Double Weighted Inverse Weibull Distribution (original) (raw)

On the Generalized Inverse Weibull Distribution

AIP Conference Proceedings, 2019

A new class of continuous distributions based on generalized inverse Weibull has been introduced. As a special case, generalized inverse Weibull-generalized inverse Weibull distribution is proposed. The probability density function, cumulative distribution function, reliability and hazard rate functions are introduced. Furthermore, most important statistical properties of the proposed distribution such as Shannon entropy, relative entropy, stress-strength model have been obtained.

The Weighted Inverse Weibull Distribution

International Journal of Research and Innovation in Applied Science

This paper introduces the Weighted Inverse Weibull distribution as inverse weighting of the Inverse Weibull distribution. Its various basic statistical properties were explicitly derived and the method of maximum likelihood estimation was used in estimating the model parameters. The model was applied to two real life data sets and its performance and flexibility was assessed with respect to existing distribution using the log-likelihood and Akaike Information Criteria as basis for judgment.

Modified Inverse Weibull Distribution

A generalized version of four parameter modified inverse weibull distribution (MIWD) is introduced in this paper. This distribution generalizes the following distributions: (1) Modified Inverse exponential distribution, (2) Modified Inverse Rayleigh distribution, (3) Inverse weibull distribution. We provide a comprehensive description of the mathematical properties of the modified inverse weibull distribution along with its reliability behaviour. We derive the moments, moment generating function and examine the order statistics. We propose the method of maximum likelihood for estimating the model parameters and obtain the observed information matrix.

A New Generalized Weighted Weibull Distribution

Pakistan Journal of Statistics and Operation Research

In this article, we present a new generalization of weighted Weibull distribution using Topp Leone family of distributions. We have studied some statistical properties of the proposed distribution including quantile function, moment generating function, probability generating function, raw moments, incomplete moments, probability, weighted moments, Rayeni and q th entropy. The have obtained numerical values of the various measures to see the eect of model parameters. Distribution of of order statistics for the proposed model has also been obtained. The estimation of the model parameters has been done by using maximum likelihood method. The eectiveness of proposed model is analyzed by means of a real data sets. Finally, some concluding remarks are given.

The Generalized Inverse Generalized Weibull Distribution and Its Properties

Journal of Probability, 2014

ABSTRACT The Inverse Weibull distribution has been applied to a wide range of situations including applications in medicine, reliability, and ecology. It can also be used to describe the degradation phenomenon of mechanical components. We introduce Inverse Generalized Weibull and Generalized Inverse Generalized Weibull (GIGW) distributions. GIGW distribution is a generalization of several distributions in literature. The mathematical properties of this distribution have been studied and the mixture model of two Generalized Inverse Generalized Weibull distributions is investigated. Estimates of parameters using method of maximum likelihood have been computed through simulations for complete and censored data.

Some Theoretical and Computational Aspects of the Inverse Generalized Power Weibull Distribution

Journal of Data Science, 2019

This paper introduces a new three-parameter distribution called inverse generalized power Weibull distribution. This distribution can be regarded as a reciprocal of the generalized power Weibull distribution. The new distribution is characterized by being a general formula for some well-known distributions, namely inverse Weibull, inverse exponential, inverse Rayleigh and inverse Nadarajah-Haghighi distributions. Some of the mathematical properties of the new distribution including the quantile, density, cumulative distribution functions, moments, moments generating function and order statistics are derived. The model parameters are estimated using the maximum likelihood method. The Monte Carlo simulation study is used to assess the performance of the maximum likelihood estimators in terms of mean squared errors. Two real datasets are used to demonstrate the flexibility of the new distribution as well as to demonstrate its applicability.

An extended version of Kumaraswamy inverse Weibull distribution and its properties

Statistica, 2016

Here we consider an extended version of the Kumaraswamy modified inverse Weibull distribution and investigate some of its theoretical properties through deriving expressions for cumulative distribution function, reliability function, hazard rate function, quantile function, characteristic function, raw moments, median, mode etc. Certain reliability measures of the distribution are obtained along with the distribution and moments of its order statistics. The maximum likelihood estimation of the parameters of the distribution is discussed and certain real life data applications are given for illustrating the usefulness of the model. Further, with the help of simulated data sets it is shown that the average bias and mean square errors of the maximum likelihood estimators are in decreasing order as the sample size increases.