High-dimensional generalized linear models
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 … Web7 de ago. de 2013 · This paper studies generalized additive partial linear models with high-dimensional covariates. We are interested in which components (including parametric and nonparametric components) are nonzero. The additive nonparametric functions are approximated by polynomial splines.
High-dimensional generalized linear models
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WebGeneralized linear model; High-dimensional inference; Matrix uncertainty selector; Measurement error; Sparse estimation; Acknowledgments. The authors would like to thank Prof. Bin Yu for discussions and Dr. Sjur Reppe for providing the bone density data. Reprints and Corporate Permissions. WebA passionate and self-motivated data scientist with +5 years of experience in analytics domain, including wrangling, analyzing and modeling large …
Web10 de abr. de 2024 · In both cases, models that are based on pairwise covariances can be used on their own or as an element in a larger model, such as a spatial generalized linear model. In this work, we are mainly concerned with using spatial information to improve the accuracy of predictions, rather than reducing bias in parameter estimates ( LeSage, 2008 ). Web1 de mar. de 2024 · Abstract. Generalized linear models (GLMs) are used in high-dimensional machine learning, statistics, communications, and signal processing. In this …
Web19 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 … WebRobust high-dimensional generalized linear models 33 functional T(F) is sufficiently regular, a von Mises expansion (von Mises, 1947) yields T(G) ...
Webboth linear and logistic high-dimensional regression models. 2.1 Estimation in high-dimensional regression For the high-dimensional linear model (1), a commonly used …
WebIn this work, we study the transfer learning problem under high-dimensional generalized linear models (GLMs), which aim to improve the fit on target data by borrowing information from useful source data. Given which sources to transfer, we propose a transfer learning algorithm on GLM, ... dunchurch councillorWeb25 de dez. de 2024 · Robust and consistent variable selection in high-dimensional generalized linear models - 24 Hours access EUR €36.00 GBP £32.00 USD $39.00 … dunchurch crossroadsWebThis article proposes a bootstrap-assisted procedure to conduct simultaneous inference for high-dimensional sparse linear models based on the recent desparsifying Lasso estimator. Our procedure allows the dimension of the parameter vector of interest to be exponentially larger than sample size, and it automatically accounts for the dependence … dunchurch cottages for saleWebThis study proposes a novel complete subset averaging (CSA) method for high-dimensional generalized linear models based on a penalized Kullback–Leibler (KL) loss. All models under consideration can be potentially misspecified, and the dimension of covariates is allowed to diverge to infinity. dunchurch festival groupWeb1 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 … dunchurch gp surgeryWebThis study proposes a novel complete subset averaging (CSA) method for high-dimensional generalized linear models based on a penalized Kullback–Leibler (KL) … dunchurch flooringWebIn this paper, a graphic model-based doubly sparse regularized estimator is discussed under the high dimensional generalized linear models, that utilizes the graph … dunchurch library