Call for Papers Special Issue on Statistics and AISLADSStatistical Learning and Data Science01Aims and ScopeStatistical Learning and Data Science (SLADS) is a newly launched journal sponsored by the C ...

Examination of the conflicting statistical methods currently used in scientific inference reveals an increasing awareness of the utility of likelihood. The concept of prior likelihood is introduced as ...

High-dimensional -omics data such as genomic, transcriptomic, and metabolomic data offer great promise in advancing precision medicine. In particular, such data have enabled the investigation of ...

It's said that statistics don't lie, but they often don't tell the whole truth, either. A Cornell statistics expert has come up with a method he believes can boost statistical power and significantly ...

Our laboratory has developed a range of data analysis workflows that incorporate advanced statistical and computational methods to interpret the complex molecular datasets generated by MS technologies ...

A new statistical method provides a more efficient way to uncover biologically meaningful changes in genomic data that span multiple conditions -- such as cell types or tissues. A new statistical ...

Papers in this series provide tutorials on statistical methods that are used in precision oncology, such as methods in discovery phase, diagnostics, and drug development. The goal of the series is to ...

Various statistical forecasting methods exist designed for use with slow-moving products, new product introductions, stable mature products and products with erratic ...

Finding a convincing explanation of a complex issue is no easy task. The decision about what qualifies as the "best" solution is inevitably subject to biases and approximations. Coupling statistics ...

Researchers from the European Central Bank, European Stability Mechanism, and Universität Bonn propose a new forecasting method called parametric tilting that helps economists incorporate new ...