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Bayesian multinomial regression with class-specific predictor selection | Paul Gustafson
; Geneviève Lefebvre
; | Date: |
27 Jan 2009 | Abstract: | Consider a multinomial regression model where the response, which indicates a
unit’s membership in one of several possible unordered classes, is associated
with a set of predictor variables. Such models typically involve a matrix of
regression coefficients, with the $(j,k)$ element of this matrix modulating the
effect of the $k$th predictor on the propensity of the unit to belong to the
$j$th class. Thus, a supposition that only a subset of the available predictors
are associated with the response corresponds to some of the columns of the
coefficient matrix being zero. Under the Bayesian paradigm, the subset of
predictors which are associated with the response can be treated as an unknown
parameter, leading to typical Bayesian model selection and model averaging
procedures. As an alternative, we investigate model selection and averaging,
whereby a subset of individual elements of the coefficient matrix are zero.
That is, the subset of predictors associated with the propensity to belong to a
class varies with the class. We refer to this as class-specific predictor
selection. We argue that such a scheme can be attractive on both conceptual and
computational grounds. | Source: | arXiv, 0901.4208 | Services: | Forum | Review | PDF | Favorites |
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