On the method of maximum likelihood estimation for the log-Pearson type 3 distribution

1988 ◽  
Vol 2 (2) ◽  
pp. 155-160 ◽  
Author(s):  
K. Arora ◽  
V. P. Singh
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Method Of Maximum Likelihood ◽  
Pearson Type ◽  
Type 3

scholarly journals Method of maximum likelihood estimation of compact group objects location on CCD-frame

2014 ◽  
Vol 5 (4(71)) ◽  
pp. 16
Author(s):  
Любовь Олеговна Михайлова ◽  
Вадим Евгеньевич Саваневич ◽  
Наталья Сергеевна Соковикова ◽  
Михаил Михайлович Безкровный ◽  
Сергей Васильевич Хламов ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Compact Group ◽  
Likelihood Estimation ◽  
Method Of Maximum Likelihood

scholarly journals A Study on Properties and Applications of a Lomax Gompertz-Makeham Distribution

2019 ◽  
pp. 1-27
Author(s):  
Innocent Boyle Eraikhuemen ◽  
Terna Godfrey Ieren ◽  
Tajan Mashingil Mabur ◽  
Mohammed Sa’ad ◽  
Samson Kuje ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Probability Distributions ◽  
Real Life ◽  
Likelihood Estimation ◽  
The Other ◽  
Unknown Parameters ◽  
Method Of Maximum Likelihood ◽  
Compound Model ◽  
New Distribution

The article presents an extension of the Gompertz-Makeham distribution using the Lomax generator of probability distributions. This generalization of the Gompertz-Makeham distribution provides a more skewed and flexible compound model called Lomax Gompertz-Makeham distribution. The paper derives and discusses some Mathematical and Statistical properties of the new distribution. The unknown parameters of the new model are estimated via the method of maximum likelihood estimation. In conclusion, the new distribution is applied to two real life datasets together with two other related models to check its flexibility or performance and the results indicate that the proposed extension is more flexible compared to the other two distributions considered in the paper based on the two datasets used.

scholarly journals Properties and Applications of a Transmuted Power Gompertz Distribution

2020 ◽  
pp. 41-58
Author(s):  
Innocent Boyle Eraikhuemen ◽  
Adana’a Felix Chama ◽  
Abraham Iorkaa Asongo ◽  
Bassa Shiwaye Yakura ◽  
Abdul Haruna Bala
Keyword(s):  
Maximum Likelihood ◽  
Probability Distribution ◽  
Maximum Likelihood Estimation ◽  
Goodness Of Fit ◽  
Real Life ◽  
Likelihood Estimation ◽  
New Model ◽  
Gompertz Distribution ◽  
Method Of Maximum Likelihood ◽  
Result Show

This article introduces and studies a new probability distribution called “Transmuted Power Gompertz distribution”. It looks at the properties of the transmuted power Gompertz distribution. The article also estimates the four parameters of the new model using the method of maximum likelihood estimation. The article further evaluates the goodness-of-fit of the proposed distribution compared to other distributions by means of applications of the model to two real life datasets and the result show that the proposed distribution is more flexible than the fitted existing distributions.

scholarly journals Optimization of diffusion imaging for multiple target regions using maximum likelihood estimation

2017 ◽  
Vol 3 (2) ◽  
pp. 203-206
Author(s):  
Lars Bielak ◽  
Michael Bock
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Diffusion Mri ◽  
Diffusion Imaging ◽  
Optimization Procedure ◽  
Likelihood Estimation ◽  
Multiple Target ◽  
B Values ◽  
Method Of Maximum Likelihood ◽  
Target Values

AbstractIn this work a procedure is proposed to determine an optimal distribution of b-values in diffusion MRI measu-rements. The optimization procedure uses a method of Maximum Likelihood Estimation which can operate on any given number of b-values, values of the diffusion coefficients (ADC) and measurement noise strengths. Optimal b-values are calculated for white and gray brain matter. An optimi-zation for more than one ADC is demonstrated using multiple target values.

scholarly journals A New Generalization of the Lomax Distribution with Increasing, Decreasing, and Constant Failure Rate

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Pelumi E. Oguntunde ◽  
Mundher A. Khaleel ◽  
Mohammed T. Ahmed ◽  
Adebowale O. Adejumo ◽  
Oluwole A. Odetunmibi
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Real Life ◽  
Likelihood Estimation ◽  
Model Parameters ◽  
Lomax Distribution ◽  
Life Data ◽  
Real Life Data ◽  
Method Of Maximum Likelihood ◽  
Better Than

Developing new compound distributions which are more flexible than the existing distributions have become the new trend in distribution theory. In this present study, the Lomax distribution was extended using the Gompertz family of distribution, its resulting densities and statistical properties were carefully derived, and the method of maximum likelihood estimation was proposed in estimating the model parameters. A simulation study to assess the performance of the parameters of Gompertz Lomax distribution was provided and an application to real life data was provided to assess the potentials of the newly derived distribution. Excerpt from the analysis indicates that the Gompertz Lomax distribution performed better than the Beta Lomax distribution, Weibull Lomax distribution, and Kumaraswamy Lomax distribution.

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.

Transmuted Erlang-Truncated Exponential Distribution

2016 ◽  
Vol 31 (2) ◽  
Author(s):  
Idika E. Okorie ◽  
Anthony C. Akpanta ◽  
Johnson Ohakwe
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Hazard Rate ◽  
Likelihood Estimation ◽  
Rate Function ◽  
Lifetime Distribution ◽  
Hazard Rate Function ◽  
Method Of Maximum Likelihood ◽  
Two Parameter ◽  
New Distribution

AbstractThis article introduces a new lifetime distribution called the transmuted Erlang-truncated exponential (TETE) distribution. This new distribution generalizes the two parameter Erlang-truncated exponential (ETE) distribution. Closed form expressions for some of its distributional and reliability properties are provided. The method of maximum likelihood estimation was proposed for estimating the parameters of the TETE distribution. The hazard rate function of the TETE distribution can be constant, increasing or decreasing depending on the value of the transmutation parameter

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