ArticleJournal of the American Statistical Association2016
Variable Selection with Prior Information for Generalized Linear Models via the Prior LASSO Method.
Article in Journal of the American Statistical Association, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
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- Incorporating prior information in gene expression network-based cancer heterogeneity analysis.Biostatistics (Oxford, England) · 2025Article
- Development and validation of a nomogram model for predicting one-year unplanned readmission in patients with chronic obstructive pulmonary disease.European journal of medical research · 2025Article
- KULLBACK-LEIBLER-BASED DISCRETE FAILURE TIME MODELS FOR INTEGRATION OF PUBLISHED PREDICTION MODELS WITH NEW TIME-TO-EVENT DATASET.The annals of applied statistics · 2025Article
- Utilizing RNA-seq data in monotone iterative generalized linear model to elevate prior knowledge quality of the circRNA-miRNA-mRNA regulatory axis.BMC bioinformatics · 2025Article
- Impact of multiple environmental factors on influenza-like illness in Fujian Province, China, 2015-2023: a multicity study.Frontiers in public health · 2025Article
- Development and validation of a nomogram for predicting the outcome of metabolic syndrome among people living with HIV after antiretroviral therapy in China.Frontiers in cellular and infection microbiology · 2025Article
- Information-incorporated sparse hierarchical cancer heterogeneity analysis.Statistics in medicine · 2024Article
- Estimating Ejection Fraction from the 12 Lead ECG among Patients with Acute Heart Failure.medRxiv : the preprint server for health sciences · 2024Article
- Classification and clinical significance of immunogenic cell death-related genes in Plasmodium falciparum infection determined by integrated bioinformatics analysis and machine learning.Malaria journal · 2024Article
- Estimation and Inference for High-Dimensional Generalized Linear Models with Knowledge Transfer.Journal of the American Statistical Association · 2024Article
- Interleukin-6 and thyroid-stimulating hormone index predict plaque stability in carotid artery stenosis: analyses by lasso-logistic regression.Frontiers in cardiovascular medicine · 2024Article
- Establishment of a Risk Prediction Model for Metabolic Syndrome in High Altitude Areas in Qinghai Province, China: A Cross-Sectional Study.Diabetes, metabolic syndrome and obesity : targets and therapy · 2024Article
- Penalized regression with multiple sources of prior effects.Bioinformatics (Oxford, England) · 2023Article
- Radiomics analysis based on CT for the prediction of pulmonary metastases in ewing sarcoma.BMC medical imaging · 2023Article
- Prior information-assisted integrative analysis of multiple datasets.Bioinformatics (Oxford, England) · 2023Article
- Information-incorporated sparse convex clustering for disease subtyping.Bioinformatics (Oxford, England) · 2023Article
- Stability Approach to Regularization Selection for Reduced-Rank Regression.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2023Article
- A Novel Algorithm for Feature Selection Using Penalized Regression with Applications to Single-Cell RNA Sequencing Data.Biology · 2022Article
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3 authors.
Funding
Abstract
LASSO is a popular statistical tool often used in conjunction with generalized linear models that can simultaneously select variables and estimate parameters. When there are many variables of interest, as in current biological and biomedical studies, the power of LASSO can be limited. Fortunately, so much biological and biomedical data have been collected and they may contain useful information about the importance of certain variables. This paper proposes an extension of LASSO, namely, prior LASSO (pLASSO), to incorporate that prior information into penalized generalized linear models. The goal is achieved by adding in the LASSO criterion function an additional measure of the discrepancy between the prior information and the model. For linear regression, the whole solution path of the pLASSO estimator can be found with a procedure similar to the Least Angle Regression (LARS). Asymptotic theories and simulation results show that pLASSO provides significant improvement over LASSO when the prior information is relatively accurate. When the prior information is less reliable, pLASSO shows great robustness to the misspecification. We illustrate the application of pLASSO using a real data set from a genome-wide association study.
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