This study describes a method for incorporating external estimates of intraclass correlation to improve the precision for the analysis of an existing group-randomized trial. The authors use a random-effects meta-analytic approach to pool the information across studies, which takes into account...

This article builds on the previous article by Blitstein et al. (2005), which showed how external estimates of intraclass correlation can be used to improve the precision for the analysis of an existing group randomized trial. The authors extend that work to sample size estimation and power...

Background:This article revisits an article published in Evaluation Review in 2005 on sample size estimation and power analysis for group-randomized trials. With help from a careful reader, we learned of an important error in the spreadsheet used to perform the calculations and generate the...

In sparse regression modeling via regularization such as the lasso, it is important to select appropriate values of tuning parameters including regularization parameters. The choice of tuning parameters can be viewed as a model selection and evaluation problem. Mallows’ Cp type criteria may be...

We propose improvements to existing degrees of freedom used for significance testing of multivariate hypotheses in small samples when missing data are handled using multiple imputation. The improvements are for 1) tests based on unrestricted fractions of missing information and 2) tests based on...

The residuals obtained from fitting a structural equation model are crucial ingredients in obtaining chi-square goodness-of-fit statistics for the model. The authors present a didactic discussion of the residuals, obtaining a geometrical interpretation by recognizing the residuals as the result...

The Wishart distribution has long been a useful tool for modeling covariance structures. According to Gyndikin’s theorem, the degrees of freedom (df) for a Wishart distribution can be any real number belonging to the Gyndikin set, either integer-valued or fractional. However, the fractional-df...

The lasso and its variants have attracted much attention recently because of its ability of simultaneous estimation and variable selection. When some prior knowledge exists in applications, the performance of estimation and variable selection can be further improved by incorporating the prior...

This paper considers Bayesian regression with normal and doubleexponential priors as forecasting methods based on large panels of time series. We show that, empirically, these forecasts are highly correlated with principal component forecasts and that they perform equally well for a wide range...