المؤلفون: Dan Han, Vicki Modisette, Melinda Forthofer, Rajib Paul

المصدر: Applied Network Science, Vol 9, Iss 1, Pp 1-31 (2024)

مصطلحات موضوعية: ERGM, Bayesian Analysis, Network, Penalized Model, Adaptive Lasso, Applied mathematics. Quantitative methods, T57-57.97

الوصف: Abstract The analysis of network data has become an increasingly prominent and demanding field across multiple research fields including data science, health, and social sciences, requiring the development of robust models and efficient computational methods. One well-established and widely employed modeling approach for network data is the Exponential Random Graph Model (ERGM). Despite its popularity, there is a recognized necessity for further advancements to enhance its flexibility and variable selection capabilities. To address this need, we propose a novel hierarchical Bayesian adaptive lasso model (BALERGM), which builds upon the foundations of the ERGM. The BALERGM leverages the strengths of the ERGM and incorporates the flexible adaptive lasso technique, thereby facilitating effective variable selection and tackling the inherent challenges posed by high-dimensional network data. The model improvements have been assessed through the analysis of simulated data, as well as two authentic datasets. These datasets encompassed friendship networks and a respondent-driven sampling dataset on active and healthy lifestyle awareness programs.

وصف الملف: electronic resource

العلاقة: https://doaj.org/toc/2364-8228Test

الوصول الحر: https://doaj.org/article/b721a0e418f6482f8a009626d88a2509Test

المؤلفون: Li, Haodong, Rosete, Sonali, Coyle, Jeremy, Phillips, Rachael V, Hejazi, Nima S, Malenica, Ivana, Arnold, Benjamin F, Benjamin-Chung, Jade, Mertens, Andrew, Colford, John M, van der Laan, Mark J, Hubbard, Alan E

المصدر: Statistics in medicine. 41(12)

مصطلحات موضوعية: Humans, Models, Statistical, Likelihood Functions, Regression Analysis, Causality, Research Design, Computer Simulation, Machine Learning, causal inference, highly adaptive lasso, machine learning, realistic simulation, targeted learning, Generic health relevance, Statistics, Public Health and Health Services, Statistics & Probability

الوصف: Several recently developed methods have the potential to harness machine learning in the pursuit of target quantities inspired by causal inference, including inverse weighting, doubly robust estimating equations and substitution estimators like targeted maximum likelihood estimation. There are even more recent augmentations of these procedures that can increase robustness, by adding a layer of cross-validation (cross-validated targeted maximum likelihood estimation and double machine learning, as applied to substitution and estimating equation approaches, respectively). While these methods have been evaluated individually on simulated and experimental data sets, a comprehensive analysis of their performance across real data based simulations have yet to be conducted. In this work, we benchmark multiple widely used methods for estimation of the average treatment effect using ten different nutrition intervention studies data. A nonparametric regression method, undersmoothed highly adaptive lasso, is used to generate the simulated distribution which preserves important features from the observed data and reproduces a set of true target parameters. For each simulated data, we apply the methods above to estimate the average treatment effects as well as their standard errors and resulting confidence intervals. Based on the analytic results, a general recommendation is put forth for use of the cross-validated variants of both substitution and estimating equation estimators. We conclude that the additional layer of cross-validation helps in avoiding unintentional over-fitting of nuisance parameter functionals and leads to more robust inferences.

الوصول الحر: https://escholarship.org/uc/item/90s412n6Test

المؤلفون: Weihai Huang, Xinyue Liu, Weize Yang, Yihua Li, Qiyan Sun, Xiangzeng Kong

المصدر: Sensors, Vol 24, Iss 12, p 3755 (2024)

مصطلحات موضوعية: electroencephalography, brain–computer interface, motor imagery, common spatial pattern, adaptive LASSO, Chemical technology, TP1-1185

الوصف: A motor imagery brain–computer interface connects the human brain and computers via electroencephalography (EEG). However, individual differences in the frequency ranges of brain activity during motor imagery tasks pose a challenge, limiting the manual feature extraction for motor imagery classification. To extract features that match specific subjects, we proposed a novel motor imagery classification model using distinctive feature fusion with adaptive structural LASSO. Specifically, we extracted spatial domain features from overlapping and multi-scale sub-bands of EEG signals and mined discriminative features by fusing the task relevance of features with spatial information into the adaptive LASSO-based feature selection. We evaluated the proposed model on public motor imagery EEG datasets, demonstrating that the model has excellent performance. Meanwhile, ablation studies and feature selection visualization of the proposed model further verified the great potential of EEG analysis.

