variable selection

Thursday, November 8, 2018 - 3:00pm - 3:30pm
Lei Liu (Washington University School of Medicine)
Mediation analysis has been commonly used to study the effect of an exposure on an outcome through a mediator. In practice, we may face the problem to estimate and test a specific mediator of interest, termed “targeted mediator, in the presence of high dimensional mediators. In this talk we present a de-biased Lasso estimate for the targeted mediator and derive its standard error estimator, which can be used to develop a test procedure for the targeted mediation effect. Extensive simulation studies are conducted to assess the performance of our method.
Wednesday, February 21, 2018 - 2:00pm - 2:40pm
Tucker McElroy (U.S. Bureau of the Census)
We develop statistical tools for time series analysis of high-dimensional multivariate datasets, when a few core series are of principal interest and there are many potential ancillary predictive variables. The
Friday, September 15, 2017 - 10:50am - 11:20am
Kosuke Imai (Princeton University)
Social scientists use conjoint analysis, which is based on randomized experiments with a factorial design, to analyze multidimensional preferences in a population. In such experiments, several factors, each with multiple levels, are randomized to form a large number of possible treatment conditions. To explore causal interaction in factorial experiments, we propose a new definition of causal interaction effect, called the average marginal interaction effect (AMIE).
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