Estimation and testing problems in auditory neuroscience via clustering
Author
Abstract
Suggested Citation
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Chris Fraley & Adrian E. Raftery, 2007. "Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 24(2), pages 155-181, September.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Roberto Rocci & Stefano Antonio Gattone & Roberto Di Mari, 2018. "A data driven equivariant approach to constrained Gaussian mixture modeling," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(2), pages 235-260, June.
- Sucharitha, Rahul Srinivas & Lee, Seokcheon, 2022. "GMM clustering for in-depth food accessibility pattern exploration and prediction model of food demand behavior," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
- Konon, Alexander, 2016. "Career choice under uncertainty," VfS Annual Conference 2016 (Augsburg): Demographic Change 145583, Verein für Socialpolitik / German Economic Association.
- Roberto Mari & Roberto Rocci & Stefano Antonio Gattone, 2020. "Scale-constrained approaches for maximum likelihood estimation and model selection of clusterwise linear regression models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(1), pages 49-78, March.
- Jelle R Dalenberg & Luca Nanetti & Remco J Renken & René A de Wijk & Gert J ter Horst, 2014. "Dealing with Consumer Differences in Liking during Repeated Exposure to Food; Typical Dynamics in Rating Behavior," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-11, March.
- Sakyajit Bhattacharya & Paul McNicholas, 2014. "A LASSO-penalized BIC for mixture model selection," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(1), pages 45-61, March.
- Patricia Gilholm & Kerrie Mengersen & Helen Thompson, 2020. "Identifying latent subgroups of children with developmental delay using Bayesian sequential updating and Dirichlet process mixture modelling," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-17, June.
- Steve Su, 2016. "Flexible modelling of survival curves for censored data," Journal of Statistical Distributions and Applications, Springer, vol. 3(1), pages 1-20, December.
- Seo, Byungtae & Kim, Daeyoung, 2012. "Root selection in normal mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2454-2470.
- Rijan Shrestha & Tomasz Kozlowski, 2016. "Inverse uncertainty quantification of input model parameters for thermal-hydraulics simulations using expectation--maximization under Bayesian framework," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(6), pages 1011-1026, May.
- Derek S. Young & Xi Chen & Dilrukshi C. Hewage & Ricardo Nilo-Poyanco, 2019. "Finite mixture-of-gamma distributions: estimation, inference, and model-based clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(4), pages 1053-1082, December.
- Csereklyei, Zsuzsanna & Thurner, Paul W. & Langer, Johannes & Küchenhoff, Helmut, 2017.
"Energy paths in the European Union: A model-based clustering approach,"
Energy Economics, Elsevier, vol. 65(C), pages 442-457.
- Zsuzsanna Csereklyei & Paul W. Thurner & Johannes Langer & Helmut Küchenhoff, 2017. "Energy paths in the European Union: A model-based clustering approach," CCEP Working Papers 1701, Centre for Climate & Energy Policy, Crawford School of Public Policy, The Australian National University.
- Oliver M Crook & Claire M Mulvey & Paul D W Kirk & Kathryn S Lilley & Laurent Gatto, 2018. "A Bayesian mixture modelling approach for spatial proteomics," PLOS Computational Biology, Public Library of Science, vol. 14(11), pages 1-29, November.
- Chi, Eric C. & Lange, Kenneth, 2014. "Stable estimation of a covariance matrix guided by nuclear norm penalties," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 117-128.
- Oscar Lao & Fan Liu & Andreas Wollstein & Manfred Kayser, 2014. "GAGA: A New Algorithm for Genomic Inference of Geographic Ancestry Reveals Fine Level Population Substructure in Europeans," PLOS Computational Biology, Public Library of Science, vol. 10(2), pages 1-11, February.
- Dolnicar, Sara & Grün, Bettina & Leisch, Friedrich, 2016. "Increasing sample size compensates for data problems in segmentation studies," Journal of Business Research, Elsevier, vol. 69(2), pages 992-999.
- ?uksza Marta & Kluge Bogus?aw & Ostrowski Jerzy & Karczmarski Jakub & Gambin Anna, 2009. "Two-Stage Model-Based Clustering for Liquid Chromatography Mass Spectrometry Data Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-36, February.
- Tin Lok James Ng & Thomas Brendan Murphy, 2021. "Model-based Clustering of Count Processes," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 188-211, July.
- Antonio Lentini & Huaitao Cheng & J. C. Noble & Natali Papanicolaou & Christos Coucoravas & Nathanael Andrews & Qiaolin Deng & Martin Enge & Björn Reinius, 2022. "Elastic dosage compensation by X-chromosome upregulation," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
- Pablo Cristini Guedes & Fernanda Maria Müller & Marcelo Brutti Righi, 2023. "Risk measures-based cluster methods for finance," Risk Management, Palgrave Macmillan, vol. 25(1), pages 1-56, March.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:biomet:v:73:y:2017:i:3:p:1010-1017. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.