Ordinal categorical time seriesmay be analyzed as censored observations from a suitable latent stochastic process, which describes the underlying evolution of the system. This approach may be considered as an alternative to Markov chain models or to regression methods for categorical time series data. The problem of parameter estimation is solved through a simple pseudolikelihood, called pairwi...

Multilevel analysis often leads to modeling with multiple latent variables on several levels. While this is less of a problem with Gaussian observed variables, maximum-likelihood (ML) estimation with categorical outcomes presents computational problems due to multidimensional numerical integration. We describe a new method that compared to ML is both computationally efficient and has similar MS...

The paper develops a class of priors that leads to equivalent posterior inference for odds ratio parameters based on prospective and retrospective models for categorical response data. The results are applicable to both unmatched and matched case-control studies. The results hold for a general class of link functions for categorical response. The proposed method can accommodate multiple and pos...

Statistical process adjustment (SPA) is utilised prevalently in novel manufacturing scenarios. When quality characteristics rather than internal process variables are inspected for the purpose of quality control, data with different resolutions may be collected. This paper proposes a Bayesian framework for parameter estimation when only categorical observations are available. The proposed metho...

We discuss the nature of cognitive representations and present a scheme for the encoding of information which accounts for both categorical and graded aspects of cognitive events. Accordingly, object categories are mapped onto the identity of cell populations via the process of categorical perception, while graded aspects such as vividness and con dence level are mapped onto the rate and degree...

In many biomedical studies, it is of interest to study the covariate effect on both longitudinal categorical outcomes and survival outcomes. For example, in cancer research, it is of interest to study the treatment effect on both quality of life which is a categorical outcome measured longitudinally and survival time. In this talk, we will discuss such joint models. Random effects are introduce...

Both the simulation research and software communities have been interested in optimization via simulation (OvS), by which we mean maximizing or minimizing the expected value of some output of a stochastic simulation. Continuous-decision-variable OvS, and gradient estimation to support it, has been an active research area with significant advances. However, the decision variables in many operati...

Abstract. We present a categorical model for intuitionistic linear logic where objects are polynomial diagrams and morphisms are simulation diagrams. The multiplicative structure (tensor product and its adjoint) can be defined in any locally cartesian closed category, whereas the additive (product and coproduct) and exponential (⊗-comonoid comonad) structures require additional properties and a...

Grounded cognition theory proposes that cognition, including meaning, is grounded in sensorimotor processing. The mechanism for grounding cognition is mental simulation, which is a type of mental imagery that re-enacts modal processing. To reveal top-down, cortical mechanisms for mental simulation of shape, event-related potentials were recorded to face and object pictures preceded by mental im...