Score, Pseudo-Score and Residual Diagnostics for Spatial Point Process Models

Score and Pseudo-Score Confidence Intervals for Categorical Data Analysis

This article reviews methods for constructing confidence intervals for analyzing categorical data. A considerable literature indicates that the method of inverting score tests performs well for a variety of cases. When the sample size is small or the parameter is near the parameter space boundary, this method usually performs much better than inverting Wald tests and sometimes better than inver...

Propensity score-based diagnostics for categorical response regression models.

For binary or categorical response models, most goodness-of-fit statistics are based on the notion of partitioning the subjects into groups or regions and comparing the observed and predicted responses in these regions by a suitable chi-squared distribution. Existing strategies create this partition based on the predicted response probabilities, or propensity scores, from the fitted model. In t...

Score distributions for Pseudo Relevance Feedback

Relevance-Based Language Models, commonly known as Relevance Models, are successful approaches to explicitly introduce the concept of relevance in the statistical Language Modelling framework of Information Retrieval. These models achieve state-of-the-art retrieval performance in the Pseudo Relevance Feedback task. It is known that one of the factors that more affect to the Pseudo Relevance Fee...

Residuals and Diagnostics for Spatial Point Processes

Residual-based diagnostics for structural equation models.

Classical diagnostics for structural equation models are based on aggregate forms of the data and are ill suited for checking distributional or linearity assumptions. We extend recently developed goodness-of-fit tests for correlated data based on subject-specific residuals to structural equation models with latent variables. The proposed tests lend themselves to graphical displays and are desig...