scholarly journals A Novel Variable Selection Method Based on a Partial KL Information Measure and Its Application to Channel Selection for Bioelectric Signal Classification

2009 ◽  
Vol 45 (12) ◽  
pp. 724-730 ◽  
Author(s):  
Taro SHIBANOKI ◽  
Keisuke SHIMA ◽  
Toshio TSUJI ◽  
Takeshi TAKAKI ◽  
Akira OTSUKA ◽  
...  
Keyword(s):  
Variable Selection ◽  
Channel Selection ◽  
Selection Method ◽  
Information Measure ◽  
Signal Classification ◽  
Variable Selection Method ◽  
Bioelectric Signal ◽  
Selection For

Variable selection for random effects two-part models

2018 ◽  
Vol 28 (9) ◽  
pp. 2697-2709 ◽  
Author(s):  
Dongxiao Han ◽  
Lei Liu ◽  
Xiaogang Su ◽  
Bankole Johnson ◽  
Liuquan Sun
Keyword(s):  
Variable Selection ◽  
Random Effects ◽  
Information Criterion ◽  
Selection Method ◽  
Sparse Estimation ◽  
Variable Selection Method ◽  
Zero Values ◽  
Selection For ◽  
And Storage ◽  
Proc Nlmixed

Random effects two-part models have been applied to longitudinal studies for zero-inflated (or semi-continuous) data, characterized by a large portion of zero values and continuous non-zero (positive) values. Examples include monthly medical costs, daily alcohol drinks, relative abundance of microbiome, etc. With the advance of information technology for data collection and storage, the number of variables available to researchers can be rather large in such studies. To avoid curse of dimensionality and facilitate decision making, it is critically important to select covariates that are truly related to the outcome. However, owing to its intricate nature, there is not yet a satisfactory variable selection method available for such sophisticated models. In this paper, we seek a feasible way of conducting variable selection for random effects two-part models on the basis of the recently proposed “minimum information criterion” (MIC) method. We demonstrate that the MIC formulation leads to a reasonable formulation of sparse estimation, which can be conveniently solved with SAS Proc NLMIXED. The performance of our approach is evaluated through simulation, and an application to a longitudinal alcohol dependence study is provided.

Predictive and Descriptive CoMFA Models: The Effect of Variable Selection

2018 ◽  
Vol 21 (2) ◽  
pp. 117-124 ◽  
Author(s):  
Bakhtyar Sepehri ◽  
Nematollah Omidikia ◽  
Mohsen Kompany-Zareh ◽  
Raouf Ghavami
Keyword(s):  
Variable Selection ◽  
Predictive Power ◽  
Selection Method ◽  
Data Sets ◽  
Data Set ◽  
Comfa Model ◽  
Variable Selection Method

Aims & Scope: In this research, 8 variable selection approaches were used to investigate the effect of variable selection on the predictive power and stability of CoMFA models. Materials & Methods: Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors and 57 ATAD2 bromodomain inhibitors were modelled by CoMFA. First of all, for all three data sets, CoMFA models with all CoMFA descriptors were created then by applying each variable selection method a new CoMFA model was developed so for each data set, 9 CoMFA models were built. Obtained results show noisy and uninformative variables affect CoMFA results. Based on created models, applying 5 variable selection approaches including FFD, SRD-FFD, IVE-PLS, SRD-UVEPLS and SPA-jackknife increases the predictive power and stability of CoMFA models significantly. Result & Conclusion: Among them, SPA-jackknife removes most of the variables while FFD retains most of them. FFD and IVE-PLS are time consuming process while SRD-FFD and SRD-UVE-PLS run need to few seconds. Also applying FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS protect CoMFA countor maps information for both fields.

scholarly journals Variable Selection in the Regularized Simultaneous Component Analysis Method for Multi-Source Data Integration

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Zhengguo Gu ◽  
Niek C. de Schipper ◽  
Katrijn Van Deun
Keyword(s):  
Variable Selection ◽  
Data Integration ◽  
Component Analysis ◽  
Selection Method ◽  
Gps Data ◽  
Positioning Systems ◽  
Variable Selection Method ◽  
Diary Data ◽  
Simultaneous Component Analysis ◽  
Travel Diary

AbstractInterdisciplinary research often involves analyzing data obtained from different data sources with respect to the same subjects, objects, or experimental units. For example, global positioning systems (GPS) data have been coupled with travel diary data, resulting in a better understanding of traveling behavior. The GPS data and the travel diary data are very different in nature, and, to analyze the two types of data jointly, one often uses data integration techniques, such as the regularized simultaneous component analysis (regularized SCA) method. Regularized SCA is an extension of the (sparse) principle component analysis model to the cases where at least two data blocks are jointly analyzed, which - in order to reveal the joint and unique sources of variation - heavily relies on proper selection of the set of variables (i.e., component loadings) in the components. Regularized SCA requires a proper variable selection method to either identify the optimal values for tuning parameters or stably select variables. By means of two simulation studies with various noise and sparseness levels in simulated data, we compare six variable selection methods, which are cross-validation (CV) with the “one-standard-error” rule, repeated double CV (rdCV), BIC, Bolasso with CV, stability selection, and index of sparseness (IS) - a lesser known (compared to the first five methods) but computationally efficient method. Results show that IS is the best-performing variable selection method.

A fast variable selection method for quantitative analysis of soils using laser-induced breakdown spectroscopy

2017 ◽  
Vol 32 (6) ◽  
pp. 1166-1176 ◽  
Author(s):  
Xiao Fu ◽  
Fa-Jie Duan ◽  
Ting-Ting Huang ◽  
Ling Ma ◽  
Jia-Jia Jiang ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Variable Selection ◽  
Selection Method ◽  
Laser Induced Breakdown Spectroscopy ◽  
Fast Variable ◽  
Breakdown Spectroscopy ◽  
Variable Selection Method ◽  
Laser Induced Breakdown

A fast variable selection method combining iPLS and mIPW-PLS is proposed to reduce the dimensions of the spectrum for LIBS quantitative analysis.

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