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Bayesian Multinomial Regression with Class-specific Predictor Selection
| Content Provider | Semantic Scholar |
|---|---|
| Author | Gustafson, Paul Lefebvre, Geneviève |
| Copyright Year | 2008 |
| Abstract | Consider a multinomial regression model where the response, which indicates a unit's membership in one of several possible un-ordered 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 kth predic-tor on the propensity of the unit to belong to the jth 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. is a vector of p predictor variables. Taking the Y = 0 class as a reference, the association between Y and X is modulated by a c× p matrix of coefficients β, with β jk describing the effect of X k on membership in the jth class relative to the 0th class, for j = 1,. .. , c. Various link functions can be used to relate the class probabilities to the linear predictor βX. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://arxiv.org/pdf/0901.4208v1.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Arabic numeral 0 Bayes factor Bayesian network Branch predictor Class Coefficient Column (database) Genetic Selection Kerrison Predictor Model selection Modulation Multinomial logistic regression Population Parameter Process (computing) Programming paradigm Subgroup Tor Messenger |
| Content Type | Text |
| Resource Type | Article |
