Suchergebnisse - "alternating expectation conditional maximization algorithm"

A bootstrap‐augmented alternating expectation‐conditional maximization algorithm for mixtures of factor analyzers

Autoren: Phillip Shreeves Jeffrey L. Andrews

Quelle: Stat. 8

Schlagwörter: 0101 mathematics, 01 natural sciences

Zugangs-URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/sta4.243
https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202002275237920301

Fast Matrix-Free Methods for Model-Based Personalized Synthetic MR Imaging

Autoren: Subrata Pal Somak Dutta Ranjan Maitra et al.

Weitere Verfasser: Subrata Pal Somak Dutta Ranjan Maitra et al.

Quelle: Journal of Computational and Graphical Statistics. 33:1109-1117

Schlagwörter: I.4.0, FOS: Computer and information sciences, J.3, I.4.6, Bloch transform, G.3, Alternating Expectation Conditional Maximization algorithm, deep image prior, Statistics - Applications, Statistics - Computation, 01 natural sciences, 62P10 (Primary), 62P30, 62E20, 62H10, 62H35, I.2.1, 03 medical and health sciences, 0302 clinical medicine, multilayered Gaussian Markov Random Field, Applications (stat.AP), Lanczos algorithm, 0101 mathematics, profile likelihood, variance estimation, Computation (stat.CO), DegreeDisciplines::Physical Sciences and Mathematics::Statistics and Probability::Applied Statistics

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2103.01532

Estimating basis functions in massive fields under the spatial mixed effects model

Autoren: Karl Pazdernik Ranjan Maitra Statistics

Weitere Verfasser: Karl Pazdernik Ranjan Maitra Statistics

Quelle: Statistical Analysis and Data Mining: The ASA Data Science Journal. 14:430-448

Schlagwörter: bandwidth, FOS: Computer and information sciences, range parameter, 05 social sciences, Alternating Expectation Conditional Maximization algorithm, maximum likelihood estimation, Machine Learning (stat.ML), basis functions, alternating expectation conditional maximization algorithm, Statistics - Applications, 01 natural sciences, Statistical Methodology, Methodology (stat.ME), DegreeDisciplines::Physical Sciences and Mathematics::Statistics and Probability::Statistical Methodology, Statistics - Machine Learning, 13. Climate action, fixed rank kriging, 62-00, 0502 economics and business, kriging, Applications (stat.AP), 0101 mathematics, Statistics - Methodology

Dateibeschreibung: application/pdf

Zugangs-URL: https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/sam.11537
http://arxiv.org/abs/2003.05990
http://ui.adsabs.harvard.edu/abs/2020arXiv200305990P/abstract
https://arxiv.org/abs/2003.05990
http://arxiv.org/pdf/2003.05990.pdf

Estimating basis functions in massive fields under the spatial mixed effects model

Autoren: Pazdernik, Karl Maitra, Ranjan Statistics

Weitere Verfasser: Pazdernik, Karl Maitra, Ranjan Statistics

Quelle: https://doi.org/10.1002/sam.11537.

Schlagwörter: DegreeDisciplines::Physical Sciences and Mathematics::Statistics and Probability::Statistical Methodology, alternating expectation conditional maximization algorithm, bandwidth, basis functions, fixed rank kriging, maximum likelihood estimation, range parameter

Dateibeschreibung: application/pdf

Relation: Estimating Basis Functions in Massive Fields under the Spatial Mixed Effects Model; https://dr.lib.iastate.edu/handle/20.500.12876/jrl8Q3Or

Verfügbarkeit: https://dr.lib.iastate.edu/handle/20.500.12876/jrl8Q3Or
https://hdl.handle.net/20.500.12876/jrl8Q3Or

A bootstrap‐augmented alternating expectation‐conditional maximization algorithm for mixtures of factor analyzers.

Autoren: Shreeves, Phillip Andrews, Jeffrey L.

Quelle: Stat; 2019, Vol. 8 Issue 1, p1-10, 10p

Fast Matrix-Free Methods for Model-Based Personalized Synthetic MR Imaging

Autoren: Pal, Subrata Dutta, Somak Maitra, Ranjan et al.

Weitere Verfasser: Pal, Subrata Dutta, Somak Maitra, Ranjan et al.

Quelle: https://doi.org/10.1080/10618600.2023.2284208.

Schlagwörter: DegreeDisciplines::Physical Sciences and Mathematics::Statistics and Probability::Applied Statistics, Alternating Expectation Conditional Maximization algorithm, Bloch transform, deep image prior, Lanczos algorithm, multilayered Gaussian Markov Random Field, profile likelihood, variance estimation

Dateibeschreibung: application/pdf

Relation: https://dr.lib.iastate.edu/handle/20.500.12876/JvNVPV9v

Verfügbarkeit: https://dr.lib.iastate.edu/handle/20.500.12876/JvNVPV9v
https://hdl.handle.net/20.500.12876/JvNVPV9v

Estimating basis functions in massive fields under the spatial random effects model

Autoren: Pazdernik, Karl T. Maitra, Ranjan

Quelle: Statistics Publications

Schlagwörter: kriging, fixed rank kriging, bandwidth, range parameter, basis functions, maximum likelihood estimation, Alternating Expectation Conditional Maximization algorithm, Statistical Methodology, stat, demo

