This paper concerns itself with the methods of identifying outliers in an otherwise normally distributed data set. Several significant tests and criteria designed for this purpose are described here, Peirce's criterion, Chauvenet's criterion, Grubbs' test, Dixon's test and Cochran's test. Deriving of the tests and criteria is indicated and finally the results of the use of the test and criteria on simulated data with normal distribution and inserted outlier are looked into. Codes in programming language R with the implementation of these test and criteria using existing functions are included. Powered by TCPDF (www.tcpdf.org
Classical methods for detecting outliers deal with continuous variables. These methods are not readi...
An outlier is an observation that appears to deviate markedly from other observations in the sample ...
228 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1982.Over the last several decades...
This paper deals with finding outliers (exceptions) in large datasets. The identification of outlier...
Researchers often lack knowledge about how to deal with outliers when analyzing their data. Even mor...
The problem of the detection of outliers is meaningful only within the context of a given statistica...
Determining if a dataset has one or more outliers is a fundamental and challenging problem in statis...
In this paper we propose a method for correctly detecting outliers based on a new technique develope...
Outliers are common place in applied time series analysis and various types of structural changes oc...
This article provides distributional results for testing multiple outliers in regression. Because di...
Outlier analysis is that the user do depends on the kinds data they have. An outlier is a data value...
This article presents a simple and efficient method to detect multiple outliers using a modification...
As said in signal processing, "One person's noise is another person's signal." F...
Determining if a dataset has one or more outliers is a fundamental and challenging problem in statis...
This article provides distributional results for testing multiple outliers in regression. Because d...
Classical methods for detecting outliers deal with continuous variables. These methods are not readi...
An outlier is an observation that appears to deviate markedly from other observations in the sample ...
228 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1982.Over the last several decades...
This paper deals with finding outliers (exceptions) in large datasets. The identification of outlier...
Researchers often lack knowledge about how to deal with outliers when analyzing their data. Even mor...
The problem of the detection of outliers is meaningful only within the context of a given statistica...
Determining if a dataset has one or more outliers is a fundamental and challenging problem in statis...
In this paper we propose a method for correctly detecting outliers based on a new technique develope...
Outliers are common place in applied time series analysis and various types of structural changes oc...
This article provides distributional results for testing multiple outliers in regression. Because di...
Outlier analysis is that the user do depends on the kinds data they have. An outlier is a data value...
This article presents a simple and efficient method to detect multiple outliers using a modification...
As said in signal processing, "One person's noise is another person's signal." F...
Determining if a dataset has one or more outliers is a fundamental and challenging problem in statis...
This article provides distributional results for testing multiple outliers in regression. Because d...
Classical methods for detecting outliers deal with continuous variables. These methods are not readi...
An outlier is an observation that appears to deviate markedly from other observations in the sample ...
228 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1982.Over the last several decades...