Outlier Identification in Telecom Data

2018 ◽  
pp. 97-106
Author(s):  
Ye Ouyang ◽  
Mantian Hu ◽  
Alexis Huet ◽  
Zhongyuan Li
Keyword(s):  
Outlier Identification

scholarly journals Univariate and Multivariate Outlier Identification for Skewed or Heavy-Tailed Distributions

2018 ◽  
Vol 18 (3) ◽  
pp. 517-532
Author(s):  
Vincenzo Verardi ◽  
Catherine Vermandele
Keyword(s):  
Computational Complexity ◽  
Multivariate Analyses ◽  
Outlier Identification ◽  
Multivariate Case ◽  
Univariate Case ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed ◽  
Identification Procedures

In univariate and in multivariate analyses, it is difficult to identify outliers in the case of skewed or heavy-tailed distributions. In this article, we propose simple univariate and multivariate outlier identification procedures that perform well with these types of distributions while keeping the computational complexity low. We describe the commands gboxplot (univariate case) and sdasym (multivariate case), which implement these procedures in Stata.

Outlier Identification in Colorectal Surgery Should Separate Elective and Nonelective Service Components

2014 ◽  
Vol 57 (9) ◽  
pp. 1098-1104 ◽  
Author(s):  
Ben E. Byrne ◽  
Ravikrishna Mamidanna ◽  
Charles A. Vincent ◽  
Omar D. Faiz
Keyword(s):  
Colorectal Surgery ◽  
Outlier Identification ◽  
Service Components

BAYESIAN IDENTIFICATION OF OUTLIERS AND CHANGE-POINTS IN MEASUREMENT ERROR MODELS

2005 ◽  
Vol 08 (04) ◽  
pp. 433-449 ◽  
Author(s):  
FERNANDO A. QUINTANA ◽  
PILAR L. IGLESIAS ◽  
HELENO BOLFARINE
Keyword(s):  
Linear Regression ◽  
Outlier Detection ◽  
Change Point ◽  
Regression Models ◽  
Point Problem ◽  
Linear Regression Models ◽  
Outlier Identification ◽  
Change Point Problem ◽  
Error In Variables ◽  
The Relationship

The problem of outlier and change-point identification has received considerable attention in traditional linear regression models from both, classical and Bayesian standpoints. In contrast, for the case of regression models with measurement errors, also known as error-in-variables models, the corresponding literature is scarce and largely focused on classical solutions for the normal case. The main object of this paper is to propose clustering algorithms for outlier detection and change-point identification in scale mixture of error-in-variables models. We propose an approach based on product partition models (PPMs) which allows one to study clustering for the models under consideration. This includes the change-point problem and outlier detection as special cases. The outlier identification problem is approached by adapting the algorithms developed by Quintana and Iglesias [32] for simple linear regression models. A special algorithm is developed for the change-point problem which can be applied in a more general setup. The methods are illustrated with two applications: (i) outlier identification in a problem involving the relationship between two methods for measuring serum kanamycin in blood samples from babies, and (ii) change-point identification in the relationship between the monthly dollar volume of sales on the Boston Stock Exchange and the combined monthly dollar volumes for the New York and American Stock Exchanges.

scholarly journals Outlier identification in radiation therapy knowledge-based planning: A study of pelvic cases

2017 ◽  
Vol 44 (11) ◽  
pp. 5617-5626 ◽  
Author(s):  
Yang Sheng ◽  
Yaorong Ge ◽  
Lulin Yuan ◽  
Taoran Li ◽  
Fang-Fang Yin ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Outlier Identification ◽  
Knowledge Based ◽  
Knowledge Based Planning
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