Outlier detection has been a topic in statistics for centuries. Over mainly the last two
decades, there has been also an increasing interest in the database and data mining …

Abstract 'Background'is the concentration of metals in pristine sediment, unaltered by human
activity and 'enrichment'is the extent present-day sediment metal concentrations exceed pre …

Single-cell RNA sequencing (scRNA-seq) has broad applications across biomedical
research. One of the key challenges is to ensure that only single, live cells are included in …

Assessing the assumption of multivariate normality is required by many parametric
multivariate statistical methods, such as MANOVA, linear discriminant analysis, principal …

Importance–performance analysis (IPA) is extensively used in hospitality and tourism
research because of its simplicity. However, due to the lack of critical statistical analysis, the …

Few books on statistical data analysis in the natural sciences are written at a level that a non-
statistician will easily understand. This is a book written in colloquial language, avoiding …

To be fully operational for facilitating decisions made at any spatial level, models and
indicators of soil ecosystem functions require the use of precise spatially referenced soil …

The minimum covariance determinant (MCD) method is a highly robust estimator of
multivariate location and scatter, for which a fast algorithm is available. Since estimating the …

This article considers critically how one of the oldest and most widely applied statistical
methods, principal components analysis (PCA), is employed with spatial data. We first …

A computationally fast procedure for identifying outliers is presented that is particularly
effective in high dimensions. This algorithm utilizes simple properties of principal …