scholarly journals Model Population Analysis for Statistical Model Comparison

10.5772/31561 ◽  
2012 ◽  
Author(s):  
Hong-Dong Li ◽  
Yi-Zeng Liang ◽  
Qing-Song Xu
Keyword(s):  
Statistical Model ◽  
Model Comparison ◽  
Population Analysis ◽  
Model Population

Model population analysis in chemometrics

2015 ◽  
Vol 149 ◽  
pp. 166-176 ◽  
Author(s):  
Bai-Chuan Deng ◽  
Yong-Huan Yun ◽  
Yi-Zeng Liang
Keyword(s):  
Population Analysis ◽  
Model Population

Recipe for uncovering predictive genes using support vector machines based on model population analysis

2011 ◽  
Vol 8 (6) ◽  
pp. 1633-1641 ◽  
Author(s):  
Hong-Dong Li ◽  
Yi-Zeng Liang ◽  
Qing-Song Xu ◽  
Dong-Sheng Cao ◽  
Bin-Bin Tan ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Population Analysis ◽  
Support Vector ◽  
Model Population ◽  
Vector Machines

scholarly journals Model population analysis for variable selection

2010 ◽  
Vol 24 (7-8) ◽  
pp. 418-423 ◽  
Author(s):  
Hong-Dong Li ◽  
Yi-Zeng Liang ◽  
Qing-Song Xu ◽  
Dong-Sheng Cao
Keyword(s):  
Variable Selection ◽  
Population Analysis ◽  
Model Population

A new strategy to prevent over-fitting in partial least squares models based on model population analysis

2015 ◽  
Vol 880 ◽  
pp. 32-41 ◽  
Author(s):  
Bai-Chuan Deng ◽  
Yong-Huan Yun ◽  
Yi-Zeng Liang ◽  
Dong-Sheng Cao ◽  
Qing-Song Xu ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Population Analysis ◽  
Model Population ◽  
New Strategy ◽  
Fitting In

Recipe for revealing informative metabolites based on model population analysis

Metabolomics ◽  
2010 ◽  
Vol 6 (3) ◽  
pp. 353-361 ◽  
Author(s):  
Hong-Dong Li ◽  
Mao-Mao Zeng ◽  
Bin-Bin Tan ◽  
Yi-Zeng Liang ◽  
Qing-Song Xu ◽  
...  
Keyword(s):  
Population Analysis ◽  
Model Population

Post-analysis Procedures

2021 ◽  
pp. 175-192
Author(s):  
Joseph A. Veech
Keyword(s):  
Statistical Model ◽  
Model Comparison ◽  
Predictive Accuracy ◽  
False Negative ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Error Rates ◽  
True Negative ◽  
Habitat Analysis ◽  
Formal Procedure

There are several additional statistical procedures that can be conducted after a habitat analysis. The statistical model produced by a habitat analysis can be assessed for fit to the data. Model fit describes how well the predictor variables explain the variance in the response variable, typically species presence–absence or abundance. When more than one statistical model has been produced by the habitat analysis, these can be compared by a formal procedure called model comparison. This usually involves identifying the model with the lowest Akaike information criterion (AIC) value. If the statistical model is considered a predictive tool then its predictive accuracy needs to be assessed. There are many metrics for assessing the predictive performance of a model and quantifying rates of correct and incorrect classification; the latter are error rates. Many of these metrics are based on the numbers of true positive, true negative, false positive, and false negative observations in an independent dataset. “True” and “false” refer to whether species presence–absence was correctly predicted or not. Predictive performance can also be assessed by constructing a receiver operating characteristic (ROC) curve and calculating area under the curve (AUC) values. High AUC values approaching 1 indicate good predictive performance, whereas a value near 0.5 indicates a poor model that predicts species presence–absence no better than a random guess.

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