Bayesian Hierarchical Growth Model for Experimental Data on the Effectiveness of an Incentive-Based Weight Reduction Method

2021 ◽  
Vol 12 (3) ◽  
pp. 1036-1047
Author(s):  
Md Azman Shahadan Et.al
Keyword(s):  
Growth Model ◽  
Weight Reduction ◽  
Dirichlet Process ◽  
Reduction Method ◽  
Growth Models ◽  
Dirichlet Process Mixture ◽  
Bayesian Hierarchical ◽  
Mixture Prior ◽  
Hierarchical Growth Model ◽  
Hierarchical Growth Models

The objective of this current research is to model the experimental data on the effectiveness of an incentive-based weight reduction method by using Bayesian hierarchical growth models. Three Bayesian hierarchical growth models are proposed, namely parametric Bayesian hierarchical growth model with correlated intercept and slope random effects model, parametric Bayesian hierarchical growth model with no correlated intercept and slope random effects model and semi-parametric Bayesian hierarchical growth model with Dirichlet process mixture prior model. The data is obtained from forty eight (48) students who had participated in an experiment on weight reduction method. The students were divided equally into two groups: single and pair groups. The experiment was carried out over the period of three months with a weight reading session for every two weeks.  At the end of the study, we had six repeated measures of each student’s weight in kg and some measures of covariates and factors.  Our results showed that the best model for the above data based on the Bayesian fit indexes and the models’ flexibility is the semi-parametric Bayesian hierarchical growth model with Dirichlet process mixture prior model. The results of the semi-parametric model showed that the ‘growth’ or reduction rates of the weight reduction experiment relate to the students’ gender, height in cm, experimental group (single or pair) and time in term of weeks.

Modeling Latent Information in Voting Data with Dirichlet Process Priors

2015 ◽  
Vol 23 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Richard Traunmüller ◽  
Andreas Murr ◽  
Jeff Gill
Keyword(s):  
Dirichlet Process ◽  
Unobserved Heterogeneity ◽  
Vote Choice ◽  
Dirichlet Process Mixture ◽  
Omitted Variables ◽  
Factors Affecting ◽  
Structural Integration ◽  
Mixture Prior ◽  
Latent Effects ◽  
Improved Model

We apply a specialized Bayesian method that helps us deal with the methodological challenge of unobserved heterogeneity among immigrant voters. Our approach is based ongeneralized linear mixed Dirichlet models(GLMDMs) where random effects are specified semiparametrically using a Dirichlet process mixture prior that has been shown to account for unobserved grouping in the data. Such models are drawn from Bayesian nonparametrics to help overcome objections handling latent effects with strongly informed prior distributions. Using 2009 German voting data of immigrants, we show that for difficult problems of missing key covariates and unexplained heterogeneity this approach provides (1) overall improved model fit, (2) smaller standard errors on average, and (3) less bias from omitted variables. As a result, the GLMDM changed our substantive understanding of the factors affecting immigrants' turnout and vote choice. Once we account for unobserved heterogeneity among immigrant voters, whether a voter belongs to the first immigrant generation or not is much less important than the extant literature suggests. When looking at vote choice, we also found that an immigrant's degree of structural integration does not affect the vote in favor of the CDU/CSU, a party that is traditionally associated with restrictive immigration policy.

scholarly journals Simultaneous Edit and Imputation For Household Data with Structural Zeros

2018 ◽  
Vol 7 (4) ◽  
pp. 498-519 ◽  
Author(s):  
Olanrewaju Akande ◽  
Andrés Barrientos ◽  
Jerome P Reiter
Keyword(s):  
Dirichlet Process ◽  
Missing Values ◽  
Secondary Data ◽  
Dirichlet Process Mixture ◽  
Bayesian Hierarchical ◽  
Household Data ◽  
Multivariate Relationships ◽  
Using Data ◽  
Structural Zeros ◽  
Multivariate Categorical

Abstract Multivariate categorical data nested within households often include reported values that fail edit constraints—for example, a participating household reports a child’s age as older than his biological parent’s age—and have missing values. Generally, agencies prefer datasets to be free from erroneous or missing values before analyzing them or disseminating them to secondary data users. We present a model-based engine for editing and imputation of household data based on a Bayesian hierarchical model that includes (i) a nested data Dirichlet process mixture of products of multinomial distributions as the model for the true latent values of the data, truncated to allow only households that satisfy all edit constraints, (ii) a model for the location of errors, and (iii) a reporting model for the observed responses in error. The approach propagates uncertainty due to unknown locations of errors and missing values, generates plausible datasets that satisfy all edit constraints, and can preserve multivariate relationships within and across individuals in the same household. We illustrate the approach using data from the 2012 American Community Survey.

