نتائج البحث - "adaptive LASSO"

1

Efficient Distributed Learning for Large-Scale Expectile Regression With Sparsity

المصدر: IEEE Access, Vol 9, Pp 64732-64746 (2021)

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f912630c3a6945ba6307615602493464Test
https://doi.org/10.1109/access.2021.3075686Test

2

Granger Causality Testing in High-Dimensional VARs: A Post-Double-Selection Procedure

المؤلفون: Stephan Smeekes, Alain Hecq, Luca Margaritella

المساهمون: QE Econometrics, RS: GSBE Theme Data-Driven Decision-Making, RS: GSBE Theme Learning and Work, RS: FSE DACS Mathematics Centre Maastricht

المصدر: Journal of Financial Econometrics, 21(3):nbab023, 915-958. Oxford University Press

مصطلحات موضوعية: FOS: Computer and information sciences, Economics and Econometrics, ADAPTIVE LASSO, Nuisance variable, Computer science, Econometrics (econ.EM), c12 - Hypothesis Testing: General, Feature selection, FREQUENCY, Least squares, Methodology (stat.ME), FOS: Economics and business, CONFIDENCE-INTERVALS, Set (abstract data type), Lasso (statistics), Granger causality, BOOTSTRAP, Econometrics, Statistics::Methodology, high-dimensional inference, post-double-selection, Statistics - Methodology, Selection (genetic algorithm), Economics - Econometrics, MODEL SELECTION, RISK, REGULARIZED ESTIMATION, vector autoregressive models, INFERENCE, SHRINKAGE, c32 - "Multiple or Simultaneous Equation Models: Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models", Volatility (finance), Finance

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b5cc170b108e2bd84c294eeb69c6d3f2Test
https://doi.org/10.1093/jjfinec/nbab023Test

3

Toward Predicting Human Performance Outcomes From Wearable Technologies: A Computational Modeling Approach

المؤلفون: Tad T. Brunyé, Kenny Yau, Kana Okano, Grace Elliott, Sara Olenich, Grace E. Giles, Ester Navarro, Seth Elkin-Frankston, Alexander L. Young, Eric L. Miller

المصدر: Frontiers in Physiology, Vol 12 (2021)
Frontiers in Physiology

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3f6ca86d3af96c453ccdc5f8c87e7018Test
https://www.frontiersin.org/articles/10.3389/fphys.2021.738973/fullTest

4

Two-step approach for assessing the health effects of environmental chemical mixtures: application to simulated datasets and real data from the Navajo Birth Cohort Study

المؤلفون: Ji-Hyun Lee, Johnnye Lewis, Laurie G. Hudson, Li Luo

المصدر: Environmental Health, Vol 18, Iss 1, Pp 1-16 (2019)
Environmental Health

مصطلحات موضوعية: Adult, Adolescent, Computer science, Health, Toxicology and Mutagenesis, Feature selection, computer.software_genre, Cohort Studies, Young Adult, 03 medical and health sciences, lcsh:RC963-969, Lasso (statistics), Pregnancy, Southwestern United States, Humans, Computer Simulation, Adaptive lasso, Chemical mixtures, 0303 health sciences, Models, Statistical, Random Forest, Research, Dimensionality reduction, lcsh:Public aspects of medicine, Public Health, Environmental and Occupational Health, 030311 toxicology, lcsh:RA1-1270, Environmental Exposure, Middle Aged, Regression, Random forest, Data set, Variable (computer science), Identification (information), Two-step approach, lcsh:Industrial medicine. Industrial hygiene, Environmental Pollutants, Female, Data mining, Environmental Health, computer

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8f70b1b01f83020432af63c40dc0486dTest
http://link.springer.com/article/10.1186/s12940-019-0482-6Test

5

Shrinkage priors for Bayesian penalized regression

المؤلفون: Van Erp, Sara, Oberski, Daniel L., Mulder, Joris, Leerstoel Klugkist, Methodology and statistics for the behavioural and social sciences

المساهمون: Leerstoel Klugkist, Methodology and statistics for the behavioural and social sciences

المصدر: Journal of Mathematical Psychology, 89, 31. Academic Press Inc.

مصطلحات موضوعية: shrinkage priors, ADAPTIVE LASSO, INFORMATION, Computer science, Overfitting, Social and Behavioral Sciences, computer.software_genre, 0302 clinical medicine, Sociology, Taverne, Physical Sciences and Mathematics, Psychology, Statistics::Methodology, General Psychology, Shrinkage, Computer Sciences, Applied Mathematics, 05 social sciences, Quantitative Psychology, Regression, FOS: Sociology, FOS: Psychology, REGULARIZATION, regression, HORSESHOE, Penalization, Statistics and Probability, Statistics::Theory, penalization, Bayesian probability, MODELS, Feature selection, Empirical Bayes, Machine learning, Bayesian, 050105 experimental psychology, Normal distribution, 03 medical and health sciences, Statistics::Machine Learning, Frequentist inference, Prior probability, 0501 psychology and cognitive sciences, empirical bayes, Shrinkage priors, business.industry, FREQUENTIST, Statistics::Computation, bayesian, Artificial intelligence, business, computer, 030217 neurology & neurosurgery, VARIABLE-SELECTION

وصف الملف: image/pdf

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4ed86355653a0fc0890764ac58840d89Test
https://doi.org/10.1016/j.jmp.2018.12.004Test

6

Combination of Ensembles of Regularized Regression Models with Resampling-Based Lasso Feature Selection in High Dimensional Data

