scholarly journals Bayesian hierarchical modeling of infant mortality in Nigeria

2019 ◽  
Vol 25 (2) ◽  
pp. 175-183
Author(s):  
Oluwayemisi Oyeronke Alaba ◽  
Chidinma Godwin
Keyword(s):  
Infant Mortality ◽  
Birth Interval ◽  
Hierarchical Modeling ◽  
Tertiary Education ◽  
Bayesian Hierarchical Modeling ◽  
Bayesian Hierarchical ◽  
North West ◽  
Individual Level ◽  
Infant Deaths ◽  
Using Data

Infant mortality and its risk factors in Nigeria was investigated using Bayesian hierarchical modeling. The hierarchical nature of the problem was examined to detect the within and between groups (states and regions) variations in infant deaths. The effect of individual level variables on the risk of a child dying before the age of one was determined using data collected from the fifth round Multiple Indicator Survey (MICS5, 2016-2017). Infants in Northern Nigeria had a higher risk of dying than others, especially in North West, while South West had the lowest risk of infant deaths. Ten percent of the variations in infant deaths was explained by differences between states while differences between regions explained only seven percent of the variations. Also, factors such as urban place of residence, mothers with secondary and tertiary education, first birth and birth interval above 2 years were associated with a decreased risk of infant deaths. Male infants, birth interval of less than 2 years, mothers with primary and no education, teenage mothers and mothers that gave birth at age 35 years and above were associated with a higher risk of infant mortality.

scholarly journals Bayesian Hierarchical Modeling of the Temporal Dynamics of Subjective Well-Being: A 10 Year Longitudinal Analysis

2018 ◽  
Author(s):  
Jeromy Anglim ◽  
Melissa K. Weinberg ◽  
Robert A. Cummins
Keyword(s):  
Temporal Dynamics ◽  
Hierarchical Modeling ◽  
Error Variance ◽  
Well Being ◽  
Small Subset ◽  
Systematic Change ◽  
Bayesian Hierarchical Modeling ◽  
Subjective Well Being ◽  
Bayesian Hierarchical ◽  
Individual Level

This study demonstrates, for the first time, how Bayesian hierarchical modeling can be applied to yield novel insights into the long-term temporal dynamics of subjective well-being (SWB). Several models were proposed and examined using Bayesian methods. The models were assessed using a sample of Australian adults (n = 1081) who provided annual SWB scores on between 5 and 10 occasions. The best fitting models involved a probit transformation, allowed error variance to vary across participants, and did not include a lag parameter. Including a random linear and quadratic effect resulted in only a small improvement over the intercept only model. Examination of individual-level fits suggested that most participants were stable with a small subset exhibiting patterns of systematic change.

scholarly journals Variance prior specification for a basket trial design using Bayesian hierarchical modeling

2018 ◽  
Vol 16 (2) ◽  
pp. 142-153 ◽  
Author(s):  
Kristen M Cunanan ◽  
Alexia Iasonos ◽  
Ronglai Shen ◽  
Mithat Gönen
Keyword(s):  
Standard Deviation ◽  
Prior Distribution ◽  
Hierarchical Modeling ◽  
Bayesian Hierarchical Modeling ◽  
Bayesian Hierarchical ◽  
Inverse Gamma ◽  
Wide Range ◽  
Basket Trial ◽  
Basket Trials ◽  
Variance Parameter

