Identification and qualitative comparison of sensitivity analysis methods that have been used
across various disciplines, and that merit consideration for application to foodsafety risk …

Bayesian Data Analysis describes how to conceptualize, perform, and critique statistical
analyses from a Bayesian perspective. Using examples largely from the authors' own …

Despite increasing interest in Bayesian approaches, especially across the social sciences, it
has been virtually impossible to find a text that introduces Bayesian data analysis in a …

The growth of biostatistics has been phenomenal in recent years and has been marked by
considerable technical innovation in both methodology and computational practicality. One …

Though there are many recent additions to graduate-level introductory books on Bayesian
analysis, none has quite our blend of theory, methods, and ap plications. We believe a …

Kendall's Advanced Theory of Statistics and Kendall's Library of Statistics The development
of modern statistical theory in the past fifty years is reflected in the history of the late Sir …

Longitudinal data are ubiquitous across Medicine, Public Health, Public Policy, Psychology,
Political Science, Biology, Sociology and Education, yet many longitudinal data sets remain …

In this paper we present a framework for addressing a variety of engineering design
challenges with limited empirical data and partial information. This framework includes …

At last—a social scientist's guide through the pitfalls of modern statistical computing
Addressing the current deficiency in the literature on statistical methods as they apply to the …

Outliers are one of the main concerns in statistics. Parametric identification results of
ordinary least squares are sensitive to outliers. Many robust estimators have been proposed …