Detection of Outliers in Constrained Regression

Introduction Package CircOutlier For Detection of Outliers in Circular-Circular Regression

One of the most important problem in any statistical analysis is the existence of unexpected observations. Some observations are not a part of the study and are known as outliers. Studies have shown that the outliers affect to the performance of statistical standard methods in models and predictions. The point of this work is to provide a couple of statistical package in R software to identi...

Some Methods of Detection of Outliers in Linear Regression Model

An outlier is an observation that deviates markedly from the majority of the data. To know which observation has greater influence on parameter estimate, detection of outlier is very important. There are several methods for detection of outliers available in the literature. A good number of test-statistics for detecting outliers have been developed. In contrast to detection, outliers are also t...

Detection of Outliers in the Complex Linear Regression Model

The existence of outliers in any type of data affects the estimation of models’ parameters. To date there are very few literatures on outlier detection tests in circular regression and it motivated us to propose simple techniques to detect any outliers. This paper considered the complex linear regression model to fit circular data. The complex residuals of complex linear regression model were e...

OWA operators in linear regression and detection of outliers

Outliers robustness in multivariate orthogonal regression

This paper deals with the problem of multivariate affine regression in the presence of outliers in the data. The method discussed is based on weighted orthogonal least squares. The weights associated with the data satisfy a suitable optimality criterion and are computed by a two-step algorithm requiring a RANSAC step and a gradient-based optimization step. Issues related to the breakdown point ...