وصف الملف: electronic resource

العلاقة: https://www.mdpi.com/1424-8220/24/12/3755Test; https://doaj.org/toc/1424-8220Test

الوصول الحر: https://doaj.org/article/5a72c3fce1a947e9beb000a7436f7beeTest

المؤلفون: Godbole, Suneeta, Labaki, Wassim, Pratte, Katherine, Hill, Andrew, Moll, Matthew, Hastie, Annette, Peters, Stephen, Gregory, Andrew, Ortega, Victor, DeMeo, Dawn, Cho, Michael, Bhatt, Surya, Wells, J, Barjaktarevic, Igor, Stringer, Kathleen, Comellas, Alejandro, ONeal, Wanda, Kechris, Katerina, Bowler, Russell

المصدر: Metabolites. 12(5)

مصطلحات موضوعية: COPD, adaptive LASSO, lung density, metabolomics

الوصف: Chronic obstructive pulmonary disease (COPD) is a disease with marked metabolic disturbance. Previous studies have shown the association between single metabolites and lung function for COPD, but whether a combination of metabolites could predict phenotype is unknown. We developed metabolomic severity scores using plasma metabolomics from the Metabolon platform from two US cohorts of ever-smokers: the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS) (n = 648; training/testing cohort; 72% non-Hispanic, white; average age 63 years) and the COPDGene Study (n = 1120; validation cohort; 92% non-Hispanic, white; average age 67 years). Separate adaptive LASSO (adaLASSO) models were used to model forced expiratory volume at one second (FEV1) and MESA-adjusted lung density using 762 metabolites common between studies. Metabolite coefficients selected by the adaLASSO procedure were used to create a metabolomic severity score (metSS) for each outcome. A total of 132 metabolites were selected to create a metSS for FEV1. The metSS-only models explained 64.8% and 31.7% of the variability in FEV1 in the training and validation cohorts, respectively. For MESA-adjusted lung density, 129 metabolites were selected, and metSS-only models explained 59.0% of the variability in the training cohort and 17.4% in the validation cohort. Regression models including both clinical covariates and the metSS explained more variability than either the clinical covariate or metSS-only models (53.4% vs. 46.4% and 31.6%) in the validation dataset. The metabolomic pathways for arginine biosynthesis; aminoacyl-tRNA biosynthesis; and glycine, serine, and threonine pathway were enriched by adaLASSO metabolites for FEV1. This is the first demonstration of a respiratory metabolomic severity score, which shows how a metSS can add explanation of variance to clinical predictors of FEV1 and MESA-adjusted lung density. The advantage of a comprehensive metSS is that it explains more disease than individual metabolites and can account for substantial collinearity among classes of metabolites. Future studies should be performed to determine whether metSSs are similar in younger, and more racially and ethnically diverse populations as well as whether a metabolomic severity score can predict disease development in individuals who do not yet have COPD.

وصف الملف: application/pdf

الوصول الحر: https://escholarship.org/uc/item/9wb6w0vgTest

المؤلفون: Morteza Amini, Mahdi Roozbeh, Nur Anisah Mohamed

المصدر: Mathematics, Vol 12, Iss 2, p 172 (2024)

مصطلحات موضوعية: adaptive LASSO, group LASSO, outlier, penalized approaches, robust methods, Mathematics, QA1-939

الوصف: Determining the predictor variables that have a non-linear effect as well as those that have a linear effect on the response variable is crucial in additive semi-parametric models. This issue has been extensively investigated by many researchers in the area of semi-parametric linear additive models, and various separation methods are proposed by the authors. A popular issue that might affect both estimation and separation results is the existence of outliers among the observations. In order to address this lack of sensitivity towards extreme observations, robust estimating approaches are frequently applied. We propose a robust method for simultaneously identifying the linear and nonlinear components of a semi-parametric linear additive model, even in the presence of outliers in the observations. Additionally, this model is sparse in that it may be used to determine which explanatory variables are ineffective by giving accurate zero estimates for their coefficients. To assess the effectiveness of the proposed method, a comprehensive Monte Carlo simulation study is conducted along with an application to investigate the dataset, which includes Boston property prices dataset.

