Forecasting Levels in Loglinear Unit Root Models

Negative Binomial Loglinear Mixed Models

Spatial scan statistics in loglinear models

The likelihood ratio spatial scan statistic has been widely used in spatial disease surveillance and spatial cluster detection applications. In order to better understand cluster mechanisms, an equivalent model-based approach is proposed to the spatial scan statistic that unifies currently loosely coupled methods for including ecological covariates in the spatial scan test. In addition, the uti...

Functional Coefficient Models under Unit Root Behavior

Abstract. We analyze the statistical properties of nonparametrically estimated functions in a functional-coefficient model if the data has a unit root. We show that the estimated function converges at a faster rate than under the stationary case. However, the estimator has a mixed normal distribution so that point-wise confidence intervals are calculated using the usual normal distribution theo...

Loglinear models for first-order probabilistic reasoning

Recent work on loglinear models in probabilistic constraint logic programming is applied to firstorder probabilistic reasoning. Probabilities are defined directly on the proofs of atomic formulae, and by marginalisation on the atomic formulae themselves. We use Stochastic Logic Programs (SLPs) composed of labelled and unlabelled definite clauses to define the proof probabilities. We have a cons...

Loglinear models for rst-order probabilistic reasoning

Recent work on loglinear models in proba-bilistic constraint logic programming is applied to rst-order probabilistic reasoning. Probabilities are deened directly on the proofs of atomic formulae, and by marginal-isation on the atomic formulae themselves. We use Stochastic Logic Programs (SLPs) composed of labelled and unlabelled deenite clauses to deene the proof probabilities. We have a conser...