Broken adaptive ridge method for variable selection in generalized partly linear models with application to the coronary artery disease data [0.03%]
广义部分线性模型中的变量选择的退化自适应岭方法及其在冠状动脉病数据中的应用
Christian Chan,Xiaotian Dai,Thierry Chekouo et al.
Christian Chan et al.
Motivated by the CATHGEN data, we develop a new statistical method for simultaneous variable selection and parameter estimation in the context of generalized partly linear models for data with high-dimensional covariates. The method is refe...
Jixin Chen
Jixin Chen
Optimization of parameters and hyperparameters is a general process for any data analysis. Because not all models are mathematically well-behaved, stochastic optimization can be useful in many analyses by randomly choosing parameters in eac...
Approximate Bayesian computational methods to estimate the strength of divergent selection in population genomics models [0.03%]
利用群体基因组模型估计分歧选择强度的近似贝叶斯计算方法
Martyna Lukaszewicz,Ousseini Issaka Salia,Paul A Hohenlohe et al.
Martyna Lukaszewicz et al.
Statistical estimation of parameters in large models of evolutionary processes is often too computationally inefficient to pursue using exact model likelihoods, even with single-nucleotide polymorphism (SNP) data, which offers a way to redu...