العلاقة: https://www.mdpi.com/2227-7390/12/2/172Test; https://doaj.org/toc/2227-7390Test; https://doaj.org/article/f984084af945426cad101ed545172960Test

الإتاحة: https://doi.org/10.3390/math12020172Test
https://doaj.org/article/f984084af945426cad101ed545172960Test

المؤلفون: Allerbo, Oskar, 1985, Jörnsten, Rebecka, 1971

المصدر: Computational Statistics. 37(4):2029-2047

مصطلحات موضوعية: Additive models, Neural networks, Regularization, Adaptive lasso, Model selection, Non-parametric regression

الوصف: Non-parametric, additive models are able to capture complex data dependencies in a flexible, yet interpretable way. However, choosing the format of the additive components often requires non-trivial data exploration. Here, as an alternative, we propose PrAda-net, a one-hidden-layer neural network, trained with proximal gradient descent and adaptive lasso. PrAda-net automatically adjusts the size and architecture of the neural network to reflect the complexity and structure of the data. The compact network obtained by PrAda-net can be translated to additive model components, making it suitable for non-parametric statistical modelling with automatic model selection. We demonstrate PrAda-net on simulated data, where we compare the test error performance, variable importance and variable subset identification properties of PrAda-net to other lasso-based regularization approaches for neural networks. We also apply PrAda-net to the massive U.K. black smoke data set, to demonstrate how PrAda-net can be used to model complex and heterogeneous data with spatial and temporal components. In contrast to classical, statistical non-parametric approaches, PrAda-net requires no preliminary modeling to select the functional forms of the additive components, yet still results in an interpretable model representation.

وصف الملف: electronic

الوصول الحر: https://research.chalmers.se/publication/528140Test
https://research.chalmers.se/publication/528140/file/528140_Fulltext.pdfTest

المؤلفون: Jianfeng Wei, Jian Yang, Xuewen Cheng, Jie Ding, Shengquan Li

المصدر: Mathematics, Vol 11, Iss 24, p 4899 (2023)

مصطلحات موضوعية: adaptive lasso, data streams, dynamic coefficients, online estimation, regression analysis, Mathematics, QA1-939

الوصف: Streaming data sequences arise from various areas in the era of big data, and it is challenging to explore efficient online models that adapt to them. To address the potential heterogeneity, we introduce a new online estimation procedure to analyze the constantly incoming streaming datasets. The underlying model structures are assumed to be the generalized linear models with dynamic regression coefficients. Our key idea lies in introducing a vector of unknown parameters to measure the differences between batch-specific regression coefficients from adjacent data blocks. This is followed by the usage of the adaptive lasso penalization methodology to accurately select nonzero components, which indicates the existence of dynamic coefficients. We provide detailed derivations to demonstrate how our proposed method not only fits within the online updating framework in which the old estimator is recursively replaced with a new one based solely on the current individual-level samples and historical summary statistics but also adaptively avoids undesirable estimation biases coming from the potential changes in model parameters of interest. Computational issues are also discussed in detail to facilitate implementation. Its practical performance is demonstrated through both extensive simulations and a real case study. In summary, we contribute to a novel online method that efficiently adapts to streaming data environment, addresses potential heterogeneity, and mitigates estimation biases from changes in coefficients.

