scholarly journals Model parameter estimation from data assimilation modeling: Temporal and spatial variability of the bottom drag coefficient

1998 ◽  
Vol 103 (C3) ◽  
pp. 5531-5549 ◽  
Author(s):  
David S. Ullman ◽  
Robert E. Wilson
Keyword(s):  
Parameter Estimation ◽  
Data Assimilation ◽  
Spatial Variability ◽  
Drag Coefficient ◽  
Model Parameter ◽  
Model Parameter Estimation ◽  
Temporal And Spatial Variability ◽  
Temporal And Spatial

scholarly journals Soil model parameter estimation with ensemble data assimilation

2009 ◽  
Vol 10 (2) ◽  
pp. 127-131 ◽  
Author(s):  
Biljana Orescanin ◽  
Borivoj Rajkovic ◽  
Milija Zupanski ◽  
Dusanka Zupanski
Keyword(s):  
Parameter Estimation ◽  
Data Assimilation ◽  
Model Parameter ◽  
Model Parameter Estimation ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Soil Model

l2-Penalized temporal logit-mixed models for the estimation of regional obesity prevalence over time

2021 ◽  
pp. 096228022110175
Author(s):  
Jan P Burgard ◽  
Joscha Krause ◽  
Ralf Münnich ◽  
Domingo Morales
Keyword(s):  
Parameter Estimation ◽  
Medical Treatment ◽  
Mixed Models ◽  
Generalized Linear Mixed Models ◽  
Linear Mixed Models ◽  
Obesity Prevalence ◽  
Model Parameter ◽  
Model Parameter Estimation ◽  
Public Health Reporting ◽  
Over Time

Obesity is considered to be one of the primary health risks in modern industrialized societies. Estimating the evolution of its prevalence over time is an essential element of public health reporting. This requires the application of suitable statistical methods on epidemiologic data with substantial local detail. Generalized linear-mixed models with medical treatment records as covariates mark a powerful combination for this purpose. However, the task is methodologically challenging. Disease frequencies are subject to both regional and temporal heterogeneity. Medical treatment records often show strong internal correlation due to diagnosis-related grouping. This frequently causes excessive variance in model parameter estimation due to rank-deficiency problems. Further, generalized linear-mixed models are often estimated via approximate inference methods as their likelihood functions do not have closed forms. These problems combined lead to unacceptable uncertainty in prevalence estimates over time. We propose an l2-penalized temporal logit-mixed model to solve these issues. We derive empirical best predictors and present a parametric bootstrap to estimate their mean-squared errors. A novel penalized maximum approximate likelihood algorithm for model parameter estimation is stated. With this new methodology, the regional obesity prevalence in Germany from 2009 to 2012 is estimated. We find that the national prevalence ranges between 15 and 16%, with significant regional clustering in eastern Germany.

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