Publications (185)
Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models
Han Liu, Kathryn Roeder, Larry Wasserman
A challenging problem in estimating high-dimensional graphical models is to choose the regularization parameter in a data-dependent way. The standard techniques include -fold cr…
Forest Guided Smoothing
Isabella Verdinelli, Larry Wasserman
We use the output of a random forest to define a family of local smoothers with spatially adaptive bandwidth matrices. The smoother inherits the flexibility of the original forest…
Multi-Tree Methods for Statistics on Very Large Datasets in Astronomy
Alexander G. Gray, Andrew W. Moore, Robert C. Nichol +3
Many fundamental statistical methods have become critical tools for scientific data analysis yet do not scale tractably to modern large datasets. This paper will describe very rece…
Minimax rates for heterogeneous causal effect estimation
Edward H. Kennedy, Sivaraman Balakrishnan, James M. Robins +1
Estimation of heterogeneous causal effects - i.e., how effects of policies and treatments vary across subjects - is a fundamental task in causal inference. Many methods for estimat…
Treelets--An adaptive multi-scale basis for sparse unordered data
Ann B. Lee, Boaz Nadler, Larry Wasserman
In many modern applications, including analysis of gene expression and text documents, the data are noisy, high-dimensional, and unordered--with no particular meaning to the given…
Improving power in genome-wide association studies: weights tip the scale
Kathryn Roeder, Bernie Devlin, Larry Wasserman
Genome-wide association analysis has generated much discussion about how to preserve power to detect signals despite the detrimental effect of multiple testing on power. We develop…