stat.ME

Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching

Probabilistic graphical models provide a flexible yet parsimonious framework for modeling dependencies among nodes in networks. There is a vast literature on parameter estimation and consistent model selection for graphical models. However, in many …

Post-selection inference on high-dimensional varying-coefficient quantile regression model

Quantile regression has been successfully used to study heterogeneous and heavy-tailed data. In this work, we study high-dimensional varying-coefficient quantile regression model that allows us to capture non-stationary effects of the input variables …

Constrained High Dimensional Statistical Inference

In typical high dimensional statistical inference problems, confidence intervals and hypothesis tests are performed for a low dimensional subset of model parameters under the assumption that the parameters of interest are unconstrained. However, in …

Tensor Canonical Correlation Analysis

In many applications, such as classification of images or videos, it is of interest to develop a framework for tensor data instead of ad-hoc way of transforming data to vectors due to the computational and under-sampling issues. In this paper, we …

Two-sample inference for high-dimensional Markov networks