An improved self-adaptive membrane computing optimization algorithm and its applications in residue hydrogenating model parameter estimation

2015 ◽  
Vol 22 (10) ◽  
pp. 3909-3915 ◽  
Author(s):  
Hui-bin Lu ◽  
Cui-mei Bo ◽  
Shi-pin Yang
Keyword(s):  
Parameter Estimation ◽  
Optimization Algorithm ◽  
Membrane Computing ◽  
Model Parameter ◽  
Model Parameter Estimation ◽  
Self Adaptive

Model parameter estimation of the PEMFCs using improved Barnacles Mating Optimization algorithm

Energy ◽  
2020 ◽  
Vol 212 ◽  
pp. 118738
Author(s):  
Zixuan Yang ◽  
Qian Liu ◽  
Leiyu Zhang ◽  
Jialei Dai ◽  
Navid Razmjooy
Keyword(s):  
Parameter Estimation ◽  
Optimization Algorithm ◽  
Model Parameter ◽  
Model Parameter Estimation

scholarly journals Optimizing the Estimation of a Histogram-Bin Width—Application to the Multivariate Mixture-Model Estimation

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1090 ◽  
Author(s):  
Branislav Panić ◽  
Jernej Klemenc ◽  
Marko Nagode
Keyword(s):  
Parameter Estimation ◽  
Mixture Model ◽  
Optimization Algorithm ◽  
Likelihood Estimation ◽  
Gaussian Mixture ◽  
Difficult Problem ◽  
Integer Optimization ◽  
Model Parameter ◽  
Model Parameter Estimation ◽  
Histogram Estimation

A maximum-likelihood estimation of a multivariate mixture model’s parameters is a difficult problem. One approach is to combine the REBMIX and EM algorithms. However, the REBMIX algorithm requires the use of histogram estimation, which is the most rudimentary approach to an empirical density estimation and has many drawbacks. Nevertheless, because of its simplicity, it is still one of the most commonly used techniques. The main problem is to estimate the optimum histogram-bin width, which is usually set by the number of non-overlapping, regularly spaced bins. For univariate problems it is usually denoted by an integer value; i.e., the number of bins. However, for multivariate problems, in order to obtain a histogram estimation, a regular grid must be formed. Thus, to obtain the optimum histogram estimation, an integer-optimization problem must be solved. The aim is therefore the estimation of optimum histogram binning, alone and in application to the mixture model parameter estimation with the REBMIX&EM strategy. As an estimator, the Knuth rule was used. For the optimization algorithm, a derivative based on the coordinate-descent optimization was composed. These proposals yielded promising results. The optimization algorithm was efficient and the results were accurate. When applied to the multivariate, Gaussian-mixture-model parameter estimation, the results were competitive. All the improvements were implemented in the rebmix R package.

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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