Mixture models are useful for monitoring the behavior of data and for offering comparisons to supplemental data, especially in the presence of unobserved heterogeneity, but one should be highly cautious when drawing causal inferences as to which population each component of the fitted mixture model represents.
Although countries with high levels of economic development generally have more personal automobile travel than less-affluent nations, income is not the only factor that determines a nation's demand for cars.
Automobility -- travel in personal vehicles -- varies between countries. This brief summarizes a study of the factors besides economic development that affect automobility and how automobility might evolve in developing countries.
The level of automobility, or travel in personal vehicles, varies among countries. By determining the factors besides economic development that have affected automobility in developed countries, researchers can predict how automobility might evolve in developing countries.
The Toolkit for Weighting and Analysis of Nonequivalent Groups, or TWANG, contains a set of functions to support causal modeling of observational data through the estimation and evaluation of propensity score weights.
Given the rewards and penalties associated with characterizing top performance, the ability of statistical benchmarks to summarize key features of the provider performance distribution should be examined.
Although study characteristics, such as trial quality, may explain some proportion of heterogeneity across study results in meta-analyses, residual heterogeneity is a crucial factor in determining when associations between moderator variables and effect sizes can be statistically detected.
The American Statistical Association named Susan Paddock as a 2013 ASA Fellow. She was honored for excellence in statistical applications to public policy research, for integrating innovative statistical methodology with substantive problems of national healthcare policy, and for noteworthy service to the profession.
RAND is introducing a new method of forecasting the outcomes of U.S. Presidential elections. Rather than repeatedly poll new random samples, this poll uses panel of 3,500 people who are asked the same questions every week. The results illustrate the effects of election season variables.
There is a bias-variance tradeoff at work in propensity score estimation; every step toward better balance usually means an increase in variance and at some point a marginal decrease in bias may not be worth the associated increase in variance.
Examines the empirical evidence for associations between a set of proposed quality criteria and estimates of effect sizes in randomized controlled trials across a variety of clinical fields and to explore variables potentially influencing the association.
The authors developed a "generalized persistence" (GP) model, a Bayesian multivariate model for estimating teacher effects that accommodates longitudinal data that are not vertically scaled by allowing less than perfect correlation of a teacher's effects across test administrations.