Functional data are occurring more and more often in practice, and various statistical
techniques have been developed to analyze them. In this paper we consider multivariate …

A new multivariate concept of quantile, based on a directional version of Koenker and
Bassett's traditional regression quantiles, is introduced for multivariate location and multiple …

The objective of this research is detection of outliers in multivariate data employing various
distance measure, particularly using robust regression diagnosis technique. Several …

Identifying abnormal data to improve data quality is of great importance for machinery health
monitoring (MHM). Existing abnormal data detection methods generally depend on …

The prevention of flood risks and the effective planning and management of water resources
require river flows to be continuously measured and analyzed at a number of stations. For a …

Summary RTK/RAS/RAF pathway alterations (RPAs) are a hallmark of lung
adenocarcinoma (LUAD). In this study, we use whole-genome sequencing (WGS) of 85 …

Boxplots are among the most widely used exploratory data analysis (EDA) tools in statistical
practice. Typical applications of boxplots include eliciting information about the underlying …

The growing neural gas (GNG) self-organizing neural network stands as one of the most
successful examples of unsupervised learning of a graph of processing units. Despite its …

Multivariate frequency analyses are needed to study floods due to dependence existing
among representative variables of the flood hydrograph. Particularly, multivariate analyses …

Conversational data in social media contain a great deal of useful information, and
conversation anomaly detection is an important research direction in the field of sentiment …