A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
The paper introduces a dynamic MVP model using high-frequency financial data.
Estimates target GGM using auxiliary studies with false discovery rate control.
New method controls FDR for sparse GLMs, identifying positive and negative relationships.
A new R package for high-dimensional regression and precision matrix estimation.
Various -penalised estimation methods such as graphical lasso and CLIME are widely used for sparse precision matrix estimation. Many of these methods have been shown to be consistent under various quantitative assumptions about the underlying true covariance matrix. Intuitively, these conditions are related to …
Gaussian Graphical Models (GGMs) have wide-ranging applications in machine learning and the natural and social sciences. In most of the settings in which they are applied, the number of observed samples is much smaller than the dimension and they are assumed to be sparse. While there are a variety of algorithms (e.g. G…
High dimensional sparse learning has imposed a great computational challenge to large scale data analysis. In this paper, we are interested in a broad class of sparse learning approaches formulated as linear programs parametrized by a {\em regularization factor}, and solve them by the parametric simplex method (PSM). O…
A simple thresholding technique improves graph selection in neural connectivity studies.
The paper improves Bayesian precision matrix estimation for high-dimensional sparse data.