A new reduced quantile function for generating families of distributions
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Abstract
In this paper, a variant of the T-X(Y) generator was developed by suppressing the scale parameter of the classical Lomax distribution in the quantile function. Uniquely, the reduction of the number of parameters essentially accounts for the parsimony of the attendant model. The study considered the Exponential distribution as the transformer and consequently obtained the New Reduced Quantile Exponential-G (NRQE-G) family. The Type-II Gumbel distribution was deployed as the baseline to obtain a special sub-model known as the New Reduced Quantile Exponential Type-II Gumbel (NRQE-T2G) model. Some functional properties of the distribution namely, moment and its related measures such as the mean, variance, second, third, and fourth moments were obtained. The Mode, skewness, Kurtosis, index of dispersion, coefficient of variation, order statistics, survival, hazard, and quantile function were also derived. The maximum likelihood estimation method was used to estimate its parameters. The model's credibility, applicability, and flexibility were proven using two real-life datasets.
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Lindley DV. Fiducial distributions and Bayes’ theorem. In: Journal of the Royal Statistical Society. Series B 1958, 102–107.
Onyekwere CK, Obulezi OJ. Chris-Jerry distribution and its applications. In: Asian Journal of Probability and Statistics 2022; 20:16–30.
Onyekwere CK, Okoro CN, Obulezi OJ, Udofia EM, Anabike IC. Modification of Shanker distribution using quadratic rank transmutation map. In: Journal of Xidian University. 2022; 16: 179–198.
Tolba AH, Onyekwere CK, El-Saeed AR, Alsadat N, Alohali H, Obulezi OJ. A New Distribution for Modeling Data with Increasing Hazard Rate: A Case of COVID-19 Pandemic and Vinyl Chloride Data. In: Sustainability. 2023; 15: 12782.
Etaga HO, Celestine EC, Onyekwere CK, Omeje IL, Nwankwo MP, Oramulu DO, Obulezi OJ. A new modification of Shanker distribution with applications to increasing failure rate data. In: Earthline Journal of Mathematical Sciences 2023; 13: 509–526.
Obulezi OJ, Anabike IC, Okoye GC, Igbokwe CP, Etaga HO, Onyekwere CK. The Kumaraswamy Chris-Jerry Distribution and its Applications. In: Journal of Xidian University. 2023; 17: 575–591.
Pearson K. X. Contributions to the mathematical theory of evolution.—II. Skew variation in homogeneous material. In: Philosophical Transactions of the Royal Society of London. (A.) 1895: 186: 343-414.
McDonald JB. Some generalized functions for the size distribution of income. In: Modeling income distributions and Lorenz curves. Springer. 2008; 37–55.
McDonald JB, Xu YJ. A generalization of the beta distribution with applications” In: Journal of Econometrics. 1995; 66:133–152.
Azzalini A. A class of distributions which includes the normal ones. In: Scandinavian journal of statistics. 1985; 171–178.
Kotz S, Vicari D, Survey of developments in the theory of continuous skewed distributions. In: Metron. 2005; 63: 225–261.
Ferreira JTAS, Steel MFJ. A constructive representation of univariate skewed distributions. In: Journal of the American Statistical Association. 2006; 101: 823–829.
Marshall AW, Olkin I. A new method for adding a parameter to a family of distributions with application to the exponential and Weibull families. In: Biometrika. 1997; 84: 641–652.
Eugene N, Lee C, Famoye F. Beta-normal distribution and its applications. In: Communications in Statistics-Theory and methods. 2002; 31: 497–512.
Alzaatreh A, Lee C, Famoye F. A new method for generating families of continuous distributions. In: Metron. 2013; 71: 63-79.
Aljarrah MA, Lee C, Famoye F. On generating TX family of distributions using quantile functions. In: Journal of Statistical Distributions and Applications. 2014; 1: 1-17.
Shorack GR, Wellner JA. Empirical processes with applications to statistics. SIAM. 2009.
Lomax KS. Business failures: Another example of the analysis of failure data”. In: Journal of the American statistical association. 1954; 49: 847–852.
Johnson NL. Kotz S. Continuous Univariate Distributions I”. 1970.
Mansour MM, Butt NS, Ansari SI, Yousof HM, Ali MM, Ibrahim M. A new exponentiated Weibull distribution’s extension: copula, mathematical properties and applications. In: Contributions to Mathematics. 2020; 57–66.
Kargbo M, Gichuhi AW, Wanjoya AK. A novel extended inverseexponential distribution and its application to COVID-19 data. In: Engineering Reports. 2023; e12828.
Weibull W. A statistical distribution function of wide applicability. In: Journal of applied mechanics. 1951.
Dey S, Dey T. Generalized inverted exponential distribution: Different methods of estimation. In: American Journal of Mathematical and Management Sciences. 2014; 33: 194-215.
Wilson EB, Worcester J. The normal logarithmic transform. In: The Review of Economics and Statistics. 1945: 17-22.
Laplace PS. Analytical probability theory. Courcier. 1814.
Lancaster HO. Forerunners of the Pearson χ2. In: Australian Journal of Statistics. 1966; 8: 117–126.
Cordeiro GM, Ortega EMM, Nadarajah S. The Kumaraswamy Weibull distribution with application to failure data. In: Journal of the Franklin Institute 2010; 347: 1399-1429.
Aijaz A, Qurat ul Ain S, Ahmad A, Tripathi R. An Extension of Erlang Distribution with Properties Having Applications In Engineering and Medical-Science. Int J Open Problems Compt Math. 2021; 14.
Oramulu DO, Igbokwe CP, Anabike IC, Etaga HO, Obulezi OJ. Simulation study of the Bayesian and non-Bayesian estimation of a new lifetime distribution parameters with increasing hazard rate. In: Asian Research Journal of Mathematics. 2023; 19:183-211.
Omoruyi FA, Omeje IL, Anabike IC, Obulezi OJ. A new variant of Rama distribution with simulation study and application to blood cancer data. In: European Journal of Theoretical and Applied Sciences. 2023; 389-409.
Wiley J. Applied Life Data Analysis. 1982.