Statistical Diagnostics of Models for Count
Data

Zamir Ali; Yu Feng; Ali Choo; Munsir Ali

doi:https://doi.org/10.14445/22315373/IJMTT-V63P502

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 63 | Number 1 | Year 2018 | Article Id. IJMTT-V63P502 | DOI : https://doi.org/10.14445/22315373/IJMTT-V63P502

Statistical Diagnostics of Models for Count Data

Zamir Ali, Yu Feng, Ali Choo, Munsir Ali

Citation :

Zamir Ali, Yu Feng, Ali Choo, Munsir Ali, "Statistical Diagnostics of Models for Count Data," International Journal of Mathematics Trends and Technology (IJMTT), vol. 63, no. 1, pp. 9-15, 2018. Crossref, https://doi.org/10.14445/22315373/IJMTT-V63P502

Abstract

While doing statistical analysis, a problem often resists that there may exist some extremely small or large observation (outliers). To deal with such problem diagnostics of the observations is the best option for the model building process. Mostly analysts use ordinary least square (OLS) method which is utterly failing in the identification of outliers. In this paper, we use the diagnostics method to detect outliers and influential points in models for count data. Gauss-Newton and Likelihood Distance method approach has been treated to detect the outliers in parameter estimation in non-linear regression analysis. We used these techniques to analyze the performance of residual and influence in the non-linear regression model. The results show us detection of single and multiple outlier cases in count data.

Keywords

Poisson Regression, Outliers, Residuals, Non-linear regression model, Likelihood distance.

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