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Journal of Applied Sciences
  Year: 2011 | Volume: 11 | Issue: 1 | Page No.: 26-35
DOI: 10.3923/jas.2011.26.35
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Detection of Outliers and Influential Observations in Binary Logistic Regression: An Empirical Study

S.K. Sarkar, Habshah Midi and Sohel Rana

Logistic regression is one of the most frequently used statistical methods as a standard method of data analysis in many fields over the last decade. However, analysis of residuals and identification of influential outliers are not studied so frequently to check the adequacy of the fitted logistic regression model. Detection of outliers and influential cases and corresponding treatment is very crucial task of any modeling exercise. A failure to detect influential cases can have severe distortion on the validity of the inferences drawn from such modeling. The aim of this study is to evaluate different measures of standardized residuals and diagnostic statistics by graphical methods to identify potential outliers. Evaluation of diagnostic statistics and their graphical display detected 25 cases as outliers but they did not play notable effect on parameter estimates and summary measures of fits. It is recommended to use residual analysis and note outlying cases that can frequently lead to valuable insights for strengthening the model.
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  •    Modified Standardized Pearson Residual for the Identification of Outliers in Logistic Regression Model
  •    Mixed Logit Model on Multivariate Binary Response using Maximum Likelihood Estimator and Generalized Estimating Equations
  •    Estimation of Parameters in Heteroscedastic Multiple Regression Model using Leverage Based Near-Neighbors
  •    The Effect of Collinearity-influential Observations on Collinear Data Set: A Monte Carlo Simulation Study
  •    Importance of Assessing the Model Adequacy of Binary Logistic Regression
How to cite this article:

S.K. Sarkar, Habshah Midi and Sohel Rana, 2011. Detection of Outliers and Influential Observations in Binary Logistic Regression: An Empirical Study. Journal of Applied Sciences, 11: 26-35.

DOI: 10.3923/jas.2011.26.35






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