A multivariable analysis is the most popular approach when investigating associations
between risk factors and disease. However, efficiency of multivariable analysis highly …

This classic textbook builds theoretical statistics from the first principles of probability theory.
Starting from the basics of probability, the authors develop the theory of statistical inference …

We use the information in collateralized debt obligations (CDO) prices to study market
expectations about how corporate defaults cluster. A three‐factor portfolio credit model …

A family of multivariate binary distributions for simulating correlated binary variables with
specified marginal means and corre Page 1 Biometrika (2003), 90, 2, pp. 455–463 © 2003 …

Spike trains recorded from populations of neurons can exhibit substantial pairwise
correlations between neurons and rich temporal structure. Thus, for the realistic simulation …

Nonparametric smoothing methods are used to model longitudinal data, but the challenge
remains to incorporate correlation into nonparametric estimation procedures. In this article …

It is important to be able to generate correlated binary data in an efficient, easily
programmed manner for, among other things, the generation of large bootstrap samples. In …

A Monte Carlo algorithm is said to be adaptive if it automatically calibrates its current
proposal distribution using past simulations. The choice of the parametric family that defines …

High-dimensional correlated binary data arise in many areas, such as observed genetic
variations in biomedical research. Data simulation can help researchers evaluate efficiency …

Dependent effect size estimates are a common problem in meta‐analysis. Recently, a robust
variance estimation method was introduced that can be used whenever effect sizes in a …