scholarly journals Some Properties and Estimation of Parameters for the Five Parameter Type I Generalized Half Logistic Distribution under Complete Observation

2020 ◽  
pp. 39-46
Author(s):  
O. A. Bello ◽  
P. O. Awodutire ◽  
I. Sule ◽  
H. O. Lawal
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Method ◽  
Likelihood Method ◽  
Logistic Distribution ◽  
Type I ◽  
Lifetime Data ◽  
Estimation Of Parameters ◽  
Distribution Estimation ◽  
Complete Observation

This paper is a further study of the five parameter type I generalized half logistic distribution. We derived some properties of the distribution. Estimation of the parameters of the distribution under complete observation was studied using the maximum likelihood method. To assess the flexibility of the distribution, it was applied to a real lifetime data and the results when compared to the sub-models showed that the five parameter type I generalized half logistic distribution performed best.

The Poisson Nadarajah–Haghighi Distribution: Properties and Applications to Lifetime Data

2019 ◽  
Vol 27 (01) ◽  
pp. 2050005
Author(s):  
Muhammad Mansoor ◽  
M. H. Tahir ◽  
Aymaan Alzaatreh ◽  
Gauss M. Cordeiro
Keyword(s):  
Maximum Likelihood ◽  
Censored Data ◽  
Survival Data ◽  
Maximum Likelihood Method ◽  
Likelihood Estimation ◽  
Real Data ◽  
Likelihood Method ◽  
Lifetime Data ◽  
Monte Carlo Simulation Study ◽  
Mathematical Properties

A new three-parameter compounded extended-exponential distribution “Poisson Nadarajah–Haghighi” is introduced and studied, which is quite flexible and can be used effectively in modeling survival data. It can have increasing, decreasing, upside-down bathtub and bathtub-shaped failure rate. A comprehensive account of the mathematical properties of the model is presented. We discuss maximum likelihood estimation for complete and censored data. The suitability of the maximum likelihood method to estimate its parameters is assessed by a Monte Carlo simulation study. Four empirical illustrations of the new model are presented to real data and the results are quite satisfactory.

scholarly journals A new family of polyno-expo-trigonometric distributions with applications

2019 ◽  
Vol 22 (04) ◽  
pp. 1950027
Author(s):  
Farrukh Jamal ◽  
Christophe Chesneau
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Method ◽  
Mean Squared Error ◽  
Real Life ◽  
Likelihood Method ◽  
Lifetime Data ◽  
New Family ◽  
Squared Error ◽  
The Mean ◽  
Special Case

In this paper, a new family of polyno-expo-trigonometric distributions is presented and investigated. A special case using the Weibull distribution, with three parameters, is considered as statistical model for lifetime data. The estimation of the parameters is performed with the maximum likelihood method. A numerical simulation study verifies that the bias and the mean squared error of the maximum likelihood estimators tend to zero as the sample size is increased. Three real life datasets are then analyzed. We show that our model has a good fit in comparison to the other well-known powerful models in the literature.

scholarly journals The Odd Generalized Exponential Type-I Generalised Half Logistic Distribution: Properties and Application

2018 ◽  
Vol 7 (01) ◽  
pp. 23505-23516
Author(s):  
M. Vijaya Lakshmi ◽  
G.V. S. R. Anjaneyulu
Keyword(s):  
Exponential Type ◽  
Maximum Likelihood Method ◽  
Information Matrix ◽  
Likelihood Method ◽  
Logistic Distribution ◽  
Model Parameters ◽  
Type I ◽  
Mathematical Properties ◽  
Lifetime Model ◽  
New Distribution

We propose a new lifetime model, called the Odd Generalized Exponential Type-I Generalised Half Logistic Distribution (OGET-IGHLD).We obtain some of its mathematical properties. We made an attempt to study some structural properties of the new distribution. The Maximum Likelihood method is used for Estimating the model parameters and the Fisher’s information matrix is also derived. We illustrate the  applications with generalised data.

scholarly journals Estimation of parameters of Nadarajah-Haghighi extension of the exponential distribution using perfect and imperfect ranked set sample

2020 ◽  
Vol 30 (2) ◽  
pp. 177-198
Author(s):  
Marija Minic
Keyword(s):  
Maximum Likelihood ◽  
Exponential Distribution ◽  
Maximum Likelihood Method ◽  
Likelihood Method ◽  
Simple Random Sample ◽  
Estimation Of Parameters ◽  
Ranked Set Sample ◽  
Scale Parameters ◽  
Wide Range ◽  
Mean Square Errors

The ranked set sampling (RSS) is a cost-effective method of sampling that can be used in a wide range of statistical problems. In this paper, the shape and the scale parameters of Nadarajah-Haghighi extension of the exponential distribution are estimated based on a simple random sample (SRS) and RSS. Three cases are considered: 1) the scale parameter is known; 2) the shape parameter is known; 3) both shape and scale parameters are unknown. Observations are done when the ranking mechanism in the ranked set sample is perfect and when it is not. Method of moments, the maximum likelihood method, and a modification of the maximum likelihood method are used. The obtained estimators are compared in terms of their biases and mean square errors (MSE). The results revealed that estimators based on RSS tend to show better properties (smaller bias and MSE) relative to their SRS counterparts, regardless of the quality of the ranking.

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