This article proposes a new family of continuous distributions generated from a log dagum random variable (named Log-Dagum Weibull Distribution) on the basis of T-X family technique. We have explored the statistical properties such as density function, Hazard function, survival function, quantile points and order statistics of the proposed family of distribution. The Log-Dagum Weibull family has been characterized via different techniques such as characterization by order statistics characterization by truncated moments and characterization by upper record values. Parameters of the proposed model are estimated by maximum likelihood method and check their performance by using four real data sets. In comparison study by using data sets shows that the new family is better to the others named as weibull distribution (WD), Lomax distribution (LD), Gamma distribution (GD), Nadarajah exponentiated exponential distribution (NEED). For the measures of goodness of these models, different criterions are analyzed for the comparison of these fitted models. Additionally, also check the excellence of these competing models is via the Anderson Darling (A^*), Kolmogrov-Simnorov (K-S) and the Cramer-von Misses (W^*). The significance of the new model is verified empirically in modeling real data.
Aneeqa Khadim is PhD Research Scholar in the department of mathematics, Mirpur University of science and technology (MUST), Mirpur-10250 (AJK), Pakistan, under the supervision of Dr. Amir Saghir in the field of probability distribution. She has four research articles in her PHD thesis. She finalized the thesis entitled “T-X family of distributions” for examination. She has published a dozen papers in international highly reputed impact factor journals, as an author or coauthor in the field of statistics and its applications. Her research specialization is in Mathematica Statistics, Distribution Theory, Inferences, Estimation, among others. She also has 9 year experience of teaching at different levels in education department. She is also associated with the Royal Statistical Society (RSS), “U.K”.