On the identifiability of finite mixtures of Laguerre distributions (Corresp.)

In this paper, the identifiability property has been studied for a suggested truncated type-I generalized logistic mixture model which is denoted by TTIGL . A suggested form of the EM algorithm has been applied on type-II progressive censored samples to obtain the maximum likelihood estimates MLE ′ s of the parameters, survival function SF , and hazard rate function HRF of the studied mixture model. Monte Carlo simulation algorithm has been applied to study the behavior of the mean squares errors MSE ′ s of the estimates. Also, a comparative study is conducted between the suggested EM algorithm and the ordinary algorithm of maximizing the likelihood function, which depends on the differentiation of the log likelihood function. The results of this paper have been applied on a real dataset as an application.

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“…e identifiability property has been explored by a variety of authors [12,[16][17][18][19][20][21][22][23][24][25][26][27].…”

Section: Identifiability Of the Finite Mixture Of Ttigl Distributionsmentioning

confidence: 99%

“…ree type-II progressive censored samples are generated from the combined real dataset using the schemes M 1 (1 (80) ), M 2 (1 (60) , 0 (20) , 1 (10) ), and M 3 (1 (50) , 0 (40) , 1 (10) ). e three samples, respectively, are as follows:…”

Section: An Applicationmentioning

confidence: 99%

Estimation Using Suggested EM Algorithm Based on Progressively Type-II Censored Samples from a Finite Mixture of Truncated Type-I Generalized Logistic Distributions with an Application

Ateya

Kilai

Aldallal

2022

Mathematical Problems in Engineering

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“…G on the basis of observations from the mixture H. So, estimation of the mixing distribution G, testing hypotheses about the mixing distribution G, etc., can be meaningfully discussed only if the family of mixing distributions is identifiable. Discussions of identifiability of mixtures may be found in several papers, among others, by Teicher (1961Teicher ( , 1963Teicher ( , 1967, Yakowitz and Spragins (1968), Mohanty (1972), Rennie (1972), Blum and Susarla (1977), AI-Hussaini and Ahmad (1981), Fraser et al (1981), Ahmad and AI-Hussaini (1982) and Kent (1983). Teicher (1963), Yakowitz and Spragins (1968), Mohanty (1972) and AI-Hussaini and Ahmad (1981) used moment generating function, characteristic function and Laplace transforms in Theorem (2) of Teicher (1963), which is stated with weaker assumption by Chandra (1977) as the required transforms.…”

Section: (11) H(x) = Fr~ F(x; A)dg(a) mentioning

confidence: 99%

Identifiability of finite mixtures using a new transform

Ahmad

1988

Ann Inst Stat Math

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“…Using a result given by [122], Mohanty [76] showed that the finite mixture of Laguerre distributions is also identifiable. Chandra [14] has proved some results given by Teicher [122] and Yakowitz and Spragins [127] by some other methods.…”

Section: Iamentioning

confidence: 99%

On Mixtures of Distributions: A Survey and Some New Results on Ranking and Selection

Gupta¹,

Huang²

1979

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On the identifiability of finite mixtures of Laguerre distributions (Corresp.)

Cited by 11 publications

References 5 publications

Estimation Using Suggested EM Algorithm Based on Progressively Type-II Censored Samples from a Finite Mixture of Truncated Type-I Generalized Logistic Distributions with an Application

Estimation Using Suggested EM Algorithm Based on Progressively Type-II Censored Samples from a Finite Mixture of Truncated Type-I Generalized Logistic Distributions with an Application

Identifiability of finite mixtures using a new transform

On Mixtures of Distributions: A Survey and Some New Results on Ranking and Selection

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