وصف الملف: electronic resource

العلاقة: https://www.mdpi.com/2227-7390/11/24/4899Test; https://doaj.org/toc/2227-7390Test

الوصول الحر: https://doaj.org/article/81eac2361ff8407fa6722a3b8fde2c6cTest

المؤلفون: Shida Ma, Yiming Hou, Yunquan Song, Feng Zhou

المصدر: Axioms, Vol 13, Iss 1, p 4 (2023)

مصطلحات موضوعية: spatial error model, exponential square loss, adaptive lasso, robust variable selection, Mathematics, QA1-939

الوصف: With the widespread application of spatial data in fields like econometrics and geographic information science, the methods to enhance the robustness of spatial econometric model estimation and variable selection have become a central focus of research. In the context of the spatial error model (SEM), this paper introduces a variable selection method based on exponential square loss and the adaptive lasso penalty. Due to the non-convex and non-differentiable nature of this proposed method, convex programming is not applicable for its solution. We develop a block coordinate descent algorithm, decompose the exponential square component into the difference of two convex functions, and utilize the CCCP algorithm in combination with parabolic interpolation for optimizing problem-solving. Numerical simulations demonstrate that neglecting the spatial effects of error terms can lead to reduced accuracy in selecting zero coefficients in SEM. The proposed method demonstrates robustness even when noise is present in the observed values and when the spatial weights matrix is inaccurate. Finally, we apply the model to the Boston housing dataset.

وصف الملف: electronic resource

العلاقة: https://www.mdpi.com/2075-1680/13/1/4Test; https://doaj.org/toc/2075-1680Test

الوصول الحر: https://doaj.org/article/0956ea67dc274e61a16e54014578b2d0Test

المؤلفون: Ferra Yanuar, Hazmira Yozza, Aidinil Zetra

المصدر: Frontiers in Applied Mathematics and Statistics, Vol 9 (2023)

مصطلحات موضوعية: low birth weight, Bayesian quantile regression, Bayesian Lasso quantile regression, Bayesian Adaptive Lasso quantile regression, quantile regression, Applied mathematics. Quantitative methods, T57-57.97, Probabilities. Mathematical statistics, QA273-280

وصف الملف: electronic resource

العلاقة: https://www.frontiersin.org/articles/10.3389/fams.2023.1233135/fullTest; https://doaj.org/toc/2297-4687Test

الوصول الحر: https://doaj.org/article/24c32f38c20d4986952c725e0bfb5107Test

المؤلفون: Qile Pan, Rongxin Zhu, Jun Qiu, Guang Cai

المصدر: PeerJ, Vol 11, p e14635 (2023)

مصطلحات موضوعية: Elite swimmer, Anthropometric characteristics, Discriminant modeling, Talent identification, Adaptive Lasso, Medicine, Biology (General), QH301-705.5

الوصف: Background Anthropometric characteristics are important factors that affect swimming performance. The aim of this study is to build a discriminant model using anthropometric factors to identify elite short-to-medium-distance freestyle swimmers through an adaptive Lasso approach. Methods The study recruited 254 swimmers (145 males and 109 females) who were divided them into elite (aged 17.9 ± 2.2 years, FINA points 793.8 ± 73.8) and non-elite (aged 17.1 ± 1.3 years, FINA points 560.6 ± 78.7) groups. Data for 73 variables were obtained, including basic information, anthropometric and derivative indicators. After filtering out highly correlated variables, 24 candidate variables were retained to be used in adaptive Lasso to select variables for prediction of elite swimmers. Deviance and area under the curve (AUC) were applied to assess the goodness of fit and prediction accuracy of the model, respectively. Results The adaptive Lasso selected 12 variables using the whole sample, with an AUC being 0.926 (95% CI [0.895–0.956]; P = 2.42 × 10−29). In stratified analysis by gender, nine variables were selected for male swimmers with an AUC of 0.921 (95% CI [0.880–0.963]; P = 8.82 × 10−17), and eight variables were for female swimmers with an AUC of 0.941 (95% CI [0.898–0.984]; P = 7.67 × 10−15). Conclusion The adaptive Lasso showed satisfactory performance in selecting anthropometric characteristics to identify elite swimmers. Additional studies with longitudinal data or data from other ethnicities are needed to validate our findings.

وصف الملف: electronic resource

العلاقة: https://peerj.com/articles/14635.pdfTest; https://peerj.com/articles/14635Test/; https://doaj.org/toc/2167-8359Test

الوصول الحر: https://doaj.org/article/bb40a9eca37046fd9d54db5cb15b5e1fTest