Some Statistical Properties of Exponentiated Weighted Weibull Distribution

2014 ◽  
Vol 2014 (2) ◽  
pp. 1-11
Author(s):  
Nofiu BAMUS ◽  
◽  
Adebayo BAMIDURO ◽  
Keyword(s):  
Weibull Distribution ◽  
Statistical Properties

scholarly journals Right Truncated Fréchet-Weibull Distribution: Statistical Properties and Application

2020 ◽  
Vol 41 (1) ◽  
pp. 20-29
Author(s):  
Abd El-monem A. M. Teamah ◽  
Ahmed A. Elbanna ◽  
Ahmed M. Gemeay
Keyword(s):  
Weibull Distribution ◽  
Statistical Properties

scholarly journals Cubic Transformation of Exponential Weibull and its Statistical Properties

2020 ◽  
pp. 39-50
Author(s):  
Haiyue Wang ◽  
Zhenhua Bao
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Weibull Distribution ◽  
Likelihood Estimation ◽  
Real Data ◽  
Statistical Properties ◽  
Reasoning Process ◽  
The Family

In this paper, a cubic transformation exponential Weibull distribution is proposed by using the family of cubic transformation distributions introduced by Rahman et al.The reasoning process of the proposed cubic transformation exponential Weibull distribution is discussed in detail, and its statistical properties and parameter estimation are also discussed.Using real data, the maximum likelihood estimation is used to simulate. Through the comparison of fitting results, it is concluded that the cubic transformation exponential Weibull distribution proposed in this paper has stronger applicability.

scholarly journals Right Truncated Fréchet-Weibull Distribution: Statistical Properties and Application

2020 ◽  
Vol 41 (1) ◽  
pp. 20-29
Author(s):  
Abd El-monem A. M. Teamah ◽  
Ahmed A. Elbanna ◽  
Ahmed M. Gemeay
Keyword(s):  
Weibull Distribution ◽  
Statistical Properties

Transmuted New Weighted Weibull distribution: Some statistical properties and application to survival time data

2021 ◽  
Author(s):  
Adamu Abubakar Umar ◽  
Aliyu Usman ◽  
Sani Salihu Abubakar ◽  
Terna Godfrey Ieren ◽  
Jamilu Yunusa Falgore ◽  
...  
Keyword(s):  
Weibull Distribution ◽  
Survival Time ◽  
Statistical Properties ◽  
Time Data ◽  
Survival Time Data

scholarly journals A family of Gamma-generated distributions: Statistical properties and applications

2021 ◽  
pp. 096228022110092
Author(s):  
Hormatollah Pourreza ◽  
Ezzatallah Baloui Jamkhaneh ◽  
Einolah Deiri
Keyword(s):  
Weibull Distribution ◽  
Real Data ◽  
Statistical Properties ◽  
Cumulative Distribution ◽  
Data Sets ◽  
Monte Carlo Simulation Study ◽  
Hazard Functions ◽  
Distribution Probability ◽  
Method Of Maximum Likelihood ◽  
Special Case

In this paper, we concentrate on the statistical properties of Gamma-X family of distributions. A special case of this family is the Gamma-Weibull distribution. Therefore, the statistical properties of Gamma-Weibull distribution as a sub-model of Gamma-X family are discussed such as moments, variance, skewness, kurtosis and Rényi entropy. Also, the parameters of the Gamma-Weibull distribution are estimated by the method of maximum likelihood. Some sub-models of the Gamma-X are investigated, including the cumulative distribution, probability density, survival and hazard functions. The Monte Carlo simulation study is conducted to assess the performances of these estimators. Finally, the adequacy of Gamma-Weibull distribution in data modeling is verified by the two clinical real data sets. Mathematics Subject Classification: 62E99; 62E15

scholarly journals Bivariate exponentiated discrete Weibull distribution: statistical properties, estimation, simulation and applications

2019 ◽  
Vol 14 (1) ◽  
pp. 29-42 ◽  
Author(s):  
M. El- Morshedy ◽  
M. S. Eliwa ◽  
A. El-Gohary ◽  
A. A. Khalil
Keyword(s):  
Weibull Distribution ◽  
Joint Probability ◽  
Discrete Distribution ◽  
Real Data ◽  
Reliability Function ◽  
Statistical Properties ◽  
Cumulative Distribution ◽  
Likelihood Method ◽  
Model Parameters ◽  
Probability Mass Function

AbstractIn this paper, a new bivariate discrete distribution is defined and studied in-detail, in the so-called the bivariate exponentiated discrete Weibull distribution. Several of its statistical properties including the joint cumulative distribution function, joint probability mass function, joint hazard rate function, joint moment generating function, mathematical expectation and reliability function for stress–strength model are derived. Its marginals are exponentiated discrete Weibull distributions. Hence, these marginals can be used to analyze the hazard rates in the discrete cases. The model parameters are estimated using the maximum likelihood method. Simulation study is performed to discuss the bias and mean square error of the estimators. Finally, two real data sets are analyzed to illustrate the flexibility of the proposed model.

Brownian motion with quadratic killing and some implications

1986 ◽  
Vol 23 (04) ◽  
pp. 893-903 ◽  
Author(s):  
Michael L. Wenocur
Keyword(s):  
Brownian Motion ◽  
Weibull Distribution ◽  
Special Function ◽  
Function Theory ◽  
Killing Rate

Brownian motion subject to a quadratic killing rate and its connection with the Weibull distribution is analyzed. The distribution obtained for the process killing time significantly generalizes the Weibull. The derivation involves the use of the Karhunen–Loève expansion for Brownian motion, special function theory, and the calculus of residues.

Statistical properties of type I intermittency

1982 ◽  
Vol 43 (4) ◽  
pp. 585-589 ◽  
Author(s):  
M. N. Bussac ◽  
C. Meunier
Keyword(s):  
Statistical Properties ◽  
Type I
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