Market Segmentation Using Brand Strategy Research: Bayesian Inference with Respect to Mixtures of Log-Linear Models
Author
Abstract
Suggested Citation
DOI: 10.1007/s00357-009-9040-1
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
- Herbert Hoijtink & Annelise Notenboom, 2004. "Model based clustering of large data sets: Tracing the development of spelling ability," Psychometrika, Springer;The Psychometric Society, vol. 69(3), pages 481-498, September.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Matthieu Marbac & Christophe Biernacki & Vincent Vandewalle, 2016. "Latent class model with conditional dependency per modes to cluster categorical data," 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. 10(2), pages 183-207, June.
- Daniël W. Palm & L. Andries Ark & Jeroen K. Vermunt, 2016. "Divisive Latent Class Modeling as a Density Estimation Method for Categorical Data," Journal of Classification, Springer;The Classification Society, vol. 33(1), pages 52-72, April.
- Matthieu Marbac & Christophe Biernacki & Vincent Vandewalle, 2015. "Model-Based Clustering for Conditionally Correlated Categorical Data," Journal of Classification, Springer;The Classification Society, vol. 32(2), pages 145-175, July.
- Chiheb-Eddine N’Cir & Nadia Essoussi & Mohamed Limam, 2015. "Kernel-Based Methods to Identify Overlapping Clusters with Linear and Nonlinear Boundaries," Journal of Classification, Springer;The Classification Society, vol. 32(2), pages 176-211, July.
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.- Davide Vidotto & Jeroen K. Vermunt & Katrijn van Deun, 2018. "Bayesian Multilevel Latent Class Models for the Multiple Imputation of Nested Categorical Data," Journal of Educational and Behavioral Statistics, , vol. 43(5), pages 511-539, October.
- Daniël W. Palm & L. Andries Ark & Jeroen K. Vermunt, 2016. "Divisive Latent Class Modeling as a Density Estimation Method for Categorical Data," Journal of Classification, Springer;The Classification Society, vol. 33(1), pages 52-72, April.
More about this item
Keywords
Bayesian computational statistics; Model based clustering; Log-linear modeling; Market segmentation; Brand strategy research; Markov Chain Monte Carlo methods; Missing data;All these keywords.
Statistics
Access and download statisticsCorrections
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:spr:jclass:v:26:y:2009:i:3:p:297-328. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.