scholarly journals The use of finite mixture models to examine the serum 25(OH)D levels among Saudis

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260748
Author(s):  
Ibrahim Al-Sumaih ◽  
Michael Donnelly ◽  
Ciaran O’Neill
Keyword(s):  
Linear Regression ◽  
Mixture Models ◽  
Finite Mixture Models ◽  
Unobserved Heterogeneity ◽  
Finite Mixture ◽  
Latent Classes ◽  
Class Iii ◽  
Interview Survey ◽  
The Relationship

Background Recorded serum 25(OH)D in survey data varies with observed and unobserved respondent characteristics. The aim of this study was to expose latent population sub-groups and examine variation across groups regarding relationships between serum 25(OH)D and observable characteristics. Methods This study explored the role of unobserved heterogeneity on associations between surveyed 25(OH)D and various factors using a sample (n = 2,641) extracted from the Saudi Health Interview Survey (2013). Linear regression and finite mixture models (FMM) were estimated and compared. The number of latent classes in the FMM was chosen based on BIC score. Result Three latent classes were identified. Class I (39.82%), class II (41.03%), and class III (19.15%) with mean 25(OH)D levels of 22.79, 34.88, and 57.45 ng/ml respectively. Distinct patterns of associations with nutrition, behaviour and socio-demographic variables were recorded across classes that were not revealed in pooled linear regression. Conclusion FMM has the potential to provide additional insights on the relationship between 25(OH)D levels and observable characteristics. It should be more widely considered as a method of investigation in this area.

scholarly journals Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models

Risks ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 10 ◽  
Author(s):  
Lluís Bermúdez ◽  
Dimitris Karlis ◽  
Isabel Morillo
Keyword(s):  
Mixture Models ◽  
Finite Mixture Models ◽  
Unobserved Heterogeneity ◽  
Finite Mixture ◽  
Insurance Claim ◽  
Poisson Models ◽  
Excess Of Zeros ◽  
Car Insurance ◽  
Insurance Data ◽  
Natural Way

When modelling insurance claim count data, the actuary often observes overdispersion and an excess of zeros that may be caused by unobserved heterogeneity. A common approach to accounting for overdispersion is to consider models with some overdispersed distribution as opposed to Poisson models. Zero-inflated, hurdle and compound frequency models are typically applied to insurance data to account for such a feature of the data. However, a natural way to deal with unobserved heterogeneity is to consider mixtures of a simpler models. In this paper, we consider k-finite mixtures of some typical regression models. This approach has interesting features: first, it allows for overdispersion and the zero-inflated model represents a special case, and second, it allows for an elegant interpretation based on the typical clustering application of finite mixture models. k-finite mixture models are applied to a car insurance claim dataset in order to analyse whether the problem of unobserved heterogeneity requires a richer structure for risk classification. Our results show that the data consist of two subpopulations for which the regression structure is different.

Finite Mixture Models

2019 ◽  
Vol 6 (1) ◽  
pp. 355-378 ◽  
Author(s):  
Geoffrey J. McLachlan ◽  
Sharon X. Lee ◽  
Suren I. Rathnayake
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Mixture Models ◽  
Scientific Literature ◽  
Finite Mixture Models ◽  
Group Structure ◽  
Finite Mixture ◽  
Potential Usefulness ◽  
General Scientific

The important role of finite mixture models in the statistical analysis of data is underscored by the ever-increasing rate at which articles on mixture applications appear in the statistical and general scientific literature. The aim of this article is to provide an up-to-date account of the theory and methodological developments underlying the applications of finite mixture models. Because of their flexibility, mixture models are being increasingly exploited as a convenient, semiparametric way in which to model unknown distributional shapes. This is in addition to their obvious applications where there is group-structure in the data or where the aim is to explore the data for such structure, as in a cluster analysis. It has now been three decades since the publication of the monograph by McLachlan & Basford (1988) with an emphasis on the potential usefulness of mixture models for inference and clustering. Since then, mixture models have attracted the interest of many researchers and have found many new and interesting fields of application. Thus, the literature on mixture models has expanded enormously, and as a consequence, the bibliography here can only provide selected coverage.

scholarly journals A Finite Mixture Modelling Perspective for Combining Experts’ Opinions with an Application to Quantile-Based Risk Measures

Risks ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 115
Author(s):  
Despoina Makariou ◽  
Pauline Barrieu ◽  
George Tzougas
Keyword(s):  
Decision Making ◽  
Mixture Models ◽  
Finite Mixture Models ◽  
Risk Measures ◽  
Finite Mixture ◽  
Parametric Family ◽  
Collective Decision Making ◽  
Multiple Sources ◽  
Mixture Modelling ◽  
Expert Opinions

The key purpose of this paper is to present an alternative viewpoint for combining expert opinions based on finite mixture models. Moreover, we consider that the components of the mixture are not necessarily assumed to be from the same parametric family. This approach can enable the agent to make informed decisions about the uncertain quantity of interest in a flexible manner that accounts for multiple sources of heterogeneity involved in the opinions expressed by the experts in terms of the parametric family, the parameters of each component density, and also the mixing weights. Finally, the proposed models are employed for numerically computing quantile-based risk measures in a collective decision-making context.

IFRS Adoption and Audit Delay: The Role of Shareholders in the Audit Committee

2018 ◽  
Vol 8 (1) ◽  
pp. 325
Author(s):  
Ishaq Ahmed Mohammed ◽  
Ayoib Che-Ahmad ◽  
Mazrah Malek
Keyword(s):  
Audit Committee ◽  
Unobserved Heterogeneity ◽  
Value Relevance ◽  
Stock Exchange ◽  
Delay Model ◽  
Corporate Reporting ◽  
Ifrs Adoption ◽  
The Difference ◽  
The Relationship

This study examined the relationship between audit delay after IFRS adoption and the role of shareholders in the audit committee as well as testing the difference of pre-and post IFRS adoption periods. A sample of 101 firms with 505 firm-year observations over five year period for firms listed on the Nigeria Stock Exchange was employed for the study, utilizing data from the annual report and accounts of the sample firms. Generalized Methods of Moment (GMM) estimation was used to check the effects of unobserved heterogeneity in audit delay model, while the test of difference in R2 value for pre-and post-adoption periods was determined using Cramer’s Z-statistics. Findings indicate that audit report lag is faster with shareholders in the audit committee. The study proved that brand named auditors such as Big4 can significantly perform faster audit task than non-Big4 firms in IFRS regime. The importance of the study’s findings demonstrates statistical inference on value relevance increase based on the unique IFRS adoption in Nigeria. Thus, regulators should consider increasing the tenure of shareholders in the audit committee to enable them to become more familiar with the corporate reporting under IFRS regime.

scholarly journals Non-local spatially varying finite mixture models for image segmentation

2021 ◽  
Vol 31 (1) ◽  
Author(s):  
Javier Juan-Albarracín ◽  
Elies Fuster-Garcia ◽  
Alfons Juan ◽  
Juan M. García-Gómez
Keyword(s):  
Image Segmentation ◽  
Mixture Models ◽  
Finite Mixture Models ◽  
Finite Mixture ◽  
Non Local ◽  
Spatially Varying
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