Instrumental Variables Estimation with Some Invalid Instruments and its Application to Mendelian Randomization

Instrumental variables have been widely used for estimating the causal effect between exposure and outcome. Conventional estimation methods require complete knowledge about all the instruments' validity; a valid instrument must not have a direct effect on the outcome and not be related to unmeasured confounders. Often, this is impractical as highlighted by Mendelian randomization studies where genetic markers are used as instruments and complete knowledge about instruments' validity is equivalent to complete knowledge about the involved genes' functions. In this paper, we propose a method for estimation of causal effects when this complete knowledge is absent. It is shown that causal effects are identified and can be estimated as long as less than $50$% of instruments are invalid, without knowing which of the instruments are invalid. We also introduce conditions for identification when the 50% threshold is violated. A fast penalized $\ell_1$ estimation method, called sisVIVE, is introduced for estimating the causal effect without knowing which instruments are valid, with theoretical guarantees on its performance. The proposed method is demonstrated on simulated data and a real Mendelian randomization study concerning the effect of body mass index on health-related quality of life index. An R package \emph{sisVIVE} is available online.Comment: 99 pages, 29 figures, 14 table

Factors Associated with the Success of an African American College President

This narrative study examines the experiences of Dr. George Wright, a successful African American college president. Dr. Wright had 14-year career at the helm of Prairie View A&M University, an Historically Black College or University (HBCU), where he changed the trajectory of the institution. His story begins with seminal experiences in the segregated schools of Lexington, Kentucky and continues to Duke University, where he became the first African American to receive a Ph.D. in History. In this study, Dr. Wright relates critical moments in his childhood, educational, and career experiences that put him on a path to success. This narrative is a message of hope, redemption, and possibility with important lessons about overcoming significant obstacles to reach the pinnacle of higher education leadership. Several lessons emerge from Dr. Wright’s narrative when considered alongside the theory and literature on leadership. These include a focus on mentoring, the importance of relationships, and recognizing and taking advantage of opportunities. Each aided in Dr. Wright’s success and were foundational pillars for his presidency. Findings and insights from this study are offered as recommendations for developing and sustaining a successful career as an African American college president

Bayesian testing of many hypotheses ×\times many genes: A study of sleep apnea

Substantial statistical research has recently been devoted to the analysis of large-scale microarray experiments which provide a measure of the simultaneous expression of thousands of genes in a particular condition. A typical goal is the comparison of gene expression between two conditions (e.g., diseased vs. nondiseased) to detect genes which show differential expression. Classical hypothesis testing procedures have been applied to this problem and more recent work has employed sophisticated models that allow for the sharing of information across genes. However, many recent gene expression studies have an experimental design with several conditions that requires an even more involved hypothesis testing approach. In this paper, we use a hierarchical Bayesian model to address the situation where there are many hypotheses that must be simultaneously tested for each gene. In addition to having many hypotheses within each gene, our analysis also addresses the more typical multiple comparison issue of testing many genes simultaneously. We illustrate our approach with an application to a study of genes involved in obstructive sleep apnea in humans.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS241 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Predicting Global Disposition of U.S. Military Personnel via Open-Source, Unclassified Means

The Joint Distribution Processing Analysis Center (JDPAC) of the United States Transportation Command (USTRANSCOM) regularly forecasts the demand of USTRANSCOM assets required by geographic and combatant commanders. These demands are subject to fluctuations due to unforeseen circumstances such as war, conflict, natural disasters, and other calamities requiring the presence of military personnel. This study evaluates the use of exponential state space smoothing, ARIMA, and Regression with ARIMA errors models to forecast the number of military personnel expected in each country, for a test set of countries of interest to USTRANSCOM and which manifest a high degree of variability in the anticipated number of troops each year. The expectation by USTRANSCOM is that accurate forecasts for the number of military personnel in each country can be leveraged to develop alternative transportation workload forecasts of demand of USTRANSCOM assets. There was not a single model that performed best for all countries and branches of service. Each model was analyzed via the traditional 80/20 forecasting evaluation metric as well as a two-year horizon cross-validation metric. The exponential smoothing model with a high level of α performed quite well for many of the models, indicating that perhaps simpler models will still provide accurate forecasts. Further research is needed to determine whether incorporating forecasts of military personnel will improve the ability to forecast demand of USTRANSCOM assets

Deterministic and Stochastic Prisoner's Dilemma Games: Experiments in Interdependent Security

This paper examines experiments on interdependent security prisoner's dilemma games with repeated play. By utilizing a Bayesian hierarchical model, we examine how subjects make investment decisions as a function of their previous experience and their treatment condition. Our main findings are that individuals have differing underlying propensities to invest that vary across time, are affected by both the stochastic nature of the game and even more so by an individual's ability to learn about his or her counterpart's choices. Implications for individual decisions and the likely play of a person's counterpart are discussed in detail.

Tourist experiences of individuals with vision impairments

People with visual disabilities Travel Australia. Tourism Research Australia