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Justine B Nasejje

Showing results (1-10 of 8) with videos related to

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BMC Research Notes|September 9, 2017
Application of random survival forests in understanding the determinants of under-five child mortality in Uganda in the presence of covariates that satisfy the proportional and non-proportional hazards assumptionJustine B Nasejje, Henry Mwambi
BMJ Open|February 18, 2022
Use of a deep learning and random forest approach to track changes in the predictive nature of socioeconomic drivers of under-5 mortality rates in sub-Saharan AfricaJustine B Nasejje, Rendani Mbuvha, Henry Mwambi
Plos One|December 28, 2022
Statistical approaches to identifying significant differences in predictive performance between machine learning and classical statistical models for survival dataJustine B Nasejje, Albert Whata, Charles Chimedza
BMC Public Health|October 3, 2015
Understanding the determinants of under-five child mortality in Uganda including the estimation of unobserved household and community effects using both frequentist and Bayesian survival analysis approachesJustine B Nasejje, Henry G Mwambi, Thomas N O Achia
BMC Medical Research Methodology|July 30, 2017
A comparison of the conditional inference survival forest model to random survival forests based on a simulation study as well as on two applications with time-to-event dataJustine B Nasejje, Henry Mwambi, Keertan Dheda, et al.
Journal of Applied Statistics|August 6, 2025
Adapting and evaluating deep-pseudo neural network for survival data with time-varying covariatesAlbert Whata, Justine B Nasejje, Najmeh Nakhaei Rad, et al.
Scientific Reports|March 4, 2025
Breast cancer prediction based on gene expression data using interpretable machine learning techniquesGabriel Kallah-Dagadu, Mohanad Mohammed, Justine B Nasejje, et al.
BMC Women'S Health|March 25, 2025
Modeling timing of sexual debut among women in Zimbabwe using a Geoadditive Discrete-Time survival approachAlphonce Bere, Innocent Maposa, Zvifadzo Matsena-Zingoni, et al.
Pageof 1

Showing results (1-10 of 8) with videos related to

Sort By:
Pageof 1
BMC Research Notes|September 9, 2017
Application of random survival forests in understanding the determinants of under-five child mortality in Uganda in the presence of covariates that satisfy the proportional and non-proportional hazards assumptionJustine B Nasejje, Henry Mwambi
BMJ Open|February 18, 2022
Use of a deep learning and random forest approach to track changes in the predictive nature of socioeconomic drivers of under-5 mortality rates in sub-Saharan AfricaJustine B Nasejje, Rendani Mbuvha, Henry Mwambi
Plos One|December 28, 2022
Statistical approaches to identifying significant differences in predictive performance between machine learning and classical statistical models for survival dataJustine B Nasejje, Albert Whata, Charles Chimedza
BMC Public Health|October 3, 2015
Understanding the determinants of under-five child mortality in Uganda including the estimation of unobserved household and community effects using both frequentist and Bayesian survival analysis approachesJustine B Nasejje, Henry G Mwambi, Thomas N O Achia
BMC Medical Research Methodology|July 30, 2017
A comparison of the conditional inference survival forest model to random survival forests based on a simulation study as well as on two applications with time-to-event dataJustine B Nasejje, Henry Mwambi, Keertan Dheda, et al.
Journal of Applied Statistics|August 6, 2025
Adapting and evaluating deep-pseudo neural network for survival data with time-varying covariatesAlbert Whata, Justine B Nasejje, Najmeh Nakhaei Rad, et al.
Scientific Reports|March 4, 2025
Breast cancer prediction based on gene expression data using interpretable machine learning techniquesGabriel Kallah-Dagadu, Mohanad Mohammed, Justine B Nasejje, et al.
BMC Women'S Health|March 25, 2025
Modeling timing of sexual debut among women in Zimbabwe using a Geoadditive Discrete-Time survival approachAlphonce Bere, Innocent Maposa, Zvifadzo Matsena-Zingoni, et al.
Pageof 1