Errors of misclassification associated with gamma distribution

Mahmoud, M. A. W.; Moustafa, H. M.

doi:10.1016/0895-7177(95)00124-k

Cited by 10 publications

(5 citation statements)

References 10 publications

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“…This problem was investigated by comparing the errors of misclassification associated with Johnson's system distributions in the appropriate transformable non-normal case with that of normal distribution [6]. Errors of misclassification associated with Gamma were also examined by [15]. Considerable work has been done by researchers in connection with errors of misclassification when the underlying distribution is transformable non-normal distribution, but the errors of misclassification associated with persistent non-normal distribution remain unresolved [12].…”

Section: Statement Of the Problemmentioning

confidence: 99%

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Errors of Misclassification Associated with Edgeworth Series Distribution (ESD)

Adeyeye¹,

Iheanyi²

2019

AJTAS

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This study investigates the errors of misclassification associated with Edgeworth Series Distribution (ESD) with a view to assessing the effects of sampling from non-normality. The effects of applying a normal classificatory rule when it is actually a persistent non-normal distribution were examined. These were achieved by comparing the errors of misclassification for ESD with ND using small sample sizes at every level of skewness factor. The simulation procedure for the experiment of the study was implemented using numerical inverse interpolation method in R program to generate a uniformly distributed random variable N. A configuration size of 1000 was obtained for the two training samples drawn at every level of skewness factor (λ 3), in the range (0.00625, 0.4). This was repeated for different small sample sizes by comparing errors of misclassification of ESD with ND. The simulation results showed that the optimum probabilities of misclassification by ESD: (E 12E) decreases and (E 12E) increases, as the skewness factor (λ 3) increases. The optimum total probability of misclassification is stable as 3 λ also increases. The probability of misclassification E 12E ≥ E 12N and E 21E ≥ E 21N at every level of λ 3. Thus, the total probabilities of misclassification are not greatly affected by the skewness factor. This asserts that the normal classification procedure is robust against departure from normality.

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Section: Statement Of the Problemmentioning

confidence: 99%

“…Errors of misclassification for classification problems with two classes of univariate-gamma distribution were studied by [15]. The gamma density functions given as…”

Section: Literature Reviewmentioning

confidence: 99%

Errors of Misclassification Associated with Edgeworth Series Distribution (ESD)

Adeyeye¹,

Iheanyi²

2019

AJTAS

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“…Initially, Lachenbruch et al (1973) and Chinganda and Subrahmaniam (1979) examined the robustness of the LDF, when the underlying distributions belongs to Johnson's system, while Subrahmaniam and Chinganda (1978) studied the case of Edgeworth type distributions. Moreover, mention should be made of the work of Balakrishnan and Kocherlakota (1985), Amoh and Kocherlakota (1986), Kocherlakota et al (1987), Mahmoud and Moustafa (1995), Moustafa and Ramadan (2005), who have performed similar studies when the parent populations are mixtures of normal, inverse gaussian, truncated normal, gamma and Gompertz, respectively.…”

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confidence: 94%

Errors of misclassification in discrimination with data from truncated t populations

Batsidis

2010

Stat Papers

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The distribution of the probabilities of misclassification is derived in this paper, which are reproduced by the use of the linear discriminant function. The statistical background is two independent doubly truncated t populations with distinct location parameters and common scale parameter and degrees of freedom. The behavior of the linear discriminant function is studied by comparing the distribution function of the errors of misclassification under the truncated t and truncated normal models.

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“…Mahmoud and Moustafa (1993) estimated a discriminant function from a mixture of two Gamma distributions. Ahmad (1994) Mahmoud and Moustafa (1995) studied the errors of misclassification associated with Gamma distribution. Ahmad (1995) studied the efficiency of a nonlinear discriminant function based on unclassified initial samples from a mixture of two Burr type XII distributions.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Discriminant Functions for Mixed Random Walk Models

Moustafa

Ramadan

Mohammed

2010

Communications in Statistics - Simulation and Computation

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A procedure is presented for finding maximum likelihood estimates of the parameters of a mixture of two random walk distributions in two cases, using classified and unclassified observations. Based on small sample size, estimation of nonlinear discriminant functions is considered. Throughout simulation experiments, the performance of the corresponding estimated nonlinear discriminant functions is investigated. The total probabilities of misclassification and percentage biases are evaluated and discussed.

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Errors of misclassification associated with gamma distribution

Cited by 10 publications

References 10 publications

Errors of Misclassification Associated with Edgeworth Series Distribution (ESD)

Errors of Misclassification Associated with Edgeworth Series Distribution (ESD)

Errors of misclassification in discrimination with data from truncated t populations

Nonlinear Discriminant Functions for Mixed Random Walk Models

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