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Consistency of spectral clustering in stochastic block models
It is shown that, under mild conditions, spectral clustering applied to the adjacency matrix of the network can consistently recover hidden communities even when the order of the maximum expected degree is as small as $\log n$ with $n$ the number of nodes.
Synaptic, transcriptional, and chromatin genes disrupted in autism
Using exome sequencing, it is shown that analysis of rare coding variation in 3,871 autism cases and 9,937 ancestry-matched or parental controls implicates 22 autosomal genes at a false discovery rate of < 0.05, plus a set of 107 genes strongly enriched for those likely to affect risk (FDR < 0.30).
Distribution-Free Predictive Inference for Regression
- Jing Lei, Max G'Sell, A. Rinaldo, R. Tibshirani, L. Wasserman
- Computer Science, MathematicsJournal of the American Statistical Association
- 14 April 2016
A general framework for distribution-free predictive inference in regression, using conformal inference, which allows for the construction of a prediction band for the response variable using any estimator of the regression function, and a model-free notion of variable importance, called leave-one-covariate-out or LOCO inference.
Differential privacy and robust statistics
We show by means of several examples that robust statistical estimators present an excellent starting point for differentially private estimators. Our algorithms use a new paradigm for differentially…
Fantope Projection and Selection: A near-optimal convex relaxation of sparse PCA
A novel convex relaxation of sparse principal subspace estimation based on the convex hull of rank-d projection matrices (the Fantope) is proposed and implies the near-optimality of DSPCA (d'Aspremont et al. ) even when the solution is not rank 1.
MINIMAX SPARSE PRINCIPAL SUBSPACE ESTIMATION IN HIGH DIMENSIONS
We study sparse principal components analysis in high dimensions, where p (the number of variables) can be much larger than n (the number of observations), and analyze the problem of estimating the…
Distribution‐free prediction bands for non‐parametric regression
A new prediction band is given by combining the idea of ‘conformal prediction’ with non‐parametric conditional density estimation and the proposed estimator, called COPS, always has a finite sample guarantee.
A goodness-of-fit test for stochastic block models
- Jing Lei
- 16 December 2014
The stochastic block model is a popular tool for studying community structures in network data. We develop a goodness-of-fit test for the stochastic block model. The test statistic is based on the…
The autism-associated chromatin modifier CHD8 regulates other autism risk genes during human neurodevelopment
This work identifies genes targeted by CHD8, a chromodomain helicase strongly associated with ASD, in human midfetal brain, human neural stem cells (hNSCs) and embryonic mouse cortex, and suggests loss of CHD 8 contributes to ASD by perturbing an ancient gene regulatory network during human brain development.
Minimax Rates of Estimation for Sparse PCA in High Dimensions
It is proved optimal, non-asymptotic lower and upper bounds on the minimax estimation error for the leading eigenvector when it belongs to an $\ell_q$ ball for $q \in [0,1]$ over a wide class of distributions.