Relation: https://lib.dr.iastate.edu/stat_las_pubs/304

Verfügbarkeit: https://lib.dr.iastate.edu/stat_las_pubs/304

Estimating Basis Functions in Massive Fields under the Spatial Mixed Effects Model

Autoren: Pazdernik, Karl Maitra, Ranjan Statistics

Weitere Verfasser: Pazdernik, Karl Maitra, Ranjan Statistics

Quelle: archive/lib.dr.iastate.edu/stat_las_pubs/304/2020_MaitraRanjan_EstimatingBasis.pdf|||Fri Jan 14 23:28:48 UTC 2022

Schlagwörter: Statistical Methodology, kriging, fixed rank kriging, bandwidth, range parameter, basis functions, maximum likelihood estimation, Alternating Expectation Conditional Maximization algorithm

Dateibeschreibung: application/pdf

Relation: Estimating basis functions in massive fields under the spatial mixed effects model; archive/lib.dr.iastate.edu/stat_las_pubs/304/; 1307; stat_las_pubs/304; https://dr.lib.iastate.edu/handle/20.500.12876/90626

Verfügbarkeit: https://dr.lib.iastate.edu/handle/20.500.12876/90626
https://hdl.handle.net/20.500.12876/90626

Logistic Normal Multinomial Factor Analyzers for Clustering Microbiome Data.

Autoren: Tu, Wangshu Subedi, Sanjeena

Quelle: Journal of Classification; Nov2023, Vol. 40 Issue 3, p638-667, 30p

Schlagwörter: MICROBIOTA, CONDITIONAL expectations, PARAMETER estimation, FACTOR structure, CLUSTER analysis (Statistics)

A robust factor analysis model based on the canonical fundamental skew-t distribution.

Autoren: Lin, Tsung-I Chen, I-An Wang, Wan-Lun

Quelle: Statistical Papers; Apr2023, Vol. 64 Issue 2, p367-393, 27p

Schlagwörter: FACTOR analysis, CONDITIONAL expectations, EXPECTATION-maximization algorithms, COVARIANCE matrices

Robust clustering of multiply censored data via mixtures of t factor analyzers.

Autoren: Wang, Wan-Lun Lin, Tsung-I

Quelle: TEST; Mar2022, Vol. 31 Issue 1, p22-53, 32p

Extending Cluster-Weighted Factor Analyzers for Multivariate Prediction and Interpretability.

Autoren: Qin, Xiaoke Martella, Francesca Subedi, Sanjeena

Quelle: Journal of Classification. Nov2025, p1-29.

Schlagwörter: *FACTOR analysis, *CRIME analysis, *STATISTICAL models, *SOCIOECONOMIC factors, *PARAMETER estimation, *MULTIVARIATE analysis, *FINITE mixture models (Statistics)

Geografische Kategorien: UNITED States

Flexible High-Dimensional Unsupervised Learning with Missing Data.

Autoren: Wei, Yuhong Tang, Yang McNicholas, Paul D.

Quelle: IEEE Transactions on Pattern Analysis & Machine Intelligence; Mar2020, Vol. 42 Issue 3, p610-621, 12p

Schlagwörter: CONDITIONAL expectations, PARAMETER estimation, GAUSSIAN mixture models, MISSING data (Statistics), DATA analysis, COVARIANCE matrices

Mixture of multivariate t nonlinear mixed models for multiple longitudinal data with heterogeneity and missing values.

Autoren: Wang, Wan-Lun

Quelle: TEST; Mar2019, Vol. 28 Issue 1, p196-222, 27p

Fuzzy K-expectiles clustering.

Autoren: D’Urso, Pierpaolo De Giovanni, Livia Federico, Lorenzo et al.

Quelle: Statistics & Computing; Apr2025, Vol. 35 Issue 2, p1-19, 19p

Cluster-weighted $$t$$ -factor analyzers for robust model-based clustering and dimension reduction.

Autoren: Subedi, Sanjeena Punzo, Antonio Ingrassia, Salvatore et al.

Quelle: Statistical Methods & Applications; Nov2015, Vol. 24 Issue 4, p623-649, 27p

Schlagwörter: CLUSTER analysis (Statistics), FACTOR analysis, ROBUST control, DIMENSION reduction (Statistics), ANALYSIS of covariance

A mixture of generalized hyperbolic factor analyzers.

Autoren: Tortora, Cristina McNicholas, Paul Browne, Ryan

Quelle: Advances in Data Analysis & Classification; Dec2016, Vol. 10 Issue 4, p423-440, 18p

A mixture of common skew-t factor analysers.

Autoren: Murray, Paula M. McNicholas, Paul D. Browne, Ryan P.

Quelle: Stat; 2014, Vol. 3 Issue 1, p68-82, 15p

Schlagwörter: SKEWNESS (Probability theory), CLUSTER analysis (Statistics), ROBUST statistics, FACTOR analysis, PARAMETER estimation

Extending mixtures of multivariate t-factor analyzers.

Autoren: Andrews, Jeffrey L. McNicholas, Paul D.

Quelle: Statistics & Computing; Jul2011, Vol. 21 Issue 3, p361-373, 13p, 1 Diagram, 14 Charts

Clustering and classification via cluster-weighted factor analyzers.

Autoren: Subedi, Sanjeena Punzo, Antonio Ingrassia, Salvatore et al.

Quelle: Advances in Data Analysis & Classification; Apr2013, Vol. 7 Issue 1, p5-40, 36p