model averaging
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2023 ◽  
Jun Liao ◽  
Alan Wan ◽  
Shuyuan He ◽  
Guohua Zou

2022 ◽  
Vol 167 ◽  
pp. 107351
René-Marcel Kruse ◽  
Alexander Silbersdorff ◽  
Benjamin Säfken

2022 ◽  
Vol 217 ◽  
pp. 204-223
Yang Bai ◽  
Rongjie Jiang ◽  
Mengli Zhang

2022 ◽  
Vol 303 ◽  
pp. 114168
Maryam Gharekhani ◽  
Ata Allah Nadiri ◽  
Rahman Khatibi ◽  
Sina Sadeghfam ◽  
Asghar Asghari Moghaddam

2022 ◽  
Vol 209 ◽  
pp. 105791
Dongxue Zhao ◽  
Jie Wang ◽  
Xueyu Zhao ◽  
John Triantafilis

Stat ◽  
2022 ◽  
Min Cai ◽  
Ruige Zhuang ◽  
Zhou Yu ◽  
Ping Wu

2022 ◽  
Vol 22 (1) ◽  
Julia Ledien ◽  
Zulma M. Cucunubá ◽  
Gabriel Parra-Henao ◽  
Eliana Rodríguez-Monguí ◽  
Andrew P. Dobson ◽  

AbstractAge-stratified serosurvey data are often used to understand spatiotemporal trends in disease incidence and exposure through estimating the Force-of-Infection (FoI). Typically, median or mean FoI estimates are used as the response variable in predictive models, often overlooking the uncertainty in estimated FoI values when fitting models and evaluating their predictive ability. To assess how this uncertainty impact predictions, we compared three approaches with three levels of uncertainty integration. We propose a performance indicator to assess how predictions reflect initial uncertainty.In Colombia, 76 serosurveys (1980–2014) conducted at municipality level provided age-stratified Chagas disease prevalence data. The yearly FoI was estimated at the serosurvey level using a time-varying catalytic model. Environmental, demographic and entomological predictors were used to fit and predict the FoI at municipality level from 1980 to 2010 across Colombia.A stratified bootstrap method was used to fit the models without temporal autocorrelation at the serosurvey level. The predictive ability of each model was evaluated to select the best-fit models within urban, rural and (Amerindian) indigenous settings. Model averaging, with the 10 best-fit models identified, was used to generate predictions.Our analysis shows a risk of overconfidence in model predictions when median estimates of FoI alone are used to fit and evaluate models, failing to account for uncertainty in FoI estimates. Our proposed methodology fully propagates uncertainty in the estimated FoI onto the generated predictions, providing realistic assessments of both central tendency and current uncertainty surrounding exposure to Chagas disease.

2022 ◽  
pp. 096228022110417
Kian Wee Soh ◽  
Thomas Lumley ◽  
Cameron Walker ◽  
Michael O’Sullivan

In this paper, we present a new model averaging technique that can be applied in medical research. The dataset is first partitioned by the values of its categorical explanatory variables. Then for each partition, a model average is determined by minimising some form of squared errors, which could be the leave-one-out cross-validation errors. From our asymptotic optimality study and the results of simulations, we demonstrate under several high-level assumptions and modelling conditions that this model averaging procedure may outperform jackknife model averaging, which is a well-established technique. We also present an example where a cross-validation procedure does not work (that is, a zero-valued cross-validation error is obtained) when determining the weights for model averaging.

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