Principal stratification is a popular framework for addressing post-randomization complications, often in conjunction with finite mixture models for estimating the causal effects of interest. Unfortunately, standard estimators of mixture parameters, like the MLE, are known to exhibit pathological behavior. We study this behavior in a simple but fundamental example, a two-component Gaussian mixture model in which only the component means and variances are unknown, and focus on the setting in which the components are weakly separated. In this case, we show that the asymptotic convergence rate of the MLE is quite poor, such as $O(n^{-1/6})$ or even $O(n^{-1/8})$. We then demonstrate via both theoretical arguments and extensive simulations that the MLE behaves like a threshold estimator in finite samples, in the sense that the MLE can give strong evidence that the means are equal when the truth is otherwise. We also explore the behavior of the MLE when the MLE is non-zero, showing that it is difficult to estimate both the sign and magnitude of the means in this case. We provide diagnostics for all of these pathologies and apply these ideas to re-analyzing two randomized evaluations of job training programs, JOBS II and Job Corps. Our results suggest that the corresponding maximum likelihood estimates should be interpreted with caution in these cases.

Users make lasting judgments about a website's appeal within a split second of seeing it for the first time. This first impression is influential enough to later affect their opinions of a site's usability and trustworthiness. In this paper, we demonstrate a means to predict the initial impression of aesthetics based on perceptual models of a website's colorfulness and visual complexity. In an online study, we collected ratings of colorfulness, visual complexity, and visual appeal of a set of 450 websites from 548 volunteers. Based on these data, we developed computational models that accurately measure the perceived visual complexity and colorfulness of website screenshots. In combination with demographic variables such as a user's education level and age, these models explain approximately half of the variance in the ratings of aesthetic appeal given after viewing a website for 500ms only.

In November 2008, we audited contests in Santa Cruz and Marin counties, California. The audits were risk-limiting: they had a prespecified minimum chance of requiring a full hand count if the outcomes were wrong. We developed a new technique for these audits, the trinomial bound. Batches of ballots are selected for audit using probabilities proportional to the amount of error each batch can conceal. Votes in the sample batches are counted by hand. Totals for each batch are compared to the semiofficial results. The ldquotaintrdquo in each sample batch is computed by dividing the largest relative overstatement of any margin by the largest possible relative overstatement of any margin. The observed taints are binned into three groups: less than or equal to zero, between zero and a thresholdd, and larger thand. The number of batches in the three bins have a joint trinomial distribution. An upper confidence bound for the overstatement of the margin in the election as a whole is constructed by inverting tests for trinomial category probabilities and projecting the resulting set. If that confidence bound is sufficiently small, the hypothesis that the outcome is wrong is rejected, and the audit stops. If not, there is a full hand count. We conducted the audits with a risk limit of 25%, ensuring at least a 75% chance of a full manual count if the outcomes were wrong. The trinomial confidence bound confirmed the results without a full count, even though the Santa Cruz audit found some errors. The trinomial bound gave better results than the Stringer bound, which is commonly used to analyze financial audit samples drawn with probability proportional to error bounds.