The original edition of this book was published in 1988. The first author, Ted Hannan,
passed away in 1994. Since the book went out of print a decade ago, I have been …

The paper gives a survey of errors-in-variables methods in system identification.
Background and motivation are given, and examples illustrate why the identification problem …

We investigate the effects of dynamic heteroskedasticity on statistical factor analysis. We
show that identification problems are alleviated when variation in factor variances is …

This book presents an overview of the different errors-in-variables (EIV) methods that can be
used for system identification. Readers will explore the properties of an EIV problem. Such …

This paper considers the problem of identifying linear systems, where the input is observed
in white noise but the output is observed in colored noise which also includes process …

Abstract High-Dimensional Dynamic Factor Models are presented in detail: The main
assumptions and their motivation, main results, illustrations by means of elementary …

The paper gives an overview of various methods for identifying dynamic errors-in-variables
systems. Several approaches are classified by how the original information in time-series …

We consider generalized linear dynamic factor models. These models have been developed
recently and they are used for high dimensional time series in order to overcome the “curse …

The paper gives an overview of errors-in-variables methods in system identification.
Background and motivation are given. Simple examples illustrate why the identification …

In this paper we present a structure theory for generalized linear dynamic factor models
(GDFM¿ s). Emphasis is laid on the so-called zeroless case. GDFM¿ s provide a way of …