High-dimensional generalized linear models

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August undefined, 2024

Web1 de mar. de 2024 · Abstract. Generalized linear models (GLMs) are used in high-dimensional machine learning, statistics, communications, and signal processing. In 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 …

Simultaneous Inference for High-Dimensional Linear Models

Web1 de jan. de 2024 · Probabilist, statistician, machine learner and financial econometrician. I have been working at both financial industry as a … 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, ... try jshell

Graphical-model based high dimensional generalized linear models

Web15 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 ... WebHá 1 dia · This paper proposes a new procedure to validate the multi-factor pricing theory by testing the presence of alpha in linear factor pricing models with a large number of … 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. try js code

High-dimensional generalized linear models

High-dimensional data and linear models: a review OAMS

http://www-stat.wharton.upenn.edu/~tcai/paper/Transfer-Learning-GLM.pdf Web3 de fev. de 2024 · Variable selection in a grouped manner is an attractive method since it respects the grouping structure in the data. In this paper, we study the adaptive group Lasso in the frame of high-dimensional generalized linear models. Both the number of groups diverging with the sample size and the number of groups exceeding the sample …

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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 … WebThe problem of obtaining an optimal spline with free knots is tantamount to minimizing derivatives of a nonlinear differentiable function over a Banach space on a compact …

WebWe consider high-dimensional generalized linear models with Lipschitz loss functions, and prove a nonasymptotic oracle inequality for the empirical risk minimizer with Lasso … WebHigh-dimensional data and linear models: a review M Brimacombe Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS, USA Abstract: The …

WebRobust high-dimensional generalized linear models 33 functional T(F) is sufﬁciently regular, a von Mises expansion (von Mises, 1947) yields T(G) ... WebA Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models. Part of Advances in Neural Information Processing Systems 35 (NeurIPS 2024) Main Conference Track. Bibtex Paper Supplemental.

http://www-stat.wharton.upenn.edu/~tcai/paper/html/Transfer-Learning-GLM.html

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 … try json parseWeb25 de abr. de 2024 · Model average receives much attention in recent years. This paper considers the semiparametric model averaging for high-dimensional longitudinal data. To minimize the prediction error, the authors estimate the model weights using a leave-subject-out cross-validation procedure. Asymptotic optimality of the proposed method is proved … phillimore bexhillWeb4 de abr. de 2008 · We consider high-dimensional generalized linear models with Lipschitz loss functions, and prove a nonasymptotic oracle inequality for the empirical … phillimore atlas index of parish registersWeb25 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 Rental. This article is also available for rental through DeepDyve. Advertisement. Citations. Views. 2,550. Altmetric. More metrics information. ×. Email alerts. Article activity alert. … try just now reviewsWebA Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models. Part of Advances in Neural Information Processing Systems 35 (NeurIPS 2024) … phillimore book publishingWebGeneralized 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. try just a little bit harder songWebTony 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 ... try justfit