Corpus ID: 237499945

Bayesian hierarchical analysis of a multifaceted program against extreme poverty

@inproceedings{Charlot2021BayesianHA,
  title={Bayesian hierarchical analysis of a multifaceted program against extreme poverty},
  author={Louis Charlot},
  year={2021}
}
The evaluation of a multifaceted program against extreme poverty in different developing countries gave encouraging results, but with important heterogeneity between countries. This master thesis proposes to study this heterogeneity with a Bayesian hierarchical analysis. The analysis we carry out with two different hierarchical models leads to a very low amount of pooling of information between countries, indicating that this observed heterogeneity should be interpreted mostly as true… Expand

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References

SHOWING 1-10 OF 42 REFERENCES
Understanding the Average Impact of Microcredit Expansions: A Bayesian Hierarchical Analysis of Seven Randomized Experiments
  • Rachael Meager
  • Computer Science
  • American Economic Journal: Applied Economics
  • 2019
TLDR
The average effect and the heterogeneity in effects across seven studies using Bayesian hierarchical models are estimated and reasonable external validity is found: true heterogeneity in results is moderate, and approximately 60 percent of observed heterogeneity is sampling variation. Expand
Passive smoking in the workplace: classical and Bayesian meta-analyses
TLDR
It is found that although all methods give reasonably similar combined estimates of relative risk of lung cancer associated with this exposure, the approximations arising from classical methods appear to be nonconservative and should be used with caution. Expand
An alternative approach to frequentist meta-analysis: A demonstration of Bayesian meta-analysis in adolescent development research.
TLDR
This introduction re-analyzes data from a meta-analysis concerning the impact of media literacy interventions on attitudes and intentions related to risky health behaviors using a Bayesian approach, and presents a non-technical introduction to Bayesian meta- analysis. Expand
Bayesian Measures of Explained Variance and Pooling in Multilevel (Hierarchical) Models
TLDR
This article presents an approach to defining R2 at each level of the multilevel model, rather than attempting to create a single summary measure of fit, based on comparing variances in a single fitted model rather than with a null model. Expand
Estimation in Parallel Randomized Experiments
Many studies comparing new treatments to standard treatments consist of parallel randomized experiments. In the example considered here, randomized experiments were conducted in eight schools toExpand
A multifaceted program causes lasting progress for the very poor: Evidence from six countries
TLDR
It is established that a multifaceted approach to increasing income and well-being for the ultrapoor is sustainable and cost-effective. Expand
Estimates of the impact of COVID-19 on global poverty
In this paper we make estimates of the potential short-term economic impact of COVID-19 on global monetary poverty through contractions in per capita household income or consumption. Our estimatesExpand
Bayesian data analysis.
  • J. Kruschke
  • Computer Science, Medicine
  • Wiley interdisciplinary reviews. Cognitive science
  • 2010
TLDR
A fatal flaw of NHST is reviewed and some benefits of Bayesian data analysis are introduced and illustrative examples of multiple comparisons in Bayesian analysis of variance and Bayesian approaches to statistical power are presented. Expand
Six Randomized Evaluations of Microcredit: Introduction and Further Steps †
Causal evidence on microcredit impacts informs theory, practice, and debates about its effectiveness as a development tool. The six randomized evaluations in this volume use a variety of sampling,Expand
Bayesian analysis of randomized controlled trials
TLDR
It is concluded that Bayesian methods are a powerful tool for incorporating data and prior information into models and have the potential to improve analyses of RCTs in eating disorders given small sample sizes, small effect size, abundant prior information, heterogeneous participants, and experimental design. Expand
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