WebThis paper is concerned with testing linear hypotheses in high dimensional generalized linear models. To deal with linear hypotheses, we first propose the constrained partial … WebHigh Dimensional Variable Selection: Non-Linear System Tree-based Approach: It makes use of the internals of the decision tree structure in variable selection. All observations begin in single root node and are split into two groups based on whether Xk c or Xk

Bayesian Methods for High-Dimensional Variable Selection

WebGeneralized linear models (GLM) provide an extension of linear models in dealing with different types of responses, including for example binary data and count data (McCullagh and Nelder (1989)). Let Y be a response variable and sup-pose the (conditional) mean of the response, , depends on the p-dimensional predictors X = (X1;:::;Xp) through dave 1961

Linear hypothesis testing for high dimensional generalized linear models

WebFeb 20, 2014 · A general method for constructing confidence intervals and statistical tests for single or low-dimensional components of a large parameter vector in a high-dimensional model and develops the corresponding theory which includes a careful analysis for Gaussian, sub-Gaussian and bounded correlated designs. 952 PDF Webdata have heavy tails. For robust estimation of high-dimensional heavy-tailed time series data, Qiu et al. (2015) developed a quantile-based Dantzig selector for the class of elliptical VAR processes. Han et al. (2024) proposed a robust estimation method for high-dimensional sparse generalized linear models with temporal dependent covariates. WebA point process generalized linear model (PP-GLM) framework for the estimation of discrete time multivariate nonlinear Hawkes processes is described. The approach is illustrated with the modeling of collective dynamics in neocortical neuronal ensembles recorded in human and non-human primates, and prediction of single-neuron spiking. dave 2008