[1] Cirrone, G., Donadio, S., Guatelli, S., Mantero, A., Mascialino, B., Parlati, S., Viarengo, P. (2004). A goodness-of- t statistical toolkit. IEEE Transactions on Nuclear Science, 51(5), 2056-2063.
[2] D'Agostino, R., & Stephens, M. (1986). Goodness-of- t Techniques. New york: Marcel Dekker,Inc.
[3] Huber Carol, C., Balakrishnan, N., Nikulin, M., & Mesbah, M. (2012). Goodness-of- t tests and model validity: Springer Science & Business Media.
[4] Cover, T.M., & Thomas, J.A. (2006). Elements of Information Theory (second edition ed.). New Jersey: A John Wiley & Sons.
[5] Shannon, C. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27, 379-423.
[6] Vasicek, O. (1976). A test for normality based on sample entropy. Journal of the Royal Statistical Society Series B: Statistical Methodology, 38(1), 54-59.
[7] Joe, H. (1989). Estimation of entropy and other functionals of a multivariate density. Annals of the Institute of Statistical Mathematics, 41, 683-697.
[8] Hall, P., & Morton, S.C. (1993). On the estimation of entropy. Annals of the Institute of Statistical Mathematics, 45, 69-88.
[9] Van Es, B. (1992). Estimating functionals related to a density by a class of statistics based on spacings. Scandinavian Journal of Statistics, 61-72.
[10] Correa, J.C. (1995). A new estimator of entropy. Communications in Statistics-Theory and Methods, 24(10), 2439-2449.
[11] Alizadeh, H.N. (2010). A new estimator of entropy and its application in testing nor-mality. Journal of Statistical Computation and Simulation, 80(10), 1151-1162.
[12] Abramowitz, M. (ed.) (1964), Handbook of Mathematical Functions with Formulas, U.S. Govt. Print. O . (Washington DC), Reprint by Dover (New York) 1965.
[13] Kang, S.B., & Lee, H.J. (2006). Goodness-of- t tests for the Weibull distribution based on the sample entropy. Journal of the Korean Data and Information Science Society, 17(1), 259-268.
[14] Krit, M., Gaudoin, O., & Remy, E. (2021). Goodness-of- t tests for the Weibull and extreme value distributions: A review and comparative study. Communications in Statistics-Simulation and Computation, 50(7), 1888-1911.
[15] Mudholkar, G.S., & Srivastava, D.K. (1993). ExponentiatedWeibull family for analyzing bathtub failure-rate data. IEEE transactions on reliability, 42(2), 299-302.
[16] Stacy, E.W. (1962). A generalization of the gamma distribution. The Annals of mathematical statistics, 1187-1192.
[17] Dhillon, B.S. (1981). Life distributions. IEEE transactions on reliability, 30(5), 457-460.
[18] Hjorth, U. (1980). A reliability distribution with increasing, decreasing, constant and bathtub-shaped failure rates. Technometrics, 22(1), 99-107.
[19] Chen, Z. (2000). A new two-parameter lifetime distribution with bathtub shape or increasing failure rate function. Statistics& Probability Letters, 49(2), 155-161.
[20] Liu, X., Ahmad, Z., Gemeay, A. M., Abdulrahman, A. T., Hafez, E., & Khalil, N. (2021). Modeling the survival times of the COVID-19 patients with a new statistical model: A case study from China. Plos one, 16(7), e0254999.
[21] Alshanbari, H. M., Odhah, O. H., Almetwally, E. M., Hussam, E., Kilai, M., & El-Bagoury, A.-A. H. (2022). Novel Type I Half Logistic Burr-Weibull Distribution: Application to COVID-19 Data. Computational and Mathematical Methods in Medicine,
2022.
)