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pro vyhledávání: '"Haris, Asad"'
Autor:
Janssen, Joseph, Meng, Shizhe, Haris, Asad, Schrunner, Stefan, Cao, Jiguo, Welch, William J., Kunz, Nadja, Ameli, Ali A.
Scientists and statisticians often want to learn about the complex relationships that connect two time-varying variables. Recent work on sparse functional historical linear models confirms that they are promising for this purpose, but several notable
Externí odkaz:
http://arxiv.org/abs/2303.06501
Autor:
Haris, Asad, Platt, Robert
We consider the problem of selecting confounders for adjustment from a potentially large set of covariates, when estimating a causal effect. Recently, the high-dimensional Propensity Score (hdPS) method was developed for this task; hdPS ranks potenti
Externí odkaz:
http://arxiv.org/abs/2112.08495
Biomedical researchers usually study the effects of certain exposures on disease risks among a well-defined population. To achieve this goal, the gold standard is to design a trial with an appropriate sample from that population. Due to the high cost
Externí odkaz:
http://arxiv.org/abs/1911.06380
We present a unified framework for estimation and analysis of generalized additive models in high dimensions. The framework defines a large class of penalized regression estimators, encompassing many existing methods. An efficient computational algor
Externí odkaz:
http://arxiv.org/abs/1903.04641
Publikováno v:
Advances in Neural Information Processing Systems 2018, 8987-8997
We present a novel approach for nonparametric regression using wavelet basis functions. Our proposal, $\texttt{waveMesh}$, can be applied to non-equispaced data with sample size not necessarily a power of 2. We develop an efficient proximal gradient
Externí odkaz:
http://arxiv.org/abs/1903.04631
Publikováno v:
In Epilepsy Research August 2022 184
Publikováno v:
Biometrika 2018, Vol. 106, No. 1, 87-107
We consider the problem of non-parametric regression with a potentially large number of covariates. We propose a convex, penalized estimation framework that is particularly well-suited for high-dimensional sparse additive models. The proposed approac
Externí odkaz:
http://arxiv.org/abs/1611.09972
Publikováno v:
Journal of Computational and Graphical Statistics 2016, Vol. 25, No. 4, 981-1004
We consider the task of fitting a regression model involving interactions among a potentially large set of covariates, in which we wish to enforce strong heredity. We propose FAMILY, a very general framework for this task. Our proposal is a generaliz
Externí odkaz:
http://arxiv.org/abs/1410.3517
Publikováno v:
Journal of Computational and Graphical Statistics, 2016 Dec 01. 25(4), 981-1004.
Externí odkaz:
https://www.jstor.org/stable/44861905
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