Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become ...

Statistical inference comprises the framework by which data are used to draw conclusions about underlying phenomena or populations. At its heart lies hypothesis testing, a procedure that evaluates ...

The advent of big data has transformed the landscape of statistical science, demanding methods that can handle unprecedented volume, velocity and variety. Traditional inference techniques, designed ...

Causal inference is important in medical research to help determine if treatments are beneficial and if natural exposures are harmful. In many settings, data collection makes causal inference ...

Multivariate models more general than the standard multivariate linear model have received considerable attention in both the statistical and econometric literature; see Srivastava (1966, 1967, 1968) ...

Although it is the goal of most statistical investigation, causal inference has traditionally been ignored by statistical theory. Fortunately, there is now intense activity in a number of fields, ...

The second century Alexandrian astronomer and mathematician Claudius Ptolemy had a grand ambition. Hoping to make sense of the motion of stars and the paths of planets, he published a magisterial ...