Bayesian Hierarchical Model for Measuring Adoption Rates in Municipal Water Systems in Rwanda
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| Natura: | Recurso digital |
| Lingua: | inglese |
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Zenodo
2001
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| _version_ | 1866901528134025216 |
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| author | Mutabazi, Kizito Bizimungu, Akalingo |
| author_facet | Mutabazi, Kizito Bizimungu, Akalingo |
| contents | <p>Municipal water systems in Rwanda face challenges related to adoption rates of new technologies and practices aimed at improving efficiency and sustainability. A Bayesian hierarchical model was developed using data from multiple municipal water systems. The model accounts for variability across different communities and incorporates covariates such as socio-economic status and infrastructure quality to estimate adoption rates accurately. The model revealed significant differences in adoption rates between urban and rural areas, with a clear trend indicating that higher levels of infrastructure investment correlate positively with increased adoption rates. Bayesian hierarchical models provide a robust framework for understanding adoption dynamics in municipal water systems across Rwanda. This methodological advancement offers valuable insights into policy-making and resource allocation strategies. Further research should explore the long-term impacts of adopted technologies on system performance and user satisfaction, as well as the potential for scaling up these findings to other regions with similar contexts. Bayesian hierarchical model, adoption rates, municipal water systems, Rwanda Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18728426 |
| institution | Zenodo |
| language | eng |
| publishDate | 2001 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Bayesian Hierarchical Model for Measuring Adoption Rates in Municipal Water Systems in Rwanda Mutabazi, Kizito Bizimungu, Akalingo Geographic Terms: Rwanda Methodological Terms: Bayesian Hierarchical Models Measurement Techniques Statistical Modelling Data Analysis Sustainability Evaluation Theoretical Terms: Adoption Rates Technological Adoption Hierarchical Bayesian Methods Latent Class Analysis Modelling Adoption Processes <p>Municipal water systems in Rwanda face challenges related to adoption rates of new technologies and practices aimed at improving efficiency and sustainability. A Bayesian hierarchical model was developed using data from multiple municipal water systems. The model accounts for variability across different communities and incorporates covariates such as socio-economic status and infrastructure quality to estimate adoption rates accurately. The model revealed significant differences in adoption rates between urban and rural areas, with a clear trend indicating that higher levels of infrastructure investment correlate positively with increased adoption rates. Bayesian hierarchical models provide a robust framework for understanding adoption dynamics in municipal water systems across Rwanda. This methodological advancement offers valuable insights into policy-making and resource allocation strategies. Further research should explore the long-term impacts of adopted technologies on system performance and user satisfaction, as well as the potential for scaling up these findings to other regions with similar contexts. Bayesian hierarchical model, adoption rates, municipal water systems, Rwanda Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.</p> |
| title | Bayesian Hierarchical Model for Measuring Adoption Rates in Municipal Water Systems in Rwanda |
| topic | Geographic Terms: Rwanda Methodological Terms: Bayesian Hierarchical Models Measurement Techniques Statistical Modelling Data Analysis Sustainability Evaluation Theoretical Terms: Adoption Rates Technological Adoption Hierarchical Bayesian Methods Latent Class Analysis Modelling Adoption Processes |
| url | https://doi.org/10.5281/zenodo.18728426 |