Outliers are observations that do not follow the distribution of data patterns and can cause
deviations from data analysis, so a method for identifying outliers is needed One method in …

Outliers are observations where the point of observation deviates from the data pattern. The
existence of outliers in the data can cause irregularities in the results of data analysis. One …

Principal component regression (PCR) is commonly used in statistical downscaling (SD)
models to forecast local-scale rainfall data based on global-scale rainfall. The global …

High-dimensional data can occur in actual cases where the variable p is larger than the
number of observations n. The problem that often occurs when adding data dimensions …

An outlier is an observation whose pattern does not follow the majority of the data. Outliers in
this study were characterized by extreme distance values, both very small and very large …

Abstract Background and Objective: The performance of classical Jennrich (J) statistic using
classical estimators suffers from masking effects. To relieve the problem, robust estimators …

Linear discriminant analysis (LDA) is one of the supervised classification techniques to deal
with relationship between a categorical variable and a set of continuous variables. The main …

The multivariate exponential weighted moving average (MEWMA) control chart, which is
based on classical estimators, is suitable for monitoring process data with non-random …

Mapping of rice field is done with a conventional two step process: training process and
classification. The results of mapping process are highly influenced by accuracy of spectral …