Abstract
We review mixture models that relate a dependent variable to a set of exogenous or explanatory variables. Also, we describe a generalized linear regression mixture model that encompasses previously developed models as special cases. The model allows for a probabilistic classification of observations into segments and simultaneous estimation of a generalized linear regression model within each segment. Previous applications of the approach to market segmentation are extensively reviewed.
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© 2000 Springer Science+Business Media New York
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Wedel, M., Kamakura, W.A. (2000). Mixture Regression Models. In: Market Segmentation. International Series in Quantitative Marketing, vol 8. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-4651-1_7
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DOI: https://doi.org/10.1007/978-1-4615-4651-1_7
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-7104-5
Online ISBN: 978-1-4615-4651-1
eBook Packages: Springer Book Archive