Conditional Akaike information criterion for generalized linear mixed models

2012 ◽  
Vol 56 (3) ◽  
pp. 629-644 ◽  
Author(s):  
Dalei Yu ◽  
Kelvin K.W. Yau
2018 ◽  
Vol 68 (1) ◽  
pp. 105-111 ◽  
Author(s):  
Noriko Iwai

Abstract Call surveys are an effective technique for detecting the presence and activity of breeding male frogs. Such surveys have been used to quantify breeding activity at a site under the assumption that male chorusing activity appropriately reflects breeding consequences, such as the number of oviposition events. However, only a few studies have actually examined the relationship between chorusing activity and breeding consequences in the field. In this study, I examined the relationship between chorusing activity (the number of male calls recorded during a five-minute period every night) and the number of oviposition events (number of oviposited egg masses during the night) of the Otton frog, Babina subaspera, with regard to the time lag between calls and oviposition. I constructed nine generalized linear mixed models (GLMMs) to explain the number of oviposition events by chorusing activity on the same night and on nights 1 to 7 days before the oviposition events. The Akaike information criterion (AiC) of the GLMM was lowest when the number of calls from nights 2 days before the oviposition events was used, indicating that breeding consequences in Otton frogs reflect the chorusing activity of 2 days prior. This study shows that frog call surveys can be reliable tools with which to represent breeding activity at a site as long as the time lag between chorusing activity and breeding consequences is considered.


2021 ◽  
pp. 096228022110175
Author(s):  
Jan P Burgard ◽  
Joscha Krause ◽  
Ralf Münnich ◽  
Domingo Morales

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.


Biometrics ◽  
2004 ◽  
Vol 60 (4) ◽  
pp. 1043-1052 ◽  
Author(s):  
Yutaka Yasui ◽  
Ziding Feng ◽  
Paula Diehr ◽  
Dale McLerran ◽  
Shirley A. A. Beresford ◽  
...  

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