High-dimensional generalized linear models
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 … Web1 de jan. de 2024 · Probabilist, statistician, machine learner and financial econometrician. I have been working at both financial industry as a …
High-dimensional generalized linear models
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Web4 de abr. de 2008 · We consider high-dimensional generalized linear models with Lipschitz loss functions, and prove a nonasymptotic oracle inequality for the empirical … Web25 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 …
WebGeneralized linear model; High-dimensional inference; Matrix uncertainty selector; Measurement error; Sparse estimation; Acknowledgments. The authors would like to … 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 …
Webon high dimensional linear regression models, and it remains unknown whether their results can be extended to a more general setting. This paper will focus on … Web1 de mar. de 2024 · Abstract. Generalized linear models (GLMs) are used in high-dimensional machine learning, statistics, communications, and signal processing. In this …
Web12 de fev. de 2024 · High-dimensional Generalized Linear Model (GLM) inferences have been studied by many scholars [3,4,5,6]. Deshpande proposed a debiasing method for constructing CIs. Cai, Athey and Zhu [8,9,10] proposed a more general linear comparison method under the condition of special load vectors.
on the beach submarineWebVector autoregressive models characterize a variety of time series in which linear combinations of current and past observations can be used to accurately predict ... ion lp2cd stylusWebboth linear and logistic high-dimensional regression models. 2.1 Estimation in high-dimensional regression For the high-dimensional linear model (1), a commonly used … ionl spring conferenceWeb10 de nov. de 2024 · Title Transfer Learning under Regularized Generalized Linear Models Version 2.0.0 Description We provide an efficient implementation for two-step multi-source transfer learning algo-rithms in high-dimensional generalized linear models (GLMs). The elastic-net penal-ized GLM with three popular families, including linear, ... on the beach sunsetWeb1 de jul. de 2024 · T-ridge estimator for generalized linear models. In this section, we exemplify the t-ridge estimator for maximum regularized likelihood estimation in generalized linear models. We consider data Z = ( y, X) that follow a conditional distribution (5) y i x i, β ∗ ∼ F with g ( E ( y i x i, β ∗)) = x i ⊤ β ∗. ion lsWebTony Cai, Zijian Guo, and Rong Ma. Abstract: This paper develops a unified statistical inference framework for high-dimensional binary generalized linear models (GLMs) with general link functions. Both unknown and known design distribution settings are considered. A two-step weighted bias-correction method is proposed for constructing ... on the beach spongebobWebHigh-dimensional Linear Discriminant Analysis: Optimality, Adaptive Algorithm, and Missing Data 1 T. Tony Cai and Linjun Zhang University of Pennsylvania Abstract This … on the beach sisters of mercy