DescriptionThe subject of this book is estimating parameters of expectation models of statistical observations. The book describes the most important aspects of the subject for applied scientists and engineers. This group of users is often not aware of estimators other than least squares. Therefore one purpose of this book is to show that statistical parameter estimation has much more to offer than least squares estimation alone. In the approach of this book, knowledge of the distribution of the observations is involved in the choice of estimators. A further advantage of the chosen approach is