Publisher Summary Mixture distributions comprise a finite or infinite number of components,
possibly of different distributional types, that can describe different features of data. The …

Categorical data arise often in many fields, including biometrics, economics, management,
manufacturing, marketing, psychology, and sociology. This book provides an introduction to …

Using Kalman techniques, it is possible to perform optimal estimation in linear Gaussian
state-space models. Here, we address the case where the noise probability density …

At the heart of a new product sales-forecasting model for consumer packaged goods is a
multiple-event timing process. Even after controlling for the effects of time-varying marketing …

Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation
presents solutions to missing data problems through explicit or noniterative sampling …

The use of sparsity to encourage parsimony in graphical models continues to attract much
attention at the interface between multivariate Signal Processing and Statistics. We propose …

We consider the problem of detecting changes in a multivariate data stream. A change
detector is defined by a detection algorithm and an alarm threshold. A detection algorithm …

The detection of changes in the distribution of process variables is referred to as the change-
point problem. Existing methods focus on detecting a single (or few) change point in a …

We develop two cusum change-point detection algorithms for data network monitoring
applications where numerous and various performance and reliability metrics are available …

The activation of tectonics and anthropogenic swarms in time and space and size remains
challenging for seismologists. One remarkably long swarm is the Lacq swarm. It has been …