Index of Sensitivity to Nonignorability (ISNI) google
Standard methods of analysis can give misleading results when some observations are nonignorably missing. Analysts currently assess nonignorability by performing sensitivity analyses using models with and without a nonignorable component. Because this approach can involve complicated modeling and arduous computation, and can yield results that are highly sensitive to untestable model assumptions, there is a need for a simple screening tool that measures the potential impact of nonignorability on an analysis. We propose a measure based on a Taylor-series approximation to the nonignorable likelihood, evaluated at the parameter estimates under the assumption of ignorability. From this approximate likelihood, we derive an index of sensitivity to nonignorability, or ISNI. One can compute ISNI without estimating a nonignorable model or positing specific values of a nonignorability parameter. We interpret ISNI in terms of an intuitive parameter that captures the extent of sensitivity. We derive a general expression for ISNI in the generalized linear model with fully observed predictors and potentially missing outcomes. …

Dirichlet Lasso (DLASSO) google
Selection of the most important predictor variables in regression analysis is one of the key problems statistical research has been concerned with for long time. In this article, we propose the methodology, Dirichlet Lasso (abbreviated as DLASSO) to address this issue in a Bayesian framework. In many modern regression settings, large set of predictor variables are grouped and the coefficients belonging to any one of these groups are either all redundant or all important in predicting the response; we say in those cases that the predictors exhibit a group structure. We show that DLASSO is particularly useful where the group structure is not fully known. We exploit the clustering property of Dirichlet Process priors to infer the possibly missing group information. The Dirichlet Process has the advantage of simultaneously clustering the variable coefficients and selecting the best set of predictor variables. We compare the predictive performance of DLASSO to Group Lasso and ordinary Lasso with real data and simulation studies. Our results demonstrate that the predictive performance of DLASSO is almost as good as that of Group Lasso when group label information is given; and superior to the ordinary Lasso for missing group information. For high dimensional data (e.g., genetic data) with missing group information, DLASSO will be a powerful approach of variable selection since it provides a superior predictive performance and higher statistical accuracy.

Zelig google
A framework that brings together an abundance of common statistical models found across packages into a unified interface, and provides a common architecture for estimation and interpretation, as well as bridging functions to absorb increasingly more models into the collective library. Zelig allows each individual package, for each statistical model, to be accessed by a common uniformly structured call and set of arguments. Moreover, Zelig automates all the surrounding building blocks of a statistical work-flow–procedures and algorithms that may be essential to one user’s application but which the original package developer did not use in their own research and might not themselves support. These include bootstrapping, jackknifing, and re-weighting of data. In particular, Zelig automatically generates predicted and simulated quantities of interest (such as relative risk ratios, average treatment effects, first differences and predicted and expected values) to interpret and visualize complex models. …