Detection of outliers and influential observations in linear mixed measurement error models with Liu estimation | ||
| Journal of Statistical Modelling: Theory and Applications | ||
| دوره 4، شماره 2، مهر 2023، صفحه 1-16 اصل مقاله (255.17 K) | ||
| نوع مقاله: Original Scientific Paper | ||
| شناسه دیجیتال (DOI): 10.22034/jsmta.2024.20632.1115 | ||
| نویسندگان | ||
| Shadi Borhani1؛ Fatemeh Ghapani* 2؛ Razieh Jafaraghaee2 | ||
| 1Department of Statistics, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran | ||
| 2Department of Mathematics and Statistics, Shoushtar Branch, Islamic Azad University, Shoushtar, Iran | ||
| چکیده | ||
| In this paper, the case deletion approach and mean shift outlier model are developed to identify influential and outlier observations using the Liu corrected likelihood estimator in linear mixed measurement error models when multicollinearity is present. We derive a corrected score test statistic for outlier detection based on mean shift outlier models. Furthermore, according to the Liu corrected likelihood estimator, several Cook’s distance is constructed for influence diagnostics. A parametric bootstrap procedure is used to obtain empirical distribution of the test statistic and a simulation study is conducted to demonstrate the performance of the diagnostic criteria. Finally, a real example is provided to illustrate the performance of the test statistics. | ||
| کلیدواژهها | ||
| Case deletion model؛ Cook's distance؛ Mean shift outlier model؛ Outlier detection؛ Score test | ||
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آمار تعداد مشاهده مقاله: 231 تعداد دریافت فایل اصل مقاله: 135 |
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