المؤلفون: Sangjin Kim, Abhijeet R Patil

المصدر: Mathematics, Vol 8, Iss 1, p 110 (2020)
Mathematics
Volume 8
Issue 1

وصف الملف: application/pdf

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::042330fa9fae6f12e608391fd3fac702Test
https://www.mdpi.com/2227-7390/8/1/110Test

7

Regularized Estimation of the Nominal Response Model

المؤلفون: Michela Battauz

المصدر: Multivariate behavioral research. 55(6)

مصطلحات موضوعية: Statistics and Probability, Mathematical optimization, Psychometrics, Computer science, Experimental and Cognitive Psychology, Overfitting, 01 natural sciences, Regularization (mathematics), 010104 statistics & probability, 0504 sociology, Arts and Humanities (miscellaneous), Lasso (statistics), Surveys and Questionnaires, Item response theory, Adaptation, Psychological, Reaction Time, Humans, Computer Simulation, multidimensional, 0101 mathematics, Adaptive lasso, lasso, Estimation, polytomous responses, Models, Statistical, Model selection, 05 social sciences, 050401 social sciences methods, item response theory, Polytomous Rasch model, General Medicine, Models, Theoretical, fused lasso, collapse, Research Design, Multidimensional Scaling Analysis, Algorithms, Numerical stability

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dd6a40c2e296d58d1cfce93b9596c62aTest
https://pubmed.ncbi.nlm.nih.gov/31682150Test

8

Macroeconomic forecasting using penalized regression methods

المؤلفون: Etienne Wijler, Stephan Smeekes

المساهمون: QE Econometrics, RS: GSBE Theme Data-Driven Decision-Making, RS: GSBE other - not theme-related research

المصدر: International Journal of Forecasting, 34(3), 408-430. Elsevier Science

مصطلحات موضوعية: Clustering high-dimensional data, APPROXIMATE FACTOR MODELS, SELECTION, Statistics::Theory, ADAPTIVE LASSO, Computer science, TIME-SERIES, Factor structure, 01 natural sciences, Factor models, 010104 statistics & probability, Statistics::Machine Learning, Lasso (statistics), 0502 economics and business, Econometrics, Statistics::Methodology, 0101 mathematics, Business and International Management, PREDICTORS, AUTOREGRESSIONS, 050205 econometrics, Factor analysis, Penalized regression, Cointegration, 05 social sciences, DYNAMIC FACTOR MODELS, Statistics::Computation, High-dimensional data, LARGE NUMBER, Macroeconomic forecasting, Principal component analysis, PRINCIPAL COMPONENT ANALYSIS, SHRINKAGE, Lasso, Forecasting

وصف الملف: application/pdf

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e94d80125a831c74d9911cfd5d9b36c3Test
https://linkinghub.elsevier.com/retrieve/pii/S0169207018300074Test

9

A Penalized Inference Approach to Stochastic Block Modelling of Community Structure in the Italian Parliament

المؤلفون: Ernst Wit, Mirko Signorelli

المساهمون: Stochastic Studies and Statistics

المصدر: Journal of the Royal Statistical Society: Series C
Journal of the Royal Statistical Society. Series C: Applied Statistics, 67(2), 355-369. Wiley
Journal of the Royal Statistical Society: Series C, 67(2), 355-369

مصطلحات موضوعية: FOS: Computer and information sciences, SELECTION, Statistics and Probability, Theoretical computer science, Parliament, Computer science, Chamber of Deputies, media_common.quotation_subject, Stochastic block model, ORACLE PROPERTIES, Inference, Network, LASSO, EXPONENTIAL-FAMILY, Statistics - Applications, 01 natural sciences, LIKELIHOOD, 010104 statistics & probability, Politics, Exponential family, 050602 political science & public administration, Applications (stat.AP), Adaptive lasso, 0101 mathematics, Bill cosponsorship, media_common, Social and Information Networks (cs.SI), 05 social sciences, Community structure, DIRECTED-GRAPHS, Computer Science - Social and Information Networks, Directed graph, NETWORKS, 0506 political science, BLOCKMODELS, Penalized likelihood, Statistics, Probability and Uncertainty

وصف الملف: application/pdf

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3769dda4b41ae524bc7d1be33cdffe50Test
https://doi.org/10.1111/rssc.12234Test

10

Smart building uncertainty analysis via adaptive Lasso

المؤلفون: Bei Yu, Bingqing Lin

المصدر: IET Cyber-Physical Systems (2017)

مصطلحات موضوعية: computational efficiency, lcsh:Computer engineering. Computer hardware, polynomials, Computer Networks and Communications, Computer science, building management systems, 020209 energy, lcsh:TK7885-7895, environmental parameters, 02 engineering and technology, 010501 environmental sciences, computer.software_genre, 01 natural sciences, lcsh:QA75.5-76.95, parameter identification, building performance simulation, statistical analysis, Lasso (statistics), environmental factors, Artificial Intelligence, energy consumption, automatic parameter selection, 0202 electrical engineering, electronic engineering, information engineering, higher order polynomial, uncertainty handling, smart building uncertainty analysis, Electrical and Electronic Engineering, Building energy simulation, Uncertainty analysis, 0105 earth and related environmental sciences, Building automation, Building management system, adaptive Lasso, Estimation theory, business.industry, building energy consumption, quadratic polynomial, Energy consumption, Quadratic function, physical parameters, Computer Science Applications, lcsh:Electronic computers. Computer science, Data mining, parameter estimation, business, computer, Information Systems

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d48b92e50cae9c5c3e4fb083ba7760c6Test
https://doi.org/10.1049/iet-cps.2017.0011Test

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