High-dimensional generalized linear models
WebVector autoregressive models characterize a variety of time series in which linear combinations of current and past observations can be used to accurately predict ... Web20 de fev. de 2014 · We consider testing regression coefficients in high dimensional generalized linear models. An investigation of the test of Goeman et al. (2011) is …
High-dimensional generalized linear models
Did you know?
Web1 de out. de 2024 · In this paper, we propose to use a penalized estimator for the homogeneity detection in the high-dimensional generalized linear model (GLM), that composed of two non-convex penalties: individual sparsity and sparsity of pairwise difference. We consider a class of non-convex penalties that includes most of existing … Web1 de set. de 2015 · Variable selection for high-dimensional generalized linear models with the weighted elastic-net procedure September 2015 Journal of Applied Statistics 43(5):1-14
Web1 de jul. de 2024 · Many current intrinsically interpretable machine learning models can only handle the data that are linear, low-dimensional, and relatively independent attributes and often with discrete attribute values, while the models that are capable of handling high-dimensional nonlinear data, like deep learning, have very poor interpretability. WebWe consider the lasso penalty for high-dimensional gener-alized linear models. Let Y ∈Y ⊂R be a real-valued (response) variable and X be a co-variable with values in some …
WebIn this paper, a graphic model-based doubly sparse regularized estimator is discussed under the high dimensional generalized linear models, that utilizes the graph structure among the predictors. The graphic information among predictors is incorporated node-by-node using a decomposed representation and the sparsity is encouraged both within and ... WebHigh-dimensional Linear Discriminant Analysis: Optimality, Adaptive Algorithm, and Missing Data 1 T. Tony Cai and Linjun Zhang University of Pennsylvania Abstract This …
WebWe propose a generalized linear low-rank mixed model (GLLRM) for the analysis of both high-dimensional and sparse responses and covariates where the responses may be …
WebTony Cai's Papers. Estimation and Inference for High-dimensional Generalized Linear Models with Knowledge Transfer. Sai Li, Linjun Zhang, Tony Cai, and Hongzhe Li. Abstract: Transfer learning provides a powerful tool for incorporating related data into a target study of interest. In epidemiology and medical studies, the classification of a ... high efficiency motor regulationWebA passionate and self-motivated data scientist with +5 years of experience in analytics domain, including wrangling, analyzing and modeling large … high efficiency oil burnerWebThis study proposes a novel complete subset averaging (CSA) method for high-dimensional generalized linear models based on a penalized Kullback–Leibler (KL) … high efficiency low water washershttp://www.personal.psu.edu/ril4/research/AOS1761PublishedVersion.pdf how fast do zz plants growhttp://www-stat.wharton.upenn.edu/~tcai/paper/Transfer-Learning-GLM.pdf how fast do you run rbcsWeb15 de mai. de 2024 · Janková et al. (2024) developed the Pearson residual-based methods for goodness-of-fit testing in high-dimensional generalized linear models. They mainly focused on sparsity settings and gave a ... how fast do you writeWeb4 de abr. de 2008 · We consider high-dimensional generalized linear models with Lipschitz loss functions, and prove a nonasymptotic oracle inequality for the empirical … high efficiency oil fired water heater