FACAIC: Model selection algorithm for the orthogonal factor model using AIC and CAIC

Psychometrika ◽  
1988 ◽  
Vol 53 (3) ◽  
pp. 407-415 ◽  
Author(s):  
Hamparsum Bozdogan ◽  
Donald E. Ramirez
Keyword(s):  
Model Selection ◽  
Factor Model ◽  
Selection Algorithm ◽  
Orthogonal Factor

scholarly journals Accounting for cell lineage and sex effects in the identification of cell-specific DNA methylation using a Bayesian model selection algorithm

PLoS ONE ◽  
2017 ◽  
Vol 12 (9) ◽  
pp. e0182455 ◽  
Author(s):  
Nicole White ◽  
Miles Benton ◽  
Daniel Kennedy ◽  
Andrew Fox ◽  
Lyn Griffiths ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Model Selection ◽  
Bayesian Model ◽  
Cell Lineage ◽  
Bayesian Model Selection ◽  
Selection Algorithm ◽  
Sex Effects

scholarly journals Non-linear contour-based multidirectional intra coding

2018 ◽  
Vol 7 ◽  
Author(s):  
Thorsten Laude ◽  
Jan Tumbrägel ◽  
Marco Munderloh ◽  
Jörn Ostermann
Keyword(s):  
Model Selection ◽  
Image Coding ◽  
Reference Area ◽  
Selection Algorithm ◽  
Still Image ◽  
Intra Coding ◽  
Coding Efficiency ◽  
Contour Model ◽  
Non Linear ◽  
Contour Models

AbstractIntra coding is an essential part of all video coding algorithms and applications. Additionally, intra coding algorithms are predestined for an efficient still image coding. To overcome limitations in existing intra coding algorithms (such as linear directional extrapolation, only one direction per block, small reference area), we propose non-linear Contour-based Multidirectional Intra Coding. This coding mode is based on four different non-linear contour models, on the connection of intersecting contours and on a boundary recall-based contour model selection algorithm. The different contour models address robustness against outliers for the detected contours and evasive curvature changes. Additionally, the information for the prediction is derived from already reconstructed pixels in neighboring blocks. The achieved coding efficiency is superior to those of related works from the literature. Compared with the closest related work, BD rate gains of 2.16% are achieved on average.

Application of a genetic algorithm based model selection algorithm for identification of carbide-based hot metal desulfurization

2020 ◽  
Vol 92 ◽  
pp. 106330
Author(s):  
Tero Vuolio ◽  
Ville-Valtteri Visuri ◽  
Aki Sorsa ◽  
Seppo Ollila ◽  
Timo Fabritius
Keyword(s):  
Genetic Algorithm ◽  
Model Selection ◽  
Selection Algorithm ◽  
Hot Metal ◽  
Hot Metal Desulfurization

A particle filter-based model selection algorithm for fatigue damage identification on aeronautical structures

2017 ◽  
Vol 24 (11) ◽  
pp. e2002 ◽  
Author(s):  
Francesco Cadini ◽  
Claudio Sbarufatti ◽  
Matteo Corbetta ◽  
Marco Giglio
Keyword(s):  
Model Selection ◽  
Particle Filter ◽  
Fatigue Damage ◽  
Damage Identification ◽  
Selection Algorithm

Predictive Modeling of Product Returns for Remanufacturing

2015 ◽  
Author(s):  
Jungmok Ma ◽  
Harrison M. Kim
Keyword(s):  
Model Selection ◽  
Predictive Model ◽  
Mixed Model ◽  
Moving Average ◽  
Predictive Performance ◽  
Performance Measure ◽  
Selection Algorithm ◽  
Product Returns ◽  
Distributed Lag

As awareness of environmental issues increases, the pressures from the public and policy makers have forced OEMs to consider remanufacturing as the key product design option. In order to make the remanufacturing operations more profitable, forecasting product returns is critical with regards to the uncertainty in quantity and timing. This paper proposes a predictive model selection algorithm to deal with the uncertainty by identifying better predictive models. Unlike other major approaches in literature (distributed lag model or DLM), the predictive model selection algorithm focuses on the predictive power over new or future returns. The proposed algorithm extends the set of candidate models that should be considered: autoregressive integrated moving average or ARIMA (previous returns for future returns), DLM (previous sales for future returns), and mixed model (both previous sales and returns for future returns). The prediction performance measure from holdout samples is used to find a better model among them. The case study of reusable bottles shows that one of the candidate models, ARIMA, can predict better than the DLM depending on the relationships between returns and sales. The univariate model is widely unexplored due to the criticism that the model cannot utilize the previous sales. Another candidate model, mixed model, can provide a chance to find a better predictive model by combining the ARIMA and DLM. The case study also shows that the DLM in the predictive model selection algorithm can provide a good predictive performance when there are relatively strong and static relationships between returns and sales.

scholarly journals A delay damage model selection algorithm for NARX neural networks

1997 ◽  
Vol 45 (11) ◽  
pp. 2719-2730 ◽  
Author(s):  
Tsung-Nan Lin ◽  
C.L. Giles ◽  
B.G. Horne ◽  
Sun-Yuan Kung
Keyword(s):  
Neural Networks ◽  
Model Selection ◽  
Damage Model ◽  
Selection Algorithm
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