The Akaike information criterion (AIC) is one of the most ubiquitous tools in statistical
modeling. The first model selection criterion to gain widespread acceptance, AIC was …

Time series forecasting (TSF) is the task of predicting future values of a given sequence
using historical data. Recently, this task has attracted the attention of researchers in the area …

Abstract Model building and data analysis in the biological sciences somewhat presupposes
that the person has some advanced education in the quantitative sciences, and statistics in …

The fourth edition follows the general layout of the third edition but includes some
modernization of topics as well as the coverage of additional topics. The preface to the third …

Abstract In Chapter 3, Section 3.6 we introduced the basic theory for estimating the
nonrandom, fixed parameter matrix B qxp for the multivariate (linear) regression (MR) model …

The abstract concept of “information” can be quantified and this has led to many important
advances in the analysis of data in the empirical sciences. This text focuses on a science …

The Akaike (1973, 1974) information criterion, AIC, and the corrected Akaike information
criterion (Hurvich and Tsai, 1989), AICc, were both designed as estimators of the expected …

This chapter introduces the general linear model, illustrating how it subsumes a variety of
standard applied models. It also introduces random vectors and matrices and the …

We propose a procedure for constructing a sparse estimator of a multivariate regression
coefficient matrix that accounts for correlation of the response variables. This method, which …

Modeling and Inverse Problems in the Presence of Uncertainty collects recent research—
including the authors' own substantial projects—on uncertainty propagation and …