Detection of outliers and influential observations in binary logistic regression: An empirical study.

QR Code

Detection of outliers and influential observations in binary logistic regression: An empirical study.

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 r...

Full description

Bibliographic Details
Main Authors:	Sarkar, S.K., Midi, Habshah, Rana, Md. Sohel
Format:	Article
Language:	English
Published:	Asian Network for Scientific Information 2011
Online Access:	http://psasir.upm.edu.my/id/eprint/24988/

Similar Items

Collinearity diagnostics of binary logistic regression model
by: Midi, Habshah, et al.
Published: (2010)

Validation and performance analysis of binary logistic regression model
by: Rana, Md. Sohel, et al.
Published: (2010)

Importance of assessing the model adequacy of binary logistic regression.
by: Sarkar, S.K., et al.
Published: (2010)

Detection of influential observations in spatial regression model based on outliers and bad leverage classification
by: Mohammed Baba, Ali, et al.
Published: (2021)

Fixed parameters Support Vector Regression for outlier detection
by: Rana, Sohel, et al.
Published: (2018)

Diagnostics for residual outliers using deviance component in binary logistic regression.
by: Ahmad, Sanizah, et al.
Published: (2011)

Optimization techniques for variable selection in binary logistic regression model applied to desire for children data.
by: Sarkar, S.K., et al.
Published: (2009)

Determinants of desire for children: a multinomial logistic regression approach
by: Rana, Md. Sohel, et al.
Published: (2013)

Outlier detection in logistic regression and its application in medical data analysis
by: Ahmad, Sanizah, et al.
Published: (2012)

Detection of outliers in the response and explanatory variables of the simple circular regression model
by: Mahmood, Ehab A., et al.
Published: (2016)

Fast improvised influential distance for the identification of influential observations in multiple linear regression
by: Habshah Midi,, et al.
Published: (2021)

Fast improvised influential distance for the identification of influential observations in multiple linear regression
by: Midi, Habshah, et al.
Published: (2021)

Adequacy of multinomial logit model with nominal responses over binary logit model.
by: Midi, Habshah, et al.
Published: (2011)

Robust detection of outliers in both response and explanatory variables of the simple circular regression model
by: Rana, Sohel, et al.
Published: (2016)

The performance of classical and robust logistic regression estimators in the presence of outliers
by: Midi, Habshah, et al.
Published: (2012)

On a robust estimator in heteroscedastic regression model in the presence of outliers
by: Midi, Habshah, et al.
Published: (2013)

Modified standardized Pearson residual for the identification of outliers in logistic regression model
by: Midi, Habshah, et al.
Published: (2013)

Two-step robust estimator in heteroscedastic regression model in the presence of outliers
by: Midi, Habshah, et al.
Published: (2014)

Diagnostics of fitted binary logistic regression model based on individual subjects and covariate patterns
by: Sarkar, S. K., et al.
Published: (2010)

Effects of a single outlier on the coefficient of determination: an empirical study
by: Fitrianto, Anwar, et al.
Published: (2014)

Detection of influential observations in principle component regression
by: Mokhtar Abdullah,
Published: (1996)

Robust Diagnostics and Estimation in Heteroscedastic Regression Model in the Presence of Outliers
by: Rana, Md. Sohel
Published: (2010)

Robust circular distance and its application in the identification of outliers in the simple circular regression model
by: Mahmood, Ehab A., et al.
Published: (2017)

Binary response modeling and validation of its predictive ability
by: Midi, Habshah, et al.
Published: (2010)

Detecting outliers and influential observations in survival model / Nor Akmal Bt. Md Noh
by: Md Noh, Nor Akmal
Published: (2010)

A robust modification of the Goldfeld-Quandt Test for the detection of heteroscedasticity in the presence of outliers
by: Rana, Md. Sohel, et al.
Published: (2008)

The effect of high leverage points on the maximum estimated likelihood for separation in logistic regression
by: Ariffin @ Mat Zin, Syaiba Balqish, et al.
Published: (2014)

Adjusting outliers in univariate circular data
by: Mahmood, Ehab A., et al.
Published: (2017)

Statistical significance of rank regression
by: Rana, Sohel, et al.
Published: (2013)

A comparative study of logistic regression approach and stratified classical analysis on desire for children in Bangladesh
by: Sarkar, Saroje Kumar, et al.
Published: (2009)

Detection of outliers in high-dimensional data using nu-support vector regression
by: Mohammed Rashid, Abdullah, et al.
Published: (2021)

Detection of outliers in high dimensional data using NU-support vector regression
by: Mohammed Rashid, Abdullah, et al.
Published: (2021)

Empirical distributions of parameter estimates in binary logistic regression using bootstrap
by: Fitrianto, Anwar, et al.
Published: (2014)

The performance of clustering approach with robust mm-estimator for multiple outlier detection in linear regression
by: Mohd. Azmi, Nurulhuda Firdaus, et al.
Published: (2006)

Robust estimation of regression parameters with heteroscedastic errors in the presence of outliers (presented at the 8th WSEAS international conference on applied computer and computational science, Hangzhou China, 20-22 May 2009)
by: Midi, Habshah, et al.
Published: (2009)

The performance of robust weighted least squares in the presence of outliers and heteroscedastic errors
by: Midi, Habshah, et al.
Published: (2009)

Identification of multiple outliers in a generalized linear model with continuous variables
by: Loo, Yee Peng, et al.
Published: (2016)

Robust multivariate least angle regression
by: Uraibi, Hassan Sami, et al.
Published: (2017)

Robust bootstrap methods in logistic regression model
by: Ariffin, Syaiba Balqish, et al.
Published: (2012)

Robust support vector regression model in the presence of outliers and leverage points
by: Dhhan, Waleed, et al.
Published: (2017)