Estimation for finite mixture of simplex models: applications to biomedical data

Quintero, Freddy Omar López; Contreras-Reyes, Javier E.

doi:10.1177/1471082x17722607

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…Logistic regression can be used in situations where the response is a Bernoulli event or arises as the proportion of events Y = 1 in n trials and it belongs to the exponential family (Hosmer Jr., Lemeshow, & Sturdivant, 2013 ) A distribution that can be used to study a variable continuous response restricted to the (0,1) interval is a simplex distribution (Zhang and Qiu, 2014). The simplex distribution is included among dispersion models, which extend the generalized linear models (Barndorff-Nielsen & Jørgensen, 1991;Jørgensen, 1997b;López, 2013;Quintero & Contreras-Reyes, 2018). A random variable y following a simplex distribution with mean and dispersion parameter has density function given by Equation 7.…”

Section: Regression Models Applied To Mixture Experimentsmentioning

confidence: 99%

A simplex dispersion model for improving precision in the odds ratio confidence interval in mixture experiments

et al. 2020

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A new approach to data analysis in mixture experiments is proposed using the simplex regression, that is in the class of dispersion models family. The advantages of this approach are illustrated in an experiment studying the mixture effect of fat, carbohydrate, and fiber on tumors’ proportion in mammary glands of rats. Model was evaluated by goodness of fit criteria, simulated envelope charts for residuals of adjusted models, odds ratios graphics and their respective confidence intervals. The simplex regression model showed better quality of fit and smaller odds ratio confidence intervals.

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Section: Regression Models Applied To Mixture Experimentsmentioning

confidence: 99%

A simplex dispersion model for improving precision in the odds ratio confidence interval in mixture experiments

et al. 2020

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“…Clinical symptoms, laboratory findings, and radiological imaging techniques such as chest computed tomography (CT) or chest radiography (X-ray) images can also be used for screening. In particular, radiological imaging techniques play a significant role in the diagnosis of COVID-19 [11,12]. The diagnosis of COVID-19 is facilitated by the bilateral patchy shadows and ground-glass opacity obtained from these techniques [13,14].…”

Section: Introductionmentioning

confidence: 99%

Automatic COVID-19 Detection Using Exemplar Hybrid Deep Features with X-ray Images

Barua

Gowdh

Rahmat

et al. 2021

IJERPH

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COVID-19 and pneumonia detection using medical images is a topic of immense interest in medical and healthcare research. Various advanced medical imaging and machine learning techniques have been presented to detect these respiratory disorders accurately. In this work, we have proposed a novel COVID-19 detection system using an exemplar and hybrid fused deep feature generator with X-ray images. The proposed Exemplar COVID-19FclNet9 comprises three basic steps: exemplar deep feature generation, iterative feature selection and classification. The novelty of this work is the feature extraction using three pre-trained convolutional neural networks (CNNs) in the presented feature extraction phase. The common aspects of these pre-trained CNNs are that they have three fully connected layers, and these networks are AlexNet, VGG16 and VGG19. The fully connected layer of these networks is used to generate deep features using an exemplar structure, and a nine-feature generation method is obtained. The loss values of these feature extractors are computed, and the best three extractors are selected. The features of the top three fully connected features are merged. An iterative selector is used to select the most informative features. The chosen features are classified using a support vector machine (SVM) classifier. The proposed COVID-19FclNet9 applied nine deep feature extraction methods by using three deep networks together. The most appropriate deep feature generation model selection and iterative feature selection have been employed to utilise their advantages together. By using these techniques, the image classification ability of the used three deep networks has been improved. The presented model is developed using four X-ray image corpora (DB1, DB2, DB3 and DB4) with two, three and four classes. The proposed Exemplar COVID-19FclNet9 achieved a classification accuracy of 97.60%, 89.96%, 98.84% and 99.64% using the SVM classifier with 10-fold cross-validation for four datasets, respectively. Our developed Exemplar COVID-19FclNet9 model has achieved high classification accuracy for all four databases and may be deployed for clinical application.

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“…Zhao et al (2014) considered the Bayesian estimation of simplex non-linear mixed models for longitudinal data. Recently, Quintero & Contreras-Reyes (2018) proposed a mixture simplex model, where the parameters were estimated by an expectation-maximisation algorithm.…”

Section: Introductionmentioning

confidence: 99%

Proportional inverse Gaussian distribution: A new tool for analysing continuous proportional data

Liu

Tian

Yuen

et al. 2021

Aus NZ J of Statistics

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Outcomes in the form of rates, fractions, proportions and percentages often appear in various fields. Existing beta and simplex distributions are frequently unable to exhibit satisfactory performances in fitting such continuous data. This paper aims to develop the normalised inverse Gaussian (N-IG) distribution proposed by Lijoi, Mena & Prünster (2005, Journal of the American Statistical Association, 100, 1278-1291) as a new tool for analysing continuous proportional data in (0, 1) and renames the N-IG as proportional inverse Gaussian (PIG) distribution. Our main contributions include: (i) To overcome the difficulty of an integral in the PIG density function, we propose a novel minorisationmaximisation (MM) algorithm via the continuous version of Jensen's inequality to calculate the maximum likelihood estimates of the parameters in the PIG distribution; (ii) We also develop an MM algorithm aided by the gradient descent algorithm for the PIG regression model, which allows us to explore the relationship between a set of covariates with the mean parameter; (iii) Both the comparative studies and the real data analyses show that the PIG distribution is better when comparing with the beta and simplex distributions in terms of the AIC, the Cramér-von Mises and the Kolmogorov-Smirnov tests. In addition, bootstrap confidence intervals and testing hypothesis on the symmetry of the PIG density are also presented. Simulation studies are conducted and the hospital stay data of Barcelona in 1988 and 1990 are analysed to illustrate the proposed methods.

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Estimation for finite mixture of simplex models: applications to biomedical data

Cited by 7 publications

References 25 publications

A simplex dispersion model for improving precision in the odds ratio confidence interval in mixture experiments

A simplex dispersion model for improving precision in the odds ratio confidence interval in mixture experiments

Automatic COVID-19 Detection Using Exemplar Hybrid Deep Features with X-ray Images

Proportional inverse Gaussian distribution: A new tool for analysing continuous proportional data

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