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 Maximum Likelihood Estimation for Three-Parameter Weibull Distribution Using Evolutionary Strategy

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Fan Yang ◽  
Hu Ren ◽  
Zhili Hu
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Weibull Distribution ◽  
Likelihood Function ◽  
Likelihood Estimation ◽  
Estimation Procedure ◽  
Evolutionary Strategy ◽  
Likelihood Method ◽  
Conventional Algorithm

The maximum likelihood estimation is a widely used approach to the parameter estimation. However, the conventional algorithm makes the estimation procedure of three-parameter Weibull distribution difficult. Therefore, this paper proposes an evolutionary strategy to explore the good solutions based on the maximum likelihood method. The maximizing process of likelihood function is converted to an optimization problem. The evolutionary algorithm is employed to obtain the optimal parameters for the likelihood function. Examples are presented to demonstrate the proposed method. The results show that the proposed method is suitable for the parameter estimation of the three-parameter Weibull distribution.

Maximum Likelihood Method for Parameter Estimation of Weibull Distribution Model Based on Fruit Fly Optimization Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 3508-3511 ◽  
Author(s):  
Xiang Ping Meng ◽  
Chuan Qi Zhao ◽  
Lei Huo
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Weibull Distribution ◽  
Optimization Algorithm ◽  
Likelihood Estimation ◽  
Fruit Fly ◽  
Distribution Model ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization

It is difficult to estimate the parameters of Weibull distribution model using Maximum Likelihood Estimation based on Ant Colony Algorithm (ACA) or Particle Swarm Optimization theory (PSO) for which is easy to fall into premature and needs more variables, thus Fruit Fly Optimization Algorithm (FOA) theory is introduced into maximum likelihood estimation, and a parameter estimation method based on FOA theory is proposed, an example has been simulated to verify the feasibility and effectiveness of this method by comparing with ACA and PSO.

scholarly journals On some properties of Generalized Transmuted Kumaraswamy distribution

2019 ◽  
pp. 577-586
Author(s):  
Aliyu Ismail Ishaq ◽  
Abubakar Usman ◽  
Tasiu Musa ◽  
Samson Agboola
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Statistical Properties ◽  
Survival Functions ◽  
Kumaraswamy Distribution ◽  
Proposed Model ◽  
Method Of Maximum Likelihood ◽  
The Family ◽  
Lifetime Model

ABSTRACTThis articles introduces a new lifetime model called the generalized transmuted Kumaraswamy distribution which extends the Kumaraswamy distribution from the family proposed by Nofal et al., (2017). We provide hazard and survival functions of the proposed distribution. The statistical properties of the proposed model are provided and the method of Maximum Likelihood Estimation (MLE) was proposed in estimating its parameters.

scholarly journals Effective estimation algorithm for parameters of multivariate Farlie–Gumbel–Morgenstern copula

2021 ◽  
Author(s):  
Shuhei Ota ◽  
Mitsuhiro Kimura
Keyword(s):  
Parameter Estimation ◽  
Data Analysis ◽  
Computational Complexity ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Asymptotic Normality ◽  
Likelihood Estimation ◽  
Real Data ◽  
Estimation Algorithm ◽  
Simulation Studies

AbstractThis paper focuses on the parameter estimation for the d-variate Farlie–Gumbel–Morgenstern (FGM) copula ($$d\ge 2$$ d ≥ 2 ), which has $$2^d-d-1$$ 2 d - d - 1 dependence parameters to be estimated; therefore, maximum likelihood estimation is not practical for a large d from the viewpoint of computational complexity. Besides, the restriction for the FGM copula’s parameters becomes increasingly complex as d becomes large, which makes parameter estimation difficult. We propose an effective estimation algorithm for the d-variate FGM copula by using the method of inference functions for margins under the restriction of the parameters. We then discuss its asymptotic normality as well as its performance determined through simulation studies. The proposed method is also applied to real data analysis of bearing reliability.

scholarly journals A new generalization of Aradhana distribution: Properties and applications

2020 ◽  
Vol 16 (2) ◽  
pp. 51-66
Author(s):  
A. Hassan ◽  
S. A. Dar ◽  
P. B. Ahmad ◽  
B. A. Para
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Simulation Study ◽  
Likelihood Estimation ◽  
Real Data ◽  
Statistical Properties ◽  
Model Parameters ◽  
R Software ◽  
Data Set

AbstractIn this paper, we introduce a new generalization of Aradhana distribution called as Weighted Aradhana Distribution (WID). The statistical properties of this distribution are derived and the model parameters are estimated by maximum likelihood estimation. Simulation study of ML estimates of the parameters is carried out in R software. Finally, an application to real data set is presented to examine the significance of newly introduced model.

scholarly journals New class of Lindley distributions: properties and applications

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Duha Hamed ◽  
Ahmad Alzaghal
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Simulation Study ◽  
Likelihood Estimation ◽  
Statistical Properties ◽  
Data Sets ◽  
Lifetime Data ◽  
Unknown Parameters ◽  
Lindley Distribution ◽  
New Class

AbstractA new generalized class of Lindley distribution is introduced in this paper. This new class is called the T-Lindley{Y} class of distributions, and it is generated by using the quantile functions of uniform, exponential, Weibull, log-logistic, logistic and Cauchy distributions. The statistical properties including the modes, moments and Shannon’s entropy are discussed. Three new generalized Lindley distributions are investigated in more details. For estimating the unknown parameters, the maximum likelihood estimation has been used and a simulation study was carried out. Lastly, the usefulness of this new proposed class in fitting lifetime data is illustrated using four different data sets. In the application section, the strength of members of the T-Lindley{Y} class in modeling both unimodal as well as bimodal data sets is presented. A member of the T-Lindley{Y} class of distributions outperformed other known distributions in modeling unimodal and bimodal lifetime data sets.

Sign in / Sign up

Export Citation Format

Share Document