Identifying outliers and high-leverage points is a fundamental step in the least-squares regression model building process. Various influence measures based on different motivational arguments, and designed to measure the influence of observations on different aspects of various regression results, are elucidated and critiqued here. On the basis of a statistical analysis of the residuals (class...

Identifying the most influential individuals can provide invaluable help in developing and deploying effective viral marketing strategies. Previous studies mainly focus on designing efficient algorithms or heuristics to find top-K influential nodes on a given static social network. While, as a matter of fact, real-world social networks keep evolving over time and a recalculation upon the change...

There is strong evidence indicating that the existing measures which are designed to detect a single high leverage collinearity-reducing observation are not effective in the presence of multiple high leverage collinearity-reducing observations. In this paper, we propose a cutoff point for a newly developed high leverage collinearity-influential measure δ D i and two existing measures δi and li ...

In Multiple linear regression models, problems arise when serious multicollinearity or influential outliers are present in the data. Failure to include significant quadratic or cross-product terms result in model specification error. Simple scatter plots are most of the time not effective in revealing the complex relationships of predictor variables or data problems in multiple linear regressio...

The prevalence of online social media such as Facebook, Twitter, LinkedIn and YouTube has attracted considerable research in social influence analysis with applications in viral marketing, online advertising, recommender systems, information diffusion, and experts finding. Social influence occurs when one?s emotions, opinions, or behaviors are affected by others. Most of the works on social inf...

An extension of some standard likelihood based procedures to heteroscedastic nonlinear regression models under scale mixtures of skew-normal (SMSN) distributions is developed. We derive a simple EM-type algorithm for iteratively computing maximum likelihood (ML) estimates and the observed information matrix is derived analytically. Simulation studies demonstrate the robustness of this flexible ...

A Matlab based software for logistic regression is developed to enhance the process of teaching quantitative topics and assist researchers with analyzing wide area of applications where categorical data is involved. The software offers an option of performing stepwise logistic regression to select the most significant predictors. The software includes a feature to detect influential observation...