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Product recommendation models are key tools in customer relationship management (CRM). This study develops a product recommendation model based on the principle that customer preference similarity stemming from prior purchase behavior is a key element in predicting current product purchase. The...
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While MRF models have been widely used in the solution of inverse problems, a major disadvantage of these models is the difficulty of parameter estimation. At its root, this parameter estimation problem stems from the inability to explicitly express the joint distribution of an MRF in terms of...
Persistent link: https://www.econbiz.de/10009430608
In this paper, we consider the problem of learning Gaussian multiresolution (MR) models in which data are only available at the finest scale, and the coarser, hidden variables serve to capture long-distance dependencies. Tree-structured MR models have limited modeling capabilities, as variables...
Persistent link: https://www.econbiz.de/10009432147
Models with structured additive predictor provide a very broad and rich framework for complex regression modeling. They can deal simultaneously with nonlinear covariate effects and time trends, unit- or cluster-specific heterogeneity, spatial heterogeneity and complex interactions between...
Persistent link: https://www.econbiz.de/10010312244
We propose geoadditive survival models for analyzing effects jointly with possibly nonlinear effects of other covariates. Within a unified Bayesian frame work, our approach extends the classical Cox model to a more general multiplicative hazard rate model, augmenting the common linear predictor...
Persistent link: https://www.econbiz.de/10010266134
We define and investigate classes of statistical models for the analysis of associations between variables, some of which are qualitative and some quantitative. In the cases where only one kind of variables is present, the models are well-known models for either contingency tables or covariance...
Persistent link: https://www.econbiz.de/10009441395
Space-varying regression models are generalizations of standard linear models where the regression coefficients are allowed to change in space. The spatial structure is specified by a multivariate extension of pairwise difference priors thus enabling incorporation of neighboring structures and...
Persistent link: https://www.econbiz.de/10012234115