Results 61 to 70 of about 1,681,604 (218)

Vine copula knockoffs for variable selection in gene expression studies

Canadian Journal of Statistics, EarlyView.
Abstract Identifying clinical and genetic markers is essential for stratifying cancer patients by survival outcomes and guiding personalized treatment strategies. However, gene expression studies often involve high‐dimensional predictors with mixed data types and complex dependence, which complicates reliable variable selection.
José Ulises Márquez Urbina, Alejandro Román Vásquez, Graciela González Farías, Gabriel Escarela   +3 more
wiley   +1 more source

Lasso for hierarchical polynomial models

Canadian Journal of Statistics, EarlyView.
Abstract The divisibility conditions implicit in a polynomial hierarchy suggest parameter constraints in regression. With this idea, we establish strong and weak hierarchies for both the lasso and relaxed lasso. Our proposal extends prior work on hierarchical lasso, which was mainly concerned with models of degree 2.
H. Maruri‐Aguilar, S. Lunagómez
wiley   +1 more source

Comparison of Nine Statistical Model Based Warfarin Pharmacogenetic Dosing Algorithms Using the Racially Diverse International Warfarin Pharmacogenetic Consortium Cohort Database. [PDF]

PLoS ONE, 2015
Multiple linear regression (MLR) and machine learning techniques in pharmacogenetic algorithm-based warfarin dosing have been reported. However, performances of these algorithms in racially diverse group have never been objectively evaluated and compared.
Rong Liu, Xi Li, Wei Zhang, Hong-Hao Zhou   +3 more
doaj   +1 more source

The life and work of Ernest Sydney Keeping

Canadian Journal of Statistics, EarlyView.
Abstract This article reviews the life and contributions of Ernest S. Keeping (1895–1984), a British‐born physicist who became a mathematics professor at the University of Alberta in 1929. He played a pivotal role in shaping the university's Mathematics Department, serving as its head from 1954 to 1961, and continuing to teach until 1970, nine years ...
David R. Bellhouse, Christian Genest
wiley   +1 more source

Sparsity via new Bayesian Lasso [PDF]

, 2020
Lasso estimate as the posterior mode assuming that the parameter   has prior density as double exponential distribution [1]. In this paper, we proposed Scale Mixture of Normals mixing with Rayleigh (SMNR) density on their variances to represent
Flaih, Ahmad Naeem, Alhusseini, Fadel Hamid Hadi, Alshaybawee, Taha   +2 more
core   +1 more source

Variational Bayesian Variable Selection in Logistic Regression Based on Spike-and-Slab Lasso

Mathematics
Logistic regression is often used to solve classification problems. This article combines the advantages of Bayesian methods and spike-and-slab Lasso to select variables in high-dimensional logistic regression.
Juanjuan Zhang, Weixian Wang, Mingming Yang, Maozai Tian   +3 more
doaj   +1 more source

Model selection for causal inference in missing exposure problems

Canadian Journal of Statistics, EarlyView.
Abstract In causal inference, properly selecting the propensity score (PS) model is an important topic and has been widely investigated in observational studies. However, there are very few studies investigating the model selection issue for causal inference when the exposure is missing at random (MAR). When the exposure variable contains missing data,
Yuliang Shi, Yeying Zhu, Joel A. Dubin
wiley   +1 more source

Robust, Adaptive Functional Regression in Functional Mixed Model Framework [PDF]

, 2011
Functional data are increasingly encountered in scientific studies, and their high dimensionality and complexity lead to many analytical challenges.
Morris, Jeffrey S., Hongxiao Zhu, Zhu, Hongxiao, Philip J. Brown, Jeffrey S. Morris, Brown, Philip J.   +5 more
core   +1 more source

A Method for Determining Medium- and Long-Term Renewable Energy Accommodation Capacity Considering Multiple Uncertain Influencing Factors

Energies
Amid the global energy transition, rapidly expanding wind and solar installations challenge power grids with variability and uncertainty. We propose an adaptive framework for renewable energy accommodation assessment under high-dimensional uncertainties,
Tingxiang Liu, Libin Yang, Zhengxi Li, Kai Wang, Pinkun He, Feng Xiao   +5 more
doaj   +1 more source

bayesQR: A Bayesian Approach to Quantile Regression

Journal of Statistical Software, 2017
After its introduction by Koenker and Basset (1978), quantile regression has become an important and popular tool to investigate the conditional response distribution in regression. The R package bayesQR contains a number of routines to estimate quantile
Dries F. Benoit, Dirk Van den Poel
doaj   +1 more source