Parameter Estimation in a Hierarchical Random Intercept Model with Censored Response: An Approach using a SEM Algorithm and Gibbs Sampling

Sankhya B ◽  
2014 ◽  
Vol 76 (2) ◽  
pp. 210-233 ◽  
Author(s):  
Y. Slaoui ◽  
G. Nuel
Keyword(s):  
Parameter Estimation ◽  
Gibbs Sampling ◽  
Sem Algorithm ◽  
Random Intercept ◽  
Random Intercept Model ◽  
Censored Response

scholarly journals Environmental policy performance and its determinants: Application of a three-level random intercept model

Energy Policy ◽  
2018 ◽  
Vol 114 ◽  
pp. 134-144 ◽  
Author(s):  
Marzio Galeotti ◽  
Yana Rubashkina ◽  
Silvia Salini ◽  
Elena Verdolini
Keyword(s):  
Environmental Policy ◽  
Policy Performance ◽  
Random Intercept ◽  
Random Intercept Model

scholarly journals Multilevel Analysis of Determinants of Anemia Prevalence among Children Aged 6–59 Months in Ethiopia: Classical and Bayesian Approaches

Anemia ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Kemal N. Kawo ◽  
Zeytu G. Asfaw ◽  
Negusse Yohannes
Keyword(s):  
Public Health ◽  
Drinking Water ◽  
Multilevel Analysis ◽  
Health Problem ◽  
Public Health Problem ◽  
Wealth Index ◽  
Bayesian Approaches ◽  
Random Intercept ◽  
Random Intercept Model ◽  
Prevalence Of Anemia

Background. Anemia is a widely spread public health problem and affects individuals at all levels. However, there is a considerable regional variation in its distribution. Objective. Thus, this study aimed to assess and model the determinants of prevalence of anemia among children aged 6–59 months in Ethiopia. Data. Cross-sectional data from Ethiopian Demographic and Health Survey was used for the analysis. It was implemented by the Central Statistical Agency from 27 December 2010 through June 2011 and the sampling technique employed was multistage. Method. The statistical models that suit the hierarchical data such as variance components model, random intercept model, and random coefficients model were used to analyze the data. Likelihood and Bayesian approaches were used to estimate both fixed effects and random effects in multilevel analysis. Result. This study revealed that the prevalence of anemia among children aged between 6 and 59 months in the country was around 42.8%. The multilevel binary logistic regression analysis was performed to investigate the variation of predictor variables of the prevalence of anemia among children aged between 6 and 59 months. Accordingly, it has been identified that the number of children under five in the household, wealth index, age of children, mothers’ current working status, education level, given iron pills, size of child at birth, and source of drinking water have a significant effect on prevalence of anemia. It is found that variances related to the random term were statistically significant implying that there is variation in prevalence of anemia across regions. From the methodological aspect, it was found that random intercept model is better compared to the other two models in fitting the data well. Bayesian analysis gave consistent estimates with the respective multilevel models and additional solutions as posterior distribution of the parameters. Conclusion. The current study confirmed that prevalence of anemia among children aged 6–59 months in Ethiopia was severe public health problem, where 42.8% of them are anemic. Thus, stakeholders should pay attention to all significant factors mentioned in the analysis of this study but wealth index/improving household income and availability of pure drinking water are the most influential factors that should be improved anyway.

Box–Cox t random intercept model for estimating usual nutrient intake distributions

2018 ◽  
Vol 27 (4) ◽  
pp. 715-734
Author(s):  
Giovana Fumes-Ghantous ◽  
Silvia L. P. Ferrari ◽  
José Eduardo Corrente
Keyword(s):  
Nutrient Intake ◽  
Random Intercept ◽  
Random Intercept Model

A maximin criterion for the logistic random intercept model with covariates

2006 ◽  
Vol 136 (3) ◽  
pp. 962-981 ◽  
Author(s):  
Mario J.N.M. Ouwens ◽  
Frans E.S. Tan ◽  
Martijn P.F. Berger
Keyword(s):  
Random Intercept ◽  
Random Intercept Model

The Development of Topic Model Based on Beta-Negative Binomial Process

2013 ◽  
Vol 427-429 ◽  
pp. 1597-1600
Author(s):  
Ya Shu Liu ◽  
Han Bing Yan
Keyword(s):  
Data Mining ◽  
Parameter Estimation ◽  
Gibbs Sampling ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Topic Model ◽  
Negative Binomial Process ◽  
Binomial Process ◽  
Design Experiments

. Topic Model is one of the important subfields in Data Mining, which has been developed very quickly and has been applicated in many fields in recent years. Many researchers have been engaged in this field. In this paper, we introduce the BNB process based on Beta and Negative Binomial distribution, using the hierarchical distribution instead of Dirichlet in LDA. And we give the expression of parameter estimation used by Gibbs sampling. Then, BNB process is applicated in the text topic classification. We design experiments to decide the numbers of topics and compare the BNB process with LDA. Experiment results show that the BNB process has better performance over LDA in English Dataset, but they have almost the same result in Chinese micro-blog topic classification. Finally we analyze the problem and give the idea in further research.

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