This study introduces generalized transmuted family of distributions. We investigate the special cases of our generalized transmuted distribution to match with some other generalization available in literature. The transmuted distributions are applied to Weibull distribution to find generalized rank map transmuted Weibull distribution. The distributional characteristics such as probability curve, mean, variance, skewness, kurtosis, distribution of largest order statistics, and their characteristics studied to compare with ordinary Weibull distribution. Hazard rate functions and distributional characteristics of largest order statistics of transmuted distributions are also studied. It is observed that the transmuted distributions are more flexible to model real data, since the data can present a high degree of skewness and kurtosis. If someone is interested to locate more flexible and higher degree of skewed distribution can explore this generalized transmuted family of distributions for future use.
The characterisation of probability distribution plays an important role in statistical studies. There are various methods of characterisation available in the literature. The characterisation using truncated moments limits the observations; hence, researchers may save time and cost. In this paper, the characterisation of three general forms of continuous distributions based on doubly truncated moments has been studied. The results are given simply and explicitly. Further, the results have been applied to some well-known continuous distributions.
In this paper, we derive the recurrence relations for the moments of function of single and two order statistics from Lindley distribution. We also consider the maximum likelihood estimation (MLE) of the parameter of the distribution based on multiply type-II censoring. The maximum likelihood estimator is comupted numerically because it does not have an explicit form for the parameter. Then, a Monte Carlo simulation study is carried out to evaluate the performance of the MLE obtained from multiply type-II censored sample.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.