CALL FOR PAPERS
COMPUTATIONAL STATISTICS & DATA ANALYSIS
Special Issue on
Total Least Squares and Errors-in-Variables Modeling
http://www.elsevier.com/locate/csda


We are inviting submissions for a special issue of Computational
Statistics and Data Analysis dealing with total least squares methods
and errors-in-variables models.

The total least squares (TLS) method is a numerical linear algebra
tool for solving approximately overdetermined systems of equations
Ax=b, where both the vector b as well as the matrix A are subject to
errors. Since its definition by Golub and Van Loan in 1980, the
classical TLS method has been extended to solve weighted, structured,
and regularized TLS problems and was applied in signal processing,
system identification, computer vision, document retrieval, computer
algebra, and other fields.

Errors-in-variables models, also known as measurement error models,
are an alternative to the classical regression model in statistics
when both the dependent as well as the independent variables are
subject to errors. Errors-in-variables models are closely related to
the TLS method and provide statistical justification for the
deterministic approximation formulations used in the numerical linear
algebra literature.

With this special issue we are looking for the synergy of statistic
and computations that will provide better computational methods for
statistically meaningful estimators.

Key areas are:

Concepts and Properties : structured and weighted TLS, other norms,
misfit versus latency errors, nonlinear measurement error models,
dynamic errors-in-variables, hypersurface fitting, statistical,
numerical, robustness and optimization aspects

Algorithms : real-time, adaptive, recursive, neural, iterative
algorithms, based on SVD or related matrix/tensor decompositions,
architectures, complexity, accuracy, regularization, convergence,
lower rank approximations

Applications : array signal and image processing, filtering, system
identification, computer vision, document retrieval, spectral
analysis, harmonic retrieval, direction finding, geology, chemistry,
biomedicine

In particular, overview papers describing recent advances on any of
the above-mentioned topics are invited. Other topics related to total
least squares, errors-in-variables modeling, and their applications
are also welcome.

The papers should have a computational or empirical component in order
to be considered for publication.  The papers must further contain
original unpublished work that is not being submitted for publication
elsewhere. Submissions will be refereed according to standard
procedures for Computational Statistics and Data Analysis. Information
about the journal can be found at http://www.elsevier.com/locate/csda

The DEADLINE for submissions is October 1, 2006.
NOTIFICATION of decision is December 15, 2006.

Electronic submission is requested. Please e-mail a postscript or PDF
file of your manuscript double spaced and as concise as possible to:
[log in to unmask]

Special issue editors:

Professor
Sabine Van Huffel
K.U. Leuven
Dept. Elektrotechniek
ESAT-SCD(SISTA)
Kasteelpark Arenberg 10
B-3001 Leuven, Belgium

Professor Chi-Lun Cheng
Institute of Statistical Science
Academia Sinica
Taipei, Taiwan, R.O.C

Dr. Nicola Mastronardi
Istituto per le Applicazioni del Calcolo "M.Picone" sez. Bari
National Council of Italy
via G. Amendola 122/D
I-70126 Bari, Italy

Professor Chris Paige
McGill University
School of Computer Science
3480 University Street
Montreal, PQ, Canada H3A 2A7

Professor Alexander Kukush
Kyiv National Taras Shevchenko University
Volodymyrska st. 60, 01033, Kyiv, Ukraine