Sparse Reduced Rank Huber Regression in High Dimensions.

We propose a sparse reduced rank Huber regression for analyzing large and complex high-dimensional data with heavy-tailed random noise. The proposed method is based on a convex relaxation of a rank- and sparsity-constrained nonconvex optimization problem, which is then solved using a block coordinate descent and an alternating direction method of multipliers algorithm. We establish nonasymptotic estimation error bounds under both Frobenius and nuclear norms in the high-dimensional setting.

Smoothed Quantile Regression with Large-Scale Inference.

Quantile regression is a powerful tool for learning the relationship between a response variable and a multivariate predictor while exploring heterogeneous effects. This paper focuses on statistical inference for quantile regression in the "increasing dimension" regime. We provide a comprehensive analysis of a convolution smoothed approach that achieves adequate approximation to computation and inference for quantile regression.

Estimating and inferring the maximum degree of stimulus-locked time-varying brain connectivity networks.

Neuroscientists have enjoyed much success in understanding brain functions by constructing brain connectivity networks using data collected under highly controlled experimental settings. However, these experimental settings bear little resemblance to our real-life experience in day-to-day interactions with the surroundings. To address this issue, neuroscientists have been measuring brain activity under natural viewing experiments in which the subjects are given continuous stimuli, such as watching a movie or listening to a story.

Transformation of speech sequences in human sensorimotor circuits.

After we listen to a series of words, we can silently replay them in our mind. Does this mental replay involve a reactivation of our original perceptual dynamics? We recorded electrocorticographic (ECoG) activity across the lateral cerebral cortex as people heard and then mentally rehearsed spoken sentences. For each region, we tested whether silent rehearsal of sentences involved reactivation of sentence-specific representations established during perception or transformation to a distinct representation.

Propagation of Information Along the Cortical Hierarchy as a Function of Attention While Reading and Listening to Stories.

How does attention route information from sensory to high-order areas as a function of task, within the relatively fixed topology of the brain? In this study, participants were simultaneously presented with 2 unrelated stories-one spoken and one written-and asked to attend one while ignoring the other. We used fMRI and a novel intersubject correlation analysis to track the spread of information along the processing hierarchy as a function of task. Processing the unattended spoken (written) information was confined to auditory (visual) cortices.

Replicates in high dimensions, with applications to latent variable graphical models.

In classical statistics, much thought has been put into experimental design and data collection. In the high-dimensional setting, however, experimental design has been less of a focus. In this paper, we stress the importance of collecting multiple replicates for each subject in this setting.

The fitness consequences of aneuploidy are driven by condition-dependent gene effects.

Aneuploidy is a hallmark of tumor cells, and yet the precise relationship between aneuploidy and a cell's proliferative ability, or cellular fitness, has remained elusive. In this study, we have combined a detailed analysis of aneuploid clones isolated from laboratory-evolved populations of Saccharomyces cerevisiae with a systematic, genome-wide screen for the fitness effects of telomeric amplifications to address the relationship between aneuploidy and cellular fitness. We found that aneuploid clones rise to high population frequencies in nutrient-limited evolution experiments and show increased fitness relative to wild type.

The cluster graphical lasso for improved estimation of Gaussian graphical models.

The task of estimating a Gaussian graphical model in the high-dimensional setting is considered. The graphical lasso, which involves maximizing the Gaussian log likelihood subject to a penalty, is a well-studied approach for this task. A surprising connection between the graphical lasso and hierarchical clustering is introduced: the graphical lasso in effect performs a two-step procedure, in which (1) single linkage hierarchical clustering is performed on the variables in order to identify connected components, and then (2) a penalized log likelihood is maximized on the subset of variables within each connected component.

Learning Graphical Models With Hubs.

We consider the problem of learning a high-dimensional graphical model in which there are a few nodes that are to many other nodes. Many authors have studied the use of an penalty in order to learn a sparse graph in the high-dimensional setting. However, the penalty implicitly assumes that each edge is equally likely and independent of all other edges.

Statistical properties of convex clustering.

In this manuscript, we study the statistical properties of . We establish that convex clustering is closely related to single linkage hierarchical clustering and -means clustering. In addition, we derive the range of the tuning parameter for convex clustering that yields a non-trivial solution.

Sparse Biclustering of Transposable Data.

We consider the task of simultaneously clustering the rows and columns of a large transposable data matrix. We assume that the matrix elements are normally distributed with a bicluster-specific mean term and a common variance, and perform biclustering by maximizing the corresponding log likelihood. We apply an ℓ penalty to the means of the biclusters in order to obtain sparse and interpretable biclusters.

Metronomic administration of chlorambucil for treatment of dogs with urinary bladder transitional cell carcinoma.

Objective: To determine the antitumor effects and toxicoses of metronomic oral administration of a low dose of chlorambucil in dogs with transitional cell carcinoma (TCC).

Design: Prospective clinical trial.

Animals: 31 client-owned dogs with TCC for which prior treatments had failed or owners had declined other treatments.