Analysis of Household Income, Expenditure and Consumption Survey Research Data for North Sinai Governorate in Egypt Using Length Biased Truncated Lomax Distribution
Abstract
The length biased truncated Lomax distribution is introduced in this study as a weighted form of the truncated Lomax distribution. The length biased truncated Lomax distribution’s essential distributional features are investigated. In the case of complete and type-II censored data, the maximum likelihood method is provided for estimating population parameter. The model parameter asymptotic confidence interval is calculated. To demonstrate the pattern of the estimate, a sample generation algorithm is supplied, as well as a Monte Carlo simulation analysis. We can see from the simulation research that as the censoring level is increased, the mean squared error of parameter estimates decrease’s for all given values. With increasing sample size, the mean squared error and average length of parameter estimates decrease. The estimates get increasingly accurate as the sample size grows higher, suggesting that its asymptotically unbiased. Furthermore, in all cases, the mean squared error diminishes as the sample size grows, indicating that the estimates of parameter are consistent. Modelling to medical data and the percentage of household spending on education out of total household expenditure from the household income, expenditure and consumption survey (HIECS) data are used to show the importance of the new model. The Kumaraswamy, beta, truncated power Lomax, truncated Weibull, and one parameter-beta distributions perform poorly in comparison to the suggested distribution.References
M. V. Aarset, How to identify a bathtub hazard rate, IEEE Transactions on Reliability, vol. 36, no. 1, pp. 106–108,
I. Abdul-Moniem, and L. S. Diab, The length-biased weighted exponentiated Lomax distribution, International
Journal for Research in Mathematics and Statistics, vol. 4, no. 1, pp. 1–14, 2018.
A.A. H. Ahmadini, A. S. Hassan, M. Elgarhy, M. Elsehetry, S. S. Alshqaq, and S. G. Nassr, Truncated Lomax inverse
Lomax distribution with applications, Intelligent Automation & Soft Computing, vol. 29, no. 1, pp199–212, 2021.
A. Ahmed, S. P. Ahmed, and A. Ahmed, Length-biased weighted Lomax distribution: statistical properties and
application, Pakistan Journal of Statistics and Operation Research, vol. 12, no. 2, pp. 245–255, 2016.
S. I. Ansari, H. Rezk, and H. M. Yousof, A new compound version of the generalized Lomax distribution for modeling
failure and service times, Pakistan Journal of Statistics and Operation Research, vol. 16, no. 1, pp. 95–107, 2020.
S. Arimoto, Information-theoretical considerations on estimation problems, Information and Control, vol. 19, no. 3,
pp. 181–194, 1971.
A. B. Atkinson and A. J. Harrison, Distribution of Personal Wealth in Britain, Cambridge University Press, 1978.
A. A. Al-Babtain, A. S. Hassan, A. N. Zaky, I. Elbatal, and M. Elgarhy, Dynamic cumulative residual Rényi entropy
for Lomax distribution: Bayesian and non-Bayesian methods, AIMS Mathematics, vol. 6, no. 4, pp 3889–3914, 2021.
R. Bantan, A. S. Hassan, M. Elgarhy, F. Jamal, C. Chenseau and M. Elsehetry, Bayesian analysis in partially
accelerated life tests for weighted Lomax distribution, CMC-Computer Materials and Continua, vol. 68, no. 3, pp.
–2875, 2021.
Y. M. Bulut, F. Z. Dogru, and O. Arslan, Alpha power Lomax distribution: properties and application, Journal of
Reliability and Statistical Studies, vol. 14, no. 1, pp. 17–32, 2021.
A. Corbellini, L. Crosato, P. Ganugi, and M. Mazzoli, Fitting Pareto II distributions on firm size: Statistical
methodology and economic puzzles, In Skiadas C. (eds) Advances in Data Analysis.Statistics for Industry and
Technology, pp. 321–328, 2010.
G. M. Cordeiro, E. M. M. Ortega and B. V. Popovic, The gamma-Lomax distribution, Journal of Statistical
Computation and Simulation, vol. 85, no. 2, pp. 305–319, 2015.
R. C. Gupta, and J. P. Keating, Relations for reliability measures under length biased sampling, Scandinavian Journal
of Statistics, pp. 49–56, 1986.
C. M. Harris, The Pareto distribution as a queue service discipline, Operations Research, vol. 16, no. 2, pp. 307–313,
A. S. Hassan and A. Al-Ghamdi, Optimum step stress accelerated life testing for Lomax distribution, Journal of
Applied Sciences Research, vol. 5, no. 12, pp. 2153–2164, 2009.
A. S. Hassan, M. A. Sabry, and A. M. Elsehetry, A new family of upper-truncated distributions: properties and
estimation, Thailand Statistician, vol. 18, no. 2, pp. 196–214, 2020.
J. Havrda, and F. Charvát, Quantification method of classification processes, Concept of Structural -Entropy.
Kybernetika, vol. 3, pp. 30–35, 1967.
O. Holland, A. Golaup, and A. Aghvami, Traffic characteristics of aggregated module downloads for mobile terminal
reconfiguration, IEE Proceedings-Communications, vol. 153, no. 5, pp. 683–690, 2006.
A. Hurairah, and A. Alabid, Beta transmuted Lomax distribution with applications, STATISTICS IN TRANSITION
new series, vol. 21, no. 2, pp. 13–34, 2020.
J. P. Klein, and M. L. Moeschberger, Survival Analysis: Techniques for Censored and Truncated Data, Springer:
Berlin/Heidelberg, Germany, 2006.
A. J. Lemonte, and G. M. Cordeiro, An extended Lomax distribution, Statistics, vol. 47, no. 4, pp. 800–816, 2013.
B. O. Oluyede, On inequalities and selection of experiments for length biased distributions, Probability in the
Engineering and Informational Sciences, vol. 13, no. 2, pp. 169–185, 1999.
G. P. Patil, and J. Ord, On size-biased sampling and related form-invariant weighted distributions, Sankhyā: The
Indian Journal of Statistics, Series B, vol. 38, pp. 48–61, 1976.
G. P. Patil, and C. R. Rao, Weighted distributions and size-biased sampling with applications to wildlife populations
and human families, Biometrics, vol. 34, pp. 179–189, 1978.
E. H. A. Rady, W. Hassanein, and T. Elhaddad, The power Lomax distribution with an application to bladder cancer
data, SpringerPlus, vol. 5, no. 1, pp. 1-22, 2016.
A. Rényi, On measures of information and entropy, in Proceedings of the 4th Berkeley Symposium on Mathematics,
SpringerPlus, pp. 547–561, 1960.
M. H. Tahir, G. M. Cordeiro, M. Mansoor, and M. Zubair, The Weibull-Lomax distribution: properties and applications,
Biometrika, Vol. 44, no. 2, pp. 461–480, 2015.
C. Tsallis, The role of constraints within generalized nonextensive statistics, Physica, vol. 261, pp. 547–561, 1998.
M. Zelen, and M. Feinleib, On the theory of screening for chronic disease, Biometrika, vol. 56, pp. 601–614, 1969.
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