Parameter Estimation for Scientists and Engineers (eBook)

Adriaan van den Bos, PhD, is Professor Emeritus of the Department of Applied Physics of Delft University of Technology, The Netherlands. He carries out research in the field of statistical signal processing, parameter estimation, statistics, and application of parameter estimation to problems in applied physics, to optics and electron-optics in particular. He authored or coauthored some fifty journal papers, and his paper "Alternative Interpretation of Maximum Entropy Spectral Analysis," published in IEEE Transactions on Information Theory in 1971, became an official Citation Classic. In addition to journal papers, he has contributed to a number of books. In 2000, Dr. van den Bos was elected to the grade of Fellow of the Institute of Electrical and Electronics Engineers for his fundamental work in modeling and identification and its application in instrumentation and signal processing.

Preface.

1 Introduction.

2 Parametric Models of Observations.

3 Distributions of Observations.

4 Precision and Accuracy.

5 Precise and Accurate Estimation.

6 Numerical Methods for Parameter Estimation.

7 Solutions or Partial Solutions to Problems.

Appendix A: Statistical Results.

Appendix B: Vectors and Matrices.

Appendix C: Positive Semidefinite and Positive Definite
Matrices.

Appendix D: Vector and Matrix Differentiation.

References.

Topic Index.

"An indispensable tool for scholars and research workers in
mathematics and the mathematical sciences." (Mathematical
Reviews, 2009)

"Despite its lean size, the book is able to cover many of the
techniques and theories in parameter estimation that are core to
applied sciences, and so this is certainly a valuable reference for
researchers and graduate students alike. The book's exposition is
lucid, making it an accessible reading for someone with a
reasonable background in elementary statistics. Thus I think anyone
in applied sciences and engineering dealing with the implementation
of expectation models and aiming to estimate model parameters will
find this book helpful. This is a great addition to resources in
learning or reviewing statistical tools that emphasize taking
advantage of valuable information from data and improving the
precision of estimation." (Technometrics, November 2008)

"I highly recommend this book to practitioners who want to
systematically learn and use, new, better techniques for parameter
estimation." (Computing Reviews, September 10, 2008)

"...appropriate for students in advanced applied statistics
courses...even more useful as a supplemental resource..."
(CHOICE, January 2008)