Different methods of estimation for the one parameter Akash distribution

Karakaya, Kadir; Tanış, Caner

doi:10.17776/csj.766011

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…These estimators are often preferred for estimating unknown parameter of distributions. Some studies using this estimator can be given as [10][11][12][13].…”

Section: Estimation Of Parameters Using Different Methodsmentioning

confidence: 99%

Parameters Estimation for the Unit log-log Distribution

Korkmaz

Karakaya

Akdoğan

et al. 2023

Cumhuriyet Science Journal

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In this paper, point estimation problem of two unknown parameters of the unit log-log distribution is examined. For point estimation, six methods of estimate such as maximum likelihood, maximum product spacing, Anderson-Darling, least squares, weighted least squares, and Cramer-von Mises are examined in detail. Extensive simulation experiments are conducted to compare the effectiveness of these estimators based on bias and mean squared errors. According to the simulation results, it is seen that all estimators performed well in terms of two criteria and take close values in case of large sample. Moreover, practical data applications are performed for all estimators. Results of the Kolmogorov-Smirnov statistics are reported for all estimators in practical applications.

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“…These estimators are often preferred for estimating unknown parameter of distributions. Some studies using this estimator can be given as [10][11][12][13].…”

Section: Estimation Of Parameters Using Different Methodsmentioning

confidence: 99%

Parameters Estimation for the Unit log-log Distribution

Korkmaz

Karakaya

Akdoğan

et al. 2023

Cumhuriyet Science Journal

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“…In this section, the maximum likelihood, least square, weighted least square, Anderson-Darling, Cramer-von Mises, and maximum product spacing methods are discussed to estimate the MoL distribution parameters. It is noticed that these estimates are also used in [2], [13], [14], [29], [30] among others. Let X 1 , X 2 , .…”

Section: Point Estimationmentioning

confidence: 99%

Modified-Lindley distribution and its applications to the real data

Kuş¹,

Korkmaz²,

Kınacı³

et al. 2022

Communications Faculty of Science University of Ankara Series A1Mathematics and Statistics

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In this paper, a new three-parameter lifetime distribution is proposed by mixing modified Weibull and generalized gamma distributions. The point estimation on the distribution parameters are discussed through several estimators. The interval estimation is also studied with two methods based on asymptotic normality and likelihood ratio. A Monte Carlo simulation study is performed to evaluate the biases and mean square errors behaviors of point estimates for a different sample of size. A simulation study is also conducted to investigate the coverage probabilities of confidence intervals. The distribution modeling analyses are provided based on several real data sets to demonstrate the fitting ability of the introduced distribution.

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“…Alshenawy [15] proposed one parameter IGD, which is a special case of IGD. It is shortly denoted this one parameter distribution by A b in [11]. IGD is reduced A b by substituting 1 a in (2).…”

Section: Inverse Gompertz Distributionmentioning

confidence: 99%

“…Tanış and Karakaya [10] focused on the problem of parameter estimation for Lindley-Geometric distribution. Karakaya and Tanış [11] studied the methods of estimation for one parameter Akash distribution. Tanış [12] compared the estimation methods for transmuted power function distribution.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Estimation Methods for One Parameter Inverse Gompertz Distribution

Tanış

Karakaya

2021

J. Sci. Arts

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In this paper, we compare the methods of estimation for one parameter lifetime distribution, which is a special case of inverse Gompertz distribution. We discuss five different estimation methods such as maximum likelihood method, least-squares method, weighted least-squares method, the method of Anderson-Darling, and the method of Crámer–von Mises. It is evaluated the performances of these estimators via Monte Carlo simulations according to the bias and mean-squared error. Furthermore, two real data applications are performed.

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Different methods of estimation for the one parameter Akash distribution

Cited by 7 publications

References 14 publications

Parameters Estimation for the Unit log-log Distribution

Parameters Estimation for the Unit log-log Distribution

Modified-Lindley distribution and its applications to the real data

A Comparison of Estimation Methods for One Parameter Inverse Gompertz Distribution

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