Background: In the era of targeted therapies, clinical trials in oncology are rapidly evolving, wherein patients from multiple diseases are now enrolled and treated according to their genomic mutation(s). In such trials, known as basket trials, the different disease cohorts form the different baskets for inference. Several approaches have been proposed in the literature to efficiently use information from all baskets while simultaneously screening to find individual baskets where the drug works. Most proposed methods are developed in a Bayesian paradigm that requires specifying a prior distribution for a variance parameter, which controls the degree to which information is shared across baskets. Methods: A common approach used to capture the correlated binary endpoints across baskets is Bayesian hierarchical modeling. We evaluate a Bayesian adaptive design in the context of a non-randomized basket trial and investigate three popular prior specifications: an inverse-gamma prior on the basket-level variance, a uniform prior and half-t prior on the basket-level standard deviation. Results: From our simulation study, we can see that the inverse-gamma prior is highly sensitive to the input hyperparameters. When the prior mean value of the variance parameter is set to be near zero [Formula: see text], this can lead to unacceptably high false-positive rates [Formula: see text] in some scenarios. Thus, use of this prior requires a fully comprehensive sensitivity analysis before implementation. Alternatively, we see that a prior that places sufficient mass in the tail, such as the uniform or half-t prior, displays desirable and robust operating characteristics over a wide range of prior specifications, with the caveat that the upper bound of the uniform prior and the scale parameter of the half-t prior must be larger than 1. Conclusion: Based on the simulation results, we recommend that those involved in designing basket trials that implement hierarchical modeling avoid using a prior distribution that places a majority of the density mass near zero for the variance parameter. Priors with this property force the model to share information regardless of the true efficacy configuration of the baskets. Many commonly used inverse-gamma prior specifications have this undesirable property. We recommend to instead consider the more robust uniform prior or half-t prior on the standard deviation.

A bank-account-information-based credit scoring method with Bayesian hierarchical modeling

2021 ◽  
pp. 2150036
Author(s):  
Suguru Yamanaka ◽  
Rei Yamamoto
Keyword(s):  
Hierarchical Modeling ◽  
Credit Scoring ◽  
Information Criterion ◽  
Bayesian Hierarchical Modeling ◽  
Scoring Method ◽  
Bank Account ◽  
Bayesian Hierarchical ◽  
Account Information ◽  
Activity Information ◽  
Financial Technology

Recent interest in financial technology (fintech) lending business has caused increasing challenges of credit scoring models using bank account activity information. Our work aims to develop a new credit scoring method based on bank account activity information. This method incorporates borrower firms’ segment-level heterogeneity, such as a segment of sales size and firm age. We employ Bayesian hierarchical modeling, which mitigates data sparsity issue due to data segmentation. We describe our modeling procedures, including data handling and variable selection. Empirical results show that our model outperforms the traditional logistic model for credit scoring in information criterion. Our model realizes advanced credit scoring based on bank account activity information in fintech lending businesses, taking segment-specific features into credit risk assessment.

scholarly journals A Bayesian Hierarchical Modeling Framework for Correcting Reporting Bias in the U.S. Tornado Database

2018 ◽  
Vol 34 (1) ◽  
pp. 15-30 ◽  
Author(s):  
Corey K. Potvin ◽  
Chris Broyles ◽  
Patrick S. Skinner ◽  
Harold E. Brooks ◽  
Erik Rasmussen
Keyword(s):  
Rural Areas ◽  
Hierarchical Modeling ◽  
Reporting Rate ◽  
Bayesian Hierarchical Modeling ◽  
Road Density ◽  
Modeling Framework ◽  
Bayesian Hierarchical ◽  
Adopted Model ◽  
Central United States ◽  
Prediction Systems

Abstract The Storm Prediction Center (SPC) tornado database, generated from NCEI’s Storm Data publication, is indispensable for assessing U.S. tornado risk and investigating tornado–climate connections. Maximizing the value of this database, however, requires accounting for systemically lower reported tornado counts in rural areas owing to a lack of observers. This study uses Bayesian hierarchical modeling to estimate tornado reporting rates and expected tornado counts over the central United States during 1975–2016. Our method addresses a serious solution nonuniqueness issue that may have affected previous studies. The adopted model explains 73% (>90%) of the variance in reported counts at scales of 50 km (>100 km). Population density explains more of the variance in reported tornado counts than other examined geographical covariates, including distance from nearest city, terrain ruggedness index, and road density. The model estimates that approximately 45% of tornadoes within the analysis domain were reported. The estimated tornado reporting rate decreases sharply away from population centers; for example, while >90% of tornadoes that occur within 5 km of a city with population > 100 000 are reported, this rate decreases to <70% at distances of 20–25 km. The method is directly extendable to other events subject to underreporting (e.g., severe hail and wind) and could be used to improve climate studies and tornado and other hazard models for forecasters, planners, and insurance/reinsurance companies, as well as for the development and verification of storm-scale prediction systems.

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