scholarly journals Comparison between the maximum likelihood and the bayesian estimation methods for logistic regression model (case study: risk of low birth weight in Indonesia)

2021 ◽  
Vol 2106 (1) ◽  
pp. 012001
Author(s):  
P R Sihombing ◽  
S R Rohimah ◽  
A Kurnia
Keyword(s):  
Logistic Regression ◽  
Birth Weight ◽  
Maximum Likelihood ◽  
Low Birth Weight ◽  
Regression Model ◽  
Bayesian Estimation ◽  
Logistic Regression Model ◽  
Estimation Method ◽  
Estimation Methods ◽  
Model Case

Abstract This study aims to compare the efficacy of logistic regression model for identifying the risk factors of low-birth-weight babies in Indonesia using the maximum likelihood estimation (MLE)and the Bayesian estimation methods. The data used in this study is secondary data derived from the 2017 Indonesian Demographic Health Survey with a total sample of 16,344 newborn babies. Selection of the best logistic regression model was based on the smaller Bayesian Schwartz Information Criterion (BIC) value. The logistic regression model with the Bayesian estimation method has a smaller BIC value than the MLE method. Twin births, baby girl, maternal age at risk, birth spacing that is too close, iron deficiency, low education, low economy, inadequate drinking water sources have provided a higher risk of low-birth-weight incidence.

scholarly journals Trends of proximate low birth weight and associations among children under-five years of age: Evidence from the 2016 Ethiopian demographic and health survey data

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246587
Author(s):  
Mesfin Wudu Kassaw ◽  
Ayele Mamo Abebe ◽  
Ayelign Mengesha Kassie ◽  
Biruk Beletew Abate ◽  
Seteamlak Adane Masresha
Keyword(s):  
Logistic Regression ◽  
Birth Weight ◽  
Low Birth Weight ◽  
Regression Model ◽  
Maternal Age ◽  
Logistic Regression Model ◽  
Maternal Education ◽  
Multiple Birth ◽  
Health Survey Data ◽  
Birth Type

Background Low birth weight puts a newborn at increased risk of death and illness, and limits their productivity in the adulthood period later. The incidence of low birth weight has been selected as an important indicator for monitoring major health goals by the World Summit for Children. The 2014 World Health Organization estimation of child death indicated that 4.53% of total deaths in Ethiopia were due to low birth weight. The aim of this study was to assess trends of proximate low birth weight and associations of low birth weight with potential determinants from 2011 to 2016. Methods This study used the 2016 Ethiopian Demographic and Health Survey data (EDHS) as data sources. According to the 2016 EDHS data, all the regions were stratified into urban and rural areas. The variable “size of child” measured according to the report of mothers before two weeks of the EDHS takes placed. The study sample refined from EDHS data and used for this further analysis were 7919 children. A logistic regression model was used to assess the association of proximate low birth weight and potential determinates of proximate low birth weight. But, the data were tested to model fitness and were fitted to Hosmer-Lemeshow-goodness of fit. Results The prevalence of proximate low birth weight in Ethiopia was 26.9% (2132), (95%CI = 25.4, 27.9). Of the prevalence of child size in year from 2011 to 2016, 17.1% was very small, and 9.8% was small. In the final multivariate logistic regression model, region (AOR = xx), (955%CI = xx), Afar (AOR = 2.44), (95%CI = 1.82, 3.27), Somalia (AOR = 0.73), (95%CI = 0.55, 0.97), Benishangul-Gumz (AOR = 0.48), (95%CI = 0.35, 0.67), SNNPR (AOR = 0.67), (95%CI = 0.48, 0.93), religion, Protestant (AOR = 0.76), (95%CI = 0.60, 0.95), residence, rural (AOR = 1.39), (95%CI = 1.07, 1.81), child sex, female (AOR = 1.43), (95%CI = 1.29, 1.59), birth type, multiple birth during first parity (AOR = 2.18), (95%CI = 1.41, 3.37), multiple birth during second parity (AOR = 2.92), (95%CI = 1.86, 4.58), preparedness for birth, wanted latter child (AOR = 1.26), (95%CI = 1.09, 1.47), fast and rapid breathing (AOR = 1.22), (95%CI = 1.02, 1.45), maternal education, unable to read and write (AOR = 1.46), (95%CI = 1.56, 2.17), and maternal age, 15–19 years old (AOR = 1.86), (95%CI = 1.19, 2.92) associated with proximate low birth weight. Conclusions The proximate LBW prevalence as indicated by small child size is high. Region, religion, residence, birth type, preparedness for birth, fast and rapid breathing, maternal education, and maternal age were associated with proximate low birth weight. Health institutions should mitigating measures on low birth weight with a special emphasis on factors identified in this study.

