image integration with local linear model using demosaicing algorithm. International Journal of Communication and Computer Technologies, 5(1), 36–42.
Sadulla, S. (2024). State-of-the-art techniques in environmental monitoring and assessment. Innovative Reviews in Engineering and Science, 1(1), 25–29.
Muralidharan, J. (2024). Machine learning techniques for anomaly detection in smart IoT sensor networks. Journal of Wireless Sensor Networks and IoT, 1(1), 10–14.