Treatment of nonignorable missing data when modeling unobserved heterogeneity with finite mixture models

2016 ◽  
Vol 59 (1) ◽  
pp. 159-171 ◽  
Author(s):  
Thomas Lehmann ◽  
Peter Schlattmann
Keyword(s):  
Missing Data ◽  
Mixture Models ◽  
Finite Mixture Models ◽  
Unobserved Heterogeneity ◽  
Finite Mixture ◽  
Nonignorable Missing Data ◽  
Nonignorable Missing

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.

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.

Accommodating Missing Data in Mixture Models for Classification by Opinion-Changing Behavior

2001 ◽  
Vol 26 (2) ◽  
pp. 233-268 ◽  
Author(s):  
Jennifer L. Hill
Keyword(s):  
Missing Data ◽  
Mixture Models ◽  
Finite Mixture Models ◽  
Finite Mixture Model ◽  
Finite Mixture ◽  
Pollution Reduction ◽  
Model Structure ◽  
Response Data ◽  
New Information ◽  
Do So

Popular theories in political science regarding opinion-changing behavior postulate the existence of one or both of two broad categories of people: those with stable opinions over time; and those who appear to hold no solid opinion and, when asked to make a choice, do so seemingly at random. The model presented here explores evidence for a third category: durable changers. People in this group will change their opinions in a rational, informed manner, after being exposed to new information. Survey data collected at four time points over nearly two years track Swiss citizens' readiness to support pollution-reduction policies. We analyzed the data using finite mixture models that allow estimation of the percentage in the poluation falling in each category for each question as well as the frequency of certain types of relevant behaviors within each category. These models extend the finite mixture model structure used in Hill and Kriesi (2001a,b) to accommodate missing response data. This extension increases the sample size by nearly 60% and weakens the missing-data assumptions required. We describe augmented models and fitting algorithms corresponding to different assumptions about the missing-data mechanism as well as the differences in results obtained.

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.

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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