scholarly journals Comparison of growth models to describe growth from birth to 6 years in a Beninese cohort of children with repeated measurements

BMJ Open ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. e035785
Author(s):  
Shukrullah Ahmadi ◽  
Florence Bodeau-Livinec ◽  
Roméo Zoumenou ◽  
André Garcia ◽  
David Courtin ◽  
...  
Keyword(s):  
Growth Model ◽  
Goodness Of Fit ◽  
Growth Models ◽  
Information Criterion ◽  
Height Growth ◽  
Sub Saharan Africa ◽  
Individual Growth ◽  
Secondary Outcome ◽  
Growth Trajectories ◽  
Best Fitting

ObjectiveTo select a growth model that best describes individual growth trajectories of children and to present some growth characteristics of this population.SettingsParticipants were selected from a prospective cohort conducted in three health centres (Allada, Sekou and Attogon) in a semirural region of Benin, sub-Saharan Africa.ParticipantsChildren aged 0 to 6 years were recruited in a cohort study with at least two valid height and weight measurements included (n=961).Primary and secondary outcome measuresThis study compared the goodness-of-fit of three structural growth models (Jenss-Bayley, Reed and a newly adapted version of the Gompertz growth model) on longitudinal weight and height growth data of boys and girls. The goodness-of-fit of the models was assessed using residual distribution over age and compared with the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The best-fitting model allowed estimating mean weight and height growth trajectories, individual growth and growth velocities. Underweight, stunting and wasting were also estimated at age 6 years.ResultsThe three models were able to fit well both weight and height data. The Jenss-Bayley model presented the best fit for weight and height, both in boys and girls. Mean height growth trajectories were identical in shape and direction for boys and girls while the mean weight growth curve of girls fell slightly below the curve of boys after neonatal life. Finally, 35%, 27.7% and 8% of boys; and 34%, 38.4% and 4% of girls were estimated to be underweight, wasted and stunted at age 6 years, respectively.ConclusionThe growth parameters of the best-fitting Jenss-Bayley model can be used to describe growth trajectories and study their determinants.

scholarly journals 0439 Nonlinear Dynamics Forecasting for Personalize Prognosis of Obstructive Sleep Apnea Onsets

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A168-A169
Author(s):  
T Le
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Real Time ◽  
Dirichlet Process ◽  
Clinical Symptoms ◽  
Risk Indicators ◽  
Dirichlet Process Mixture ◽  
Obstructive Sleep ◽  
Estimated Risk ◽  
Normal States

Abstract Introduction The emphasis on disease prevention, early detection, and preventive treatments will revolutionize the way sleep clinicians evaluate their patients. Obstructive Sleep Apnea (OSA) is one of the most prevalent sleep disorders with approximately 100 millions patients been diagnosed worldwide. The effectiveness of sleep disorder therapies can be enhanced by providing personalized and real-time prediction of OSA episode onsets. Previous attempts at OSA prediction are limited to capturing the nonlinear, nonstationary dynamics of the underlying physiological processes. Methods This paper reports an investigation into heart rate dynamics aiming to predict in real time the onsets of OSA episode before the clinical symptoms appear. The method includes (a) a representation of a transition state space network to characterize dynamic transition of apneic states (b) a Dirichlet-Process Mixture-Gaussian-Process prognostic method for estimating the distribution of the time estimate the remaining time until the onset of an impending OSA episode by considering the stochastic evolution of the normal states to an anomalous (apnea) Results The approach was tested using three datasets including (1) 20 records from 14 OSA subjects in benchmark ECG apnea databases (Physionet.org), (2) records of eight subjects from previous work. The average prediction accuracy (R2) is reported as 0.75%, with 87% of observations within the 95% confidence interval. Estimated risk indicators at 1 to 3 min till apnea onset are reported as 85.8 %, 80.2 %, and 75.5 %, respectively. Conclusion The present prognosis approach can be integrated with wearable devices to facilitate individualized treatments and timely prevention therapies. Support N/A

Sign in / Sign up

Export Citation Format

Share Document