High-dimensional generalized linear models

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WebA passionate and self-motivated data scientist with +5 years of experience in analytics domain, including wrangling, analyzing and modeling large … 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.

Adaptive Testing for Alphas in High-dimensional Factor Pricing …

WebWe study robust high-dimensional estimation of generalized linear models (GLMs); where a small number k of the n observations can be arbitrarily corrupted, and where the … WebVector autoregressive models characterize a variety of time series in which linear combinations of current and past observations can be used to accurately predict ... dunk disrupt 2 pale ivory black

Covariate Selection in High-Dimensional Generalized Linear Models With ...

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

High-dimensional Inference for Generalized Linear Models with …

Bayesian generalized linear low rank regression models for the

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 … Web25 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. … dunked crosswordhttp://www-stat.wharton.upenn.edu/~tcai/paper/Transfer-Learning-GLM.pdf dunked twisted box

"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 … " - High-dimensional generalized linear models

High-dimensional generalized linear models

WebWe consider high-dimensional generalized linear models with Lipschitz loss functions, and prove a nonasymptotic oracle inequality for the empirical risk minimizer with Lasso … WebTony 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 ...

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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 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 …

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

Webthe high-dimensional GLM transfer learning setting. Extensive simulations and a real-data experiment verify the e ectiveness of our algorithms. Keywords: Generalized linear …

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 ∗. dunked on 1 hourWebIn this paper, a graphic model-based doubly sparse regularized estimator is discussed under the high dimensional generalized linear models, that utilizes the graph … dunked for charityWebThis 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 … dunked cookies utahWebHigh-dimensional data and linear models: a review M Brimacombe Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS, USA Abstract: The … dunke chocolatesWeb1 de mar. de 2024 · Abstract. Generalized linear models (GLMs) are used in high-dimensional machine learning, statistics, communications, and signal processing. In this … dunked twisted box with buddyWebThis 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. dunked on 2 lyricsWebA Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models. Part of Advances in Neural Information Processing Systems 35 (NeurIPS 2024) … dunk contest winner 2022