scholarly journals Spatial distribution and factors associated with low birth weight in Ethiopia using data from Ethiopian demographic and health survey 2016: spatial and multilevel analysis

2020 ◽  
Author(s):  
Alemneh Mekuriaw Liyew ◽  
Malede Mequanent Sisay ◽  
Achenef Asmamaw Muche
Keyword(s):  
Logistic Regression ◽  
Spatial Distribution ◽  
Birth Weight ◽  
Low Birth Weight ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Mixed Effects ◽  
Sub Saharan Africa ◽  
Factors Associated ◽  
Risk Areas

AbstractBackgroundLow birth weight (LBW) was a leading cause of neonatal mortality. It showed an increasing trend in Sub-Saharan Africa for the last one and half decade. Moreover, it was a public health problem in Ethiopia. Even though different studies were conducted to identify its predictors, contextual factors were insufficiently addressed in Ethiopia. There was also limited evidence on the spatial distribution of low birth weight. Therefore, this study aimed to explore spatial distribution and factors associated with low birth weight in Ethiopia.MethodSecondary data analysis was conducted using the 2016 EDHS data. A total of 1502 (weighted sample) mothers whose neonates were weighed at birth five years preceding the survey were included. GIS 10.1, SaTscan, stata, and Excel were used for data cleaning and analysis. A multi-level mixed-effects logistic regression model was fitted to identify factors associated with low birth weight. Finally, hotspot areas from GIS results, log-likelihood ratio (LLR) and relative risk with p-value of spatial scan statistics, AOR with 95% CI and random effects for mixed-effects logistic regression model were reported.ResultsLow birth weight was spatially clustered in Ethiopia. Primary (LLR=11.57; P=0.002) clusters were detected in the Amhara region. Whereas secondary (LLR=11.4; P=0.003;LLR=10.14,P=0.0075) clusters were identified at Southwest Oromia, north Oromia, south Afar, and Southeast Amhara regions. Being severely anemic (AOR=1.47;95%CI1.04,2.01), having no education (AOR=1.82;95%CI1.12,2.96), Prematurity (AOR=5.91;95%CI3.21,10.10) female neonate (AOR=1.38;95%CI1.04,1.84)were significantly associated with LBWConclusionLBW was spatially clustered in Ethiopia with high-risk areas in Amhara,Oromia, and Afar regions and it was affected by socio demographic factors. Therefore, focusing the policy intervention in those geogrsphically low birth weight risk areas and improving maternal education and nutrtion could be vital to reduce the low birth weight disparity in Ethiopia.

scholarly journals ESTIMATION OF THE BINARY LOGISTIC REGRESSION MODEL PARAMETER USING BOOTSTRAP RE-SAMPLING

2018 ◽  
Vol 48 (3) ◽  
pp. 199-204 ◽  
Author(s):  
R. LI ◽  
J. ZHOU ◽  
L. WANG
Keyword(s):  
Logistic Regression ◽  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
Parametric Bootstrap ◽  
Binary Logistic Regression Model ◽  
Bayesian Bootstrap ◽  
Non Parametric

