This paper introduces the R-package ordinal for the analysis of ordinal data using
cumulative link models. The model framework implemented in ordinal includes partial …

This paper presents an integrated framework for estimation and inference from generalized
linear models using adjusted score equations that result in mean and median bias …

Meta-analysis is a common tool for synthesizing results of multiple studies. Among methods
for performing meta-analysis, the approach known as 'fixed effects' or 'inverse variance …

In this definitive book, DR Cox gives a comprehensive and balanced appraisal of statistical
inference. He develops the key concepts, describing and comparing the main ideas and …

This lively book lays out a methodology of confidence distributions and puts them through
their paces. Among other merits they lead to optimal combinations of confidence from …

In randomized experiments, treatment and control groups should be roughly the same—
balanced—in their distributions of pretreatment variables. But how nearly so? Can …

The datasets used in statistical analyses are often small in the sense that the number of
observations n is less than 5 times the number of parameters p to be estimated. In contrast …

A modern and comprehensive treatment of tolerance intervals and regions The topic of
tolerance intervals and tolerance regions has undergone significant growth during recent …

Robust statistics is an extension of classical parametric statistics that specifically takes into
account the fact that the assumed parametric models used by the researchers are only …

Ordered categorical data, or simply ordinal data, are commonplace in scientific disciplines
where humans are used as measurement instruments. Examples include school gradings …