Review on Outliers Identification Methods for Univariate Circular Biological Data

A Review and Comparison of Methods for Detecting Outliers in Univariate Data Sets

Appendix a Methods for Identifying Data Outliers

Only extremely large rates are flagged, not extremely small ones, because only large values will have a major influence on statistics involving pounds of pesticide use. What value to use for the maximum rate in each criterion is somewhat arbitrary; the value determines how conservative one wants to be. We chose maximum rates to be close to what were considered obvious outliers by a group of sci...

Longitudinal Data: Simple Univariate Methods of Analysis

These notes descibe various simple models and methods for analysis of longitudinal data and show how the analyses can be carried out using SAS. The descriptions require some knowledge of analysis of variance, also with random eeects as in split-plot experiments, in particular. Knowledge of multivariate analysis is not required, and the plain multivariate analysis of variance , treating each ser...

Informal identification of outliers in medical data

Informal box plot identification of outliers in realworld medical data was studied. Box plots were used to detect univariate outliers directly whereas the box plotted Mahalanobis distances identified multivariate outliers. Vertigo and female urinary incontinence data were used in the tests. The removal of outliers increased the descriptive classification accuracy of discriminant analysis functi...

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...