In this paper, the non-parametric bootstrap and non-parametric Bayesian bootstrap methods are applied for parameter estimation in the binary logistic regression model. A real data study and a simulation study are conducted to compare the Nonparametric bootstrap, Non-parametric Bayesian bootstrap and the maximum likelihood methods. Study results shows that three methods are all effective ways for parameter estimation in the binary logistic regression model. In small sample case, the non-parametric Bayesian bootstrap method performs relatively better than the non-parametric bootstrap and the maximum likelihood method for parameter estimation in the binary logistic regression model.

scholarly journals Dynamic Simulation of Urban Expansion Based on Cellular Automata and Logistic Regression Model: Case Study of the Hyrcanian Region of Iran

2016 ◽  
Vol 8 (8) ◽  
pp. 810 ◽  
Author(s):  
Meisam Jafari ◽  
Hamid Majedi ◽  
Seyed Monavari ◽  
Ali Alesheikh ◽  
Mirmasoud Kheirkhah Zarkesh
Keyword(s):  
Logistic Regression ◽  
Cellular Automata ◽  
Regression Model ◽  
Dynamic Simulation ◽  
Logistic Regression Model ◽  
Urban Expansion ◽  
Model Case

Factors determining child mortality in Uganda

2017 ◽  
Vol 44 (5) ◽  
pp. 633-642 ◽  
Author(s):  
Will Kaberuka ◽  
Alex Mugarura ◽  
Javan Tindyebwa ◽  
Debra S. Bishop
Keyword(s):  
Health Care ◽  
Logistic Regression ◽  
Birth Weight ◽  
Regression Model ◽  
Marital Status ◽  
Birth Order ◽  
Child Mortality ◽  
Logistic Regression Model ◽  
Household Wealth ◽  
Content Type

Purpose The purpose of this paper is to establish socio-economic factors and maternal practices that determine child mortality in Uganda. Design/methodology/approach The paper examines the role of sex, birth weight, birth order and duration of breastfeeding of a child; age, marital status and education of the mother; and household wealth in determining child mortality. The study employs a logistic regression model to establish which of the factors significantly impacts child mortality in Uganda. Findings The study established that education level, age and marital status of the mother as well as household wealth significantly impact child mortality. Also important are the sex, birth weight, birth order and breastfeeding duration. Research limitations/implications Policies aimed at promoting breastfeeding and education of female children can make a significant contribution to the reduction of child mortality in Uganda. Practical implications Health care intervention programs should focus on single, poor and uneducated mothers as their children are at great risk due to poor and inadequate health care utilization. Originality/value This paper could be the first effort in examining child mortality status in Uganda using a logistic regression model.

The estimated parameter of logistic regression model by Markov Chain Monte Carlo method with multicollinearity

2020 ◽  
Vol 36 (4) ◽  
pp. 1253-1259
Author(s):  
Autcha Araveeporn ◽  
Yuwadee Klomwises
Keyword(s):  
Monte Carlo ◽  
Logistic Regression ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Maximum Likelihood ◽  
Regression Model ◽  
Ridge Regression ◽  
Logistic Regression Model ◽  
Classical Method ◽  
Likelihood Method

Markov Chain Monte Carlo (MCMC) method has been a popular method for getting information about probability distribution for estimating posterior distribution by Gibbs sampling. So far, the standard methods such as maximum likelihood and logistic ridge regression methods have represented to compare with MCMC. The maximum likelihood method is the classical method to estimate the parameter on the logistic regression model by differential the loglikelihood function on the estimator. The logistic ridge regression depends on the choice of ridge parameter by using crossvalidation for computing estimator on penalty function. This paper provides maximum likelihood, logistic ridge regression, and MCMC to estimate parameter on logit function and transforms into a probability. The logistic regression model predicts the probability to observe a phenomenon. The prediction accuracy evaluates in terms of the percentage with correct predictions of a binary event. A simulation study conducts a binary response variable by using 2, 4, and 6 explanatory variables, which are generated from multivariate normal distribution on the positive and negative correlation coefficient or called multicollinearity problem. The criterion of these methods is to compare by a maximum of predictive accuracy. The outcomes find that MCMC satisfies all situations.

Sign in / Sign up

Export Citation Format

Share Document