Graphical Influence Diagnostics for Changepoint Models

Analysis of Changepoint Models

Influence Diagnostics for Linear Mixed Models

Linear mixed models are extremely sensitive to outlying responses and extreme points in the fixed and random effect design spaces. Few diagnostics are available in standard computing packages. We provide routine diagnostic tools, which are computationally inexpensive. The diagnostics are functions of basic building blocks: studentized residuals, error contrast matrix, and the inverse of the res...

changepoint: An R Package for Changepoint Analysis

One of the key challenges in changepoint analysis is the ability to detect multiple changes within a given time series or sequence. The changepoint package has been developed to provide users with a choice of multiple changepoint search methods to use in conjunction with a given changepoint method and in particular provides an implementation of the recently proposed PELT algorithm. This article...

Learning Articulation Changepoint Models from Demonstration

We introduce CHAMP, an algorithm for online Bayesian changepoint detection in settings where it is difficult or undesirable to integrate over the parameters of candidate models. CHAMP is used in combination with several articulation models to detect changes in articulated motion of objects in the world, allowing a robot to infer physically-grounded task information. We focus on three settings w...

Influence Diagnostics for Correlated Data

Correlated or clustered data are frequently encountered in medical and dental research. Although there has been a large amount of research on analytic methods for clustered data, there has been only a limited amount of work in the area of influence diagnostics for these methods. We present a simple approach to influence diagnostics for clustered data that is practical even for very large data s...