RESIDUAL ANALYSIS IN RASCH POISSON COUNTS MODELS

Multivariate Poisson Hidden Markov Models for Analysis of Spatial Counts

Observation Driven Models for Poisson Counts

This paper is concerned with a general class of observation driven models for time series of counts whose conditional distributions given past observations and explanatory variables follow a Poisson distribution. These models provide a flexible framework for modeling a wide range of dependence structures. Conditions for stationarity and ergodicity of these processes are established from which t...

Hybrid Bilinear and Trilinear Models for Exploratory Analysis of Three-Way Poisson Counts

We propose a probabilistic model class for the analysis of three-way count data, motivated by studying the subjectivity of language. Our models are applicable for instance to a data tensor of how many times each subject used each term in each context, thus revealing individual variation in natural language use. As our main goal is exploratory analysis, we propose hybrid bilinear and trilinear m...

Understanding and Improving Residual Distributions for Linear Poisson Models

The method of Linear Poisson Models (LPMs) is able to construct approximating linear models of histogram data behaviour based upon the assumption of independant Poisson noise. Crucially the method has been developed with associated techniques for the assessment of the effects of noise on both model construction and subsequent use of these models in quantitative data analysis. As a consequence i...

Causal Rasch models

Rasch's unidimensional models for measurement show how to connect object measures (e.g., reader abilities), measurement mechanisms (e.g., machine-generated cloze reading items), and observational outcomes (e.g., counts correct on reading instruments). Substantive theory shows what interventions or manipulations to the measurement mechanism can be traded off against a change to the object measur...