scholarly journals Anℓ1-oracle inequality for the Lasso in finite mixture Gaussian regression models

2013 ◽  
Vol 17 ◽  
pp. 650-671 ◽  
Author(s):  
Caroline Meynet
Keyword(s):  
Regression Models ◽  
Finite Mixture ◽  
Oracle Inequality ◽  
Gaussian Regression

Finite Mixture Dynamic Regression Modeling of Panel Data With Implications for Dynamic Response Analysis

2005 ◽  
Vol 30 (2) ◽  
pp. 169-187 ◽  
Author(s):  
David Kaplan
Keyword(s):  
Probability Density Function ◽  
Dynamic Response ◽  
Regression Model ◽  
Density Function ◽  
Regression Models ◽  
Linear Models ◽  
Finite Mixture ◽  
Response Analysis ◽  
Model Parameters ◽  
Mixture Density

This article considers the problem of estimating dynamic linear regression models when the data are generated from finite mixture probability density function where the mixture components are characterized by different dynamic regression model parameters. Specifically, conventional linear models assume that the data are generated by a single probability density function characterized by a single set of regression model parameters. However, when the true generating model is finite mixture density function, then estimation of conventional linear models under the assumption of a single density function may lead to erroneous conclusions. Instead, it may be desirable to estimate the regression model under the assumption that the data are derived from a finite mixture density function and to examine differences in the parameters of the model within each mixture component. Dynamic regression models and subsequent dynamic response analysis using dynamic multipliers are also likely to be affected by the existence of a finite mixture density because dynamic multipliers are functions of the regression model parameters. Utilizing finite mixture modeling applied to two real data examples, this article shows that dynamic responses to changes in exogenous variables can be quite different depending on the number and nature of underlying mixture components. Implications for substantive conclusions based on the use of dynamic multipliers is discussed.

scholarly journals Finite mixture of compositional regression with gaussian errors

2018 ◽  
Vol 41 (1) ◽  
pp. 75-86
Author(s):  
Taciana Shimizu ◽  
Francisco Louzada ◽  
Adriano Suzuki
Keyword(s):  
Maximum Likelihood ◽  
Em Algorithm ◽  
Regression Model ◽  
Simulation Study ◽  
Regression Models ◽  
Finite Mixture ◽  
Maximum Likelihood Estimates ◽  
The Real ◽  
Compositional Structure ◽  
Volleyball Players

In this paper, we consider to evaluate the efficiency of volleyball players according to the performance of attack, block and serve, but considering the compositional structure of the data related to the fundaments. The finite mixture of regression models better fitted the data in comparison with the usual regression model. The maximum likelihood estimates are obtained via an EM algorithm. A simulation study revels that the estimates are closer to the real values, the estimators are asymptotically unbiased for the parameters. A real Brazilian volleyball dataset related to the efficiency of the players is considered for the analysis.

scholarly journals Self-Assessed Life Expectancy Among Older Adults in Côte D’Ivoire

2020 ◽  
Author(s):  
Richard K Moussa ◽  
Vakaramoko Diaby
Keyword(s):  
Older Adults ◽  
Life Expectancy ◽  
Cote D'ivoire ◽  
Regression Models ◽  
Côte D’Ivoire ◽  
Finite Mixture ◽  
Cote D’Ivoire ◽  
Survival Probabilities ◽  
Côte D'ivoire ◽  
Fourth Component

Abstract Background The purpose of this study was to estimate individuals’ expected longevity based on self-assessed survival probabilities and determine the predictors of such subjective life expectancy in a sample of elderly people (50 years and older) in Côte d’Ivoire. Methods Paper-based questionnaires were administered to a sample (n=267) of older adults residing in the city of Dabou, Côte d’Ivoire in May 2017. Information on subjective expectations regarding health, comorbidities, and self-assessed survival probabilities were collected. We estimated self-assessed life expectancy and its determinants using a two-pronged approach by: (i) estimating individuals’ life expectancy using the self-assessed survival probabilities (SSPs), and (ii) applying a finite mixture of regression models to form homogenous groups of individuals (clusters/components) and investigate the determinants. A spline-based approach was used to estimate the overall distribution of life expectancy for each individual using two to four points of self-assessed survival probabilities. A finite mixture of regression models was used to identify homogeneous groups of individuals (i.e. clusters/components) of the overall subjective life expectancy distribution of the study participants. Results The mean subjective life expectancy in older people varied according to four components/clusters. The average subjective life expectancy among the elderly was 79.51, 78.89, 80.02 and 77.79 years in the first, second, third and fourth component of the subjects' overall subjective life expectancy, respectively. The effect of sociodemographic characteristics, comorbidities, and lifestyle on subjective life expectancy varied across components. For instance, a U-shape relationship between household per capita income and subjective life expectancy was found for individuals classified into the third component, and an inverse U-shape relationship was found for individuals classified into the fourth component. Conclusions We extended the estimation of subjective life expectancy by accounting for heterogeneity in the distribution of the estimated subjective life expectancy. This approach improved the usual methods for estimating individual subjective life expectancies and may provide insight into the elderly’s perception of aging, which could be used to forecast the demand for health services and long-term care needs.

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