Study on Outlier Detection Method in Survival Analysis: Weibull Regression Outlier Model

Outlier Detection in Survival Analysis

Outlier detection is an important task in many data-mining applications. In this paper, we present two parametric outlier detection methods for survival data. Both methods propose to perform outlier detection in a multivariate setting, using the Cox regression as the model and the concordance c-index as a measure of goodness of fit. The first method is a single-step procedure that presents a de...

Outlier Detection by Boosting Regression Trees

A procedure for detecting outliers in regression problems is proposed. It is based on information provided by boosting regression trees. The key idea is to select the most frequently resampled observation along the boosting iterations and reiterate after removing it. The selection criterion is based on Tchebychev’s inequality applied to the maximum over the boosting iterations of ...

Selection of Best Outlier Detection Method Using Regression Analysis

Outliers are unusual data values that are inconsistent with most of the records. Such non-representative records can seriously affect the model to be produced, so detecting outlier is a significant job to achieve higher accuracy. Several outlier detection methods are used in literature for real as well as simulated data sets. The aim of this study is to compare the two outlier detection method ...

Outlier Analysis Outlier Analysis

Multiple Linear Regression Models in Outlier Detection

Identifying anomalous values in the realworld database is important both for improving the quality of original data and for reducing the impact of anomalous values in the process of knowledge discovery in databases. Such anomalous values give useful information to the data analyst in discovering useful patterns. Through isolation, these data may be separated and analyzed. The analysis of outlie...