A projection pursuit framework for testing

Comment on "High dimensional

Uniform inference for highdimensional

Twosample testing in nonsparse

Linear hypothesis testing in dense

Highdimensional inference in linear models:

Robust confidence intervals in highdimensional

Boosting in the presence of outliers:

Robustness in sparse linear models:

Randomized Maximum Contrast Selection:

Structured Estimation in NonParametric Cox ModelIn this paper, we study theoretical properties of the nonparametric Cox proportional hazards model in a high dimensional nonasymptotic setting. We establish the finite sample oracle l2 bounds for a general class of group penalties that allow possible hierarchical and overlapping structures. We approximate the log partial likelihood with a quadratic functional and use truncation arguments to reduce the error. Unlike the existing literature, we exemplify differences between bounded and possibly unbounded nonparametric covariate effects. In particular, we show that bounded effects can lead to prediction bounds similar to the simple linear models, whereas unbounded effects can lead to larger prediction bounds. In both situations we do not assume that the true parameter is necessarily sparse. Lastly, we present new theoretical results for hierarchical and smoothed estimation in the nonparametric Cox model. We provide two examples of the proposed general framework: a Cox model with interactions and an ANOVA type Cox model.
with Rui Song, Electronic Journal of Statistics (2015), 9(1), p.492534 
Cultivating Disaster Donors Using Data AnalyticsNonprofit organizations use directmail marketing to cultivate onetime donors and convert them into recurring contributors. Cultivated donors generate much more revenue than new donors, but also lapse with time, making it important to steadily draw in new cultivations. We propose a new empirical model based on importance subsample aggregation of a large number of penalized logistic regressions. We show via simulation that a simple design strategy based on these insights has potential to improve success rates from 5.4% to 8.1%
with Ilya Ryzhov and Bin Han, Management Science (2016), 62 (3), p. 849866 
Regularization for Cox's proportional

Composite QuasiLikelihood
