Outlier is a data point that deviates too much from the rest of dataset. Most of real-world dataset have outlier. Outlier analysis is one of the techniques in data mining whose task is to discover the data which have an exceptional behavior compare to remaining dataset. Outlier detection plays an important role in data mining field. Outlier Detection is useful in many fields like Medical, Netwo...

We introduce one-sided versions of Huber’s contamination model, in which corrupted samples tend to take larger values than uncorrupted ones. Two intertwined problems are addressed: estimation the mean (minimum effect) and selection (outliers). Regarding minimum effect, we derive minimax risks estimators that adaptive with respect unknown number contaminations. The optimal convergence rates diff...

This study is about outlier detection in time series data. The main objective is to derive and to test statistics for detecting additive outlier (AO) and innovative outlier (IO) in GARCH(1,1) processes and subsequently to develop a procedure for testing the presence of outliers using the statistics. A test statistic has been derived for each type of outlier. In the derivation of the statistics,...

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 ...

The purpose of this article is to evaluate the application of forecasting models along with the use of residual control charts to assess production processes whose samples have autocorrelation characteristics. The main objective is to determine the efficiency of control charts for individual observations (CCIO) and exponentially weighted moving average (EWMA) charts when they are applied to res...