American Journal of Applied Mathematics and Statistics
ISSN (Print): 2328-7306 ISSN (Online): 2328-7292 Website: Editor-in-chief: Mohamed Seddeek
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American Journal of Applied Mathematics and Statistics. 2018, 6(4), 126-134
DOI: 10.12691/ajams-6-4-2
Open AccessArticle

Evaluating Methods of Assessing “Optimism” in Regression Models

Daniel Thoya1, , Antony Waititu1, Thomas Magheto1 and Antony Ngunyi2

1Department of Statistics and Actuarial Science, Jommo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

2Department of Statistics and Actuarial Science, Dedan Kimathi University of Science and Technology, Nyeri, Kenya

Pub. Date: July 20, 2018

Cite this paper:
Daniel Thoya, Antony Waititu, Thomas Magheto and Antony Ngunyi. Evaluating Methods of Assessing “Optimism” in Regression Models. American Journal of Applied Mathematics and Statistics. 2018; 6(4):126-134. doi: 10.12691/ajams-6-4-2


The purpose of this study was to evaluate the methods used to assess “optimism” in regression models. Particularly, focus was on the use of pseudo R2 values of cox &snail and the Nagelkerke to identify the best statistic for measuring “optimism” in regression models, measure model performance and determine the relationship between “optimism” and over fitting. Different underlying data sets assume different models that fit their data accurately. However, the fitted regression models usually fit the data they are based on better than new data. This is what we call ‘optimism’. Specific focus will be on determining the best statistic for measuring optimism in regression models, assess model performance using ‘optimism’ through cross-validation and also determining the relationship between optimism and over fitting of regression models. The study focus on three models (Cox-regression, Logistic regression and Linear Regression) and bootstrap procedure was used.

optimism pseudo-r-square cox & snell Nagelkerke

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