We adapt a semi-Bayesian hierarchical modeling framework to jointly characterize the space–time variability of seasonal precipitation totals and precipitation extremes across the Northern Great Plains ...

Single nucleotide polymorphism (SNP) interaction plays a critical role for complex diseases. The primary limitation of logistic regressions (LR) in testing SNP–SNP interactions is that coefficient ...

In the setting of nonparametric multivariate regression with unknown error variance σ², we study asymptotic properties of a Bayesian method for estimating a ...

This article considers methodology for hierarchical functional data analysis, motivated by studies of reproductive hormone profiles in the menstrual cycle. Current methods standardize the cycle ...

Multivariate analysis in statistics is a set of useful methods for analyzing data when there are more than one variables under consideration. Multivariate analysis techniques may be used for several ...

Understanding one of the most important types of data analysis. by Amy Gallo You probably know by now that whenever possible you should be making data-driven decisions at work. But do you know how to ...

From a banking supervisory perspective, this paper analyzes aspects of market risk of a supervisory trading portfolio comprised of the trading books of eleven German banks with a regulatory approved ...

Learn how to build a multivariate linear regression model step by step—no libraries, just pure C++ logic! Mary Trump issues warning on long-term impact of Donald Trump move I Built a 1500HP Big Block ...