Total Least Squares and Errors-in-Variables Modeling: Analysis, Algorithms and Applications [Hardcover]

In response to a growing interest in Total Least Squares (TLS) and Errors-In-Variables (EIV) modeling by researchers and practitioners, well-known experts from several disciplines were invited to prepare an overview paper and present it at the third international workshop on TLS and EIV modeling held in Leuven, Belgium, August 27-29, 2001. These invited papers, representing two-thirds of the book, together with a selection of other presented contributions yield a complete overview of the main scientific achievements since 1996 in TLS and Errors-In-Variables modeling. In this way, the book nicely completes two earlier books on TLS (SIAM 1991 and 1997). Not only computational issues, but also statistical, numerical, algebraic properties are described, as well as many new generalizations and applications. Being aware of the growing interest in these techniques, it is a strong belief that this book will aid and stimulate users to apply the new techniques and models correctly to their own practical problems.to Total Least Squares and Errors-in-Variables Modeling.- TLS and its Improvements by Semiparametric Approach.- Unifying Least Squares, Total Least Squares and Data Least Squares.- Bounds for the Least Squares Residual Using Scaled TLS.- Recent Developments in Rank Revealing and Lanczos Methods for Total Least Squares Related Problems.- A Regularized Total Least Squares Algorithm.- The Parametric Quadratic Form Method for Solving TLS Problems with Elementwise Weighting.- Structured Total Least Squares: Analysis, Algorithms and Applications.- Fast Structured Total Least Squares Algorithms via Exploitation of the Displacement Structure.- The Extended STLS Algorithm for Minimizing the Extended LS Criterion.- Bayesian Smoothing for Measurement Error Problems.- On the Polynomial Measurement Error Model.- On Consistent Estimators in Nonlinear Functional EIV Models.- On Consistent Estimators in Linear and Bilinear Multivariate Errors-in-Variables Models.- Idenl#"