Missing value imputation (MVI) has been studied for several decades being the basic
solution method for incomplete dataset problems, specifically those where some data …

Recently, numerous studies have been conducted on Missing Value Imputation (MVI),
intending the primary solution scheme for the datasets containing one or more missing …

Modeling and Analysis of Compositional Data presents a practical and comprehensive
introduction to the analysis of compositional data along with numerous examples to illustrate …

Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or
mean imputation, only work under highly restrictive conditions, which are often not met in …

Missing data affect nearly every discipline by complicating the statistical analysis of collected
data. But since the 1990s, there have been important developments in the statistical …

This paper studies an example-driven k-parameter computation that identifies different k
values for different test samples in kNN prediction applications, such as classification …

Compositional count data are discrete vectors representing the numbers of outcomes falling
into any of several mutually exclusive categories. Compositional techniques based on the …

GIS-based mineral prospectivity mapping (MPM) is a computer-aided methodology for
delineating and better constraining target areas deemed prospective for mineral deposits of …

Modern applications in chemometrics and bioinformatics result in compositional data sets
with a high proportion of zeros. An example are microbiome data, where zeros refer to …

The programming language and software environment R (R Development Core Team 2010)
is nowadays one of the most widely used and most popular software tools for statistics and …