Robust inference in time series analysis is concerned with developing statistical methods that remain valid under departures from standard model assumptions, such as the presence of heteroskedasticity ...

Whenever inference for variance components is required, the choice between one-sided and two-sided tests is crucial. This choice is usually driven by whether or not negative variance components are ...

Interpreting 'omics data often involves statistical analysis of large numbers of loci such as genes, binding sites or single-nucleotide polymorphisms (SNPs). Although the data set as a whole may be ...