High-dimensional generalized linear models

Author: wzfe

August undefined, 2024

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 … 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.

Transfer Learning under High-dimensional Generalized Linear …

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 … WebAbstract. In this paper, we propose a sparse generalized linear model incorporating graphical structure among predictors (sGLMg), which is an extension of [37] where they exploit the structure information among predictors to improve the performance for the linear regression. There is an explicit expression between the coefficient and the ... lithium d cell

Tony Cai

WebVector autoregressive models characterize a variety of time series in which linear combinations of current and past observations can be used to accurately predict ... 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 … Web1 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 ∗. lithium dc to dc battery charger

High-dimensional generalized linear models incorporating …

High-dimensional generalized linear models and the lasso

Web16 de mar. de 2024 · This generalized form is an expansion and the resulting discriminant function is not linear in x, but it is linear in y. The d’-functions yi(x) merely map points in … Web7 de set. de 2024 · Statistical inferences for high-dimensional regression models have been extensively studied for their wide applications ranging from genomics, … impulse ice cream marketWebHá 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 … impulse hub wheel bicycle

"WebHigh-dimensional Linear Discriminant Analysis: Optimality, Adaptive Algorithm, and Missing Data 1 T. Tony Cai and Linjun Zhang University of Pennsylvania Abstract This … " - High-dimensional generalized linear models

High-dimensional generalized linear models

Bayesian generalized linear low rank regression models for the

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

Did you know?

WebRobust high-dimensional generalized linear models 33 functional T(F) is sufﬁciently regular, a von Mises expansion (von Mises, 1947) yields T(G) ... WebWe consider high-dimensional generalized linear models with Lipschitz loss functions, and prove a nonasymptotic oracle inequality for the empirical risk minimizer with Lasso …

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 … http://www-stat.wharton.upenn.edu/~tcai/paper/html/Inference-GLM.html

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 … 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 …

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 ...

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, ... lithium d cell batts rechargeableWeb19 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 … lithium days 2022 halleWeb10 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, ... impulse imageryWeb7 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. impulse hyperphysicsWebTony 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 ... lithium ddiWeb10 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 ). lithium deep cycle battery adelaideWeb4 de dez. de 2024 · Vector autoregressive models characterize a variety of time series in which linear combinations of current and past observations can be used to accurately … lithium d cell 1.5v