Previous surveys of the literature have shown that reports of statistical analyses often lack
important information, causing lack of transparency and failure of reproducibility. Editors and …

Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

Model-Based Clustering, Classification, and Denisty Estimation Using mclust in R Model-
based clustering and classification methods provide a systematic statistical approach to …

For the vast majority of stars in the second Gaia data release, reliable distances cannot be
obtained by inverting the parallax. A correct inference procedure must instead be used to …

Novelty detection is the task of classifying test data that differ in some respect from the data
that are available during training. This may be seen as “one-class classification”, in which a …

Kernel smoothing has greatly evolved since its inception to become an essential
methodology in the data science tool kit for the 21st century. Its widespread adoption is due …

The grammar of graphics is ubiquitous, providing the foundation for a variety of popular
visualization tools and toolkits. Yet support for uncertainty visualization in the grammar …

An essential textbook for any student or researcher in biology needing to design
experiments, sample programs or analyse the resulting data. The text begins with a revision …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

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