We analyze multivariate binary time series using a mixed parameterization in terms of the conditional expectations given the past and the pairwise canonical interactions among contemporaneous variables. This allows consistent inference on the influence of past variables even if the...

We introduce robust regression-based online filters for multivariate time series and discuss their performance in real time signal extraction settings. We focus on methods that can deal with time series exhibiting patterns such as trends, level changes, outliers and a high level of noise as well...

This paper deals with the identification of treatment effects when the outcome variable is ordered. If outcomes are measured ordinally, previously developed methods to investigate the impact of an endogenous binary regressor on average outcomes cannot be applied as the expectation of an ordered...

One serious problem in deep hole drilling is the occurrence of a dynamic disturbances called spiralling. A common explanation for the occurrence of spiralling is the coincidence of time varying bending eigenfrequencies of the tool with multiples of the spindle rotation frequency. We propose a...

We present a new way of constructing bivariate copulas, by recalling and gluing two (or more) copulas. Examples illustrate how this construction can be applied to build complicated copulas from simple ones.