The cellwise minimum covariance determinant estimator
Abstract The usual Minimum Covariance Determinant (MCD) estimator of a covariance
matrix is robust against casewise outliers. These are cases (that is, rows of the data matrix) …
matrix is robust against casewise outliers. These are cases (that is, rows of the data matrix) …
Robust covariance estimation with missing values and cell-wise contamination
Large datasets are often affected by cell-wise outliers in the form of missing or erroneous
data. However, discarding any samples containing outliers may result in a dataset that is too …
data. However, discarding any samples containing outliers may result in a dataset that is too …
The relationship between educational level and the role of parents with learning achievement in mathematics
DC Mardiati, B Alorgbey, AB Zarogi - Interval: Indonesian Journal of …, 2024 - cahaya-ic.com
Purpose of the study: This research aims to determine the relationship between parental
education level and student learning achievement and between the role of parents and …
education level and student learning achievement and between the role of parents and …
[HTML][HTML] Challenges of cellwise outliers
It is well-known that real data often contain outliers. The term outlier usually refers to a case,
usually denoted by a row of the n× d data matrix. In recent times a different type has come …
usually denoted by a row of the n× d data matrix. In recent times a different type has come …
Multivariate Singular Spectrum Analysis by Robust Diagonalwise Low-Rank Approximation
Abstract Multivariate Singular Spectrum Analysis (MSSA) is a powerful and widely used
nonparametric method for multivariate time series, which allows the analysis of complex …
nonparametric method for multivariate time series, which allows the analysis of complex …
[PDF][PDF] Identification of rainfall patterns on hydrological simulation using robust principal component analysis
SM Shaharudin, N Ahmad, NH Zainuddin… - Indonesian Journal of …, 2018 - eprints.utm.my
A robust dimension reduction method in Principal Component Analysis (PCA) was used to
rectify the issue of unbalanced clusters in rainfall patterns due to the skewed nature of …
rectify the issue of unbalanced clusters in rainfall patterns due to the skewed nature of …
Factors determining intention to continue using E-HRM
The development of information technology has promoted organizational transformation
through the utilization of an electronic information system. This research aimed to identify …
through the utilization of an electronic information system. This research aimed to identify …
An introduction to new robust linear and monotonic correlation coefficients
Background The most common measure of association between two continuous variables is
the Pearson correlation (Maronna et al. in Safari an OMC. Robust statistics, 2019 …
the Pearson correlation (Maronna et al. in Safari an OMC. Robust statistics, 2019 …
Real-time outlier detection for large datasets by RT-DetMCD
Modern industrial machines can generate gigabytes of data in seconds, frequently pushing
the boundaries of available computing power. Together with the time criticality of industrial …
the boundaries of available computing power. Together with the time criticality of industrial …
[HTML][HTML] Portfolio optimization using cellwise robust association measures and clustering methods with application to highly volatile markets
This paper introduces the minCluster portfolio, which is a portfolio optimization method
combining the optimization of downside risk measures, hierarchical clustering and cellwise …
combining the optimization of downside risk measures, hierarchical clustering and cellwise …