Similar to the DINA model, the positive response probability is The DINO model is the dual model of the DINA model. The DINO model is often used in the application of psychiatric assessment, for which the positive response to a diagnostic question (item) could be due to the presence of one disorder (attributes) among several. Example 5 (C-RUM model) The GLM-type parametrization with a logistic link function is used for the compensatory reparameterized unified model (C-RUM), that is The corresponding item parameter vector is ( β j k : j = 1 , · · · , J, k = 0 , · · · , K ) . The C-RUM model is a compensatory model and one can recognize (6) as a structure in multidimensional IRT model or in factor analysis. We discuss the identifiability for two types of parameters separately: 1. item parameters and the attribute distribution, 2. item partial information and the Q -matrix. Let θ denote the vector of all item parameters and the attribute distribution. Its identifiability is defined as follows. Definition 1 The parameter θ is said to be identifiable if, for any θ ′ = θ , the resulting marginal distributions of the responses Y in (1) are distinct. If the parameter θ is identifiable, then, thanks to the entropy inequality [16] and under very mild conditions, the maximum likelihood estimator is consistent. The identifiability of the Q -matrix is different from that of the regular item parameters. In what follows, we provide some discussions on the estimation of the Q -matrix for a generic diagnostic clas- sification model. One may view this problem from different perspectives. The most straightforward 2.3 Identifiability of model parameters p$_{j,}$ α = (1 − s$_{j}$ ) ξ j $_{DINO}$( α ,Q $^{)}$g $^{1}$− ξ j $_{DINO}$( α ,Q ) j . p$_{j,}$ α = exp( β j 0 + ∑ K k =1 β j $_{k}$q$_{jk}$ α$_{k}$ ) 1 + exp( β j 0 + K k =1 β j $_{k}$q$_{jk}$ α$_{k}$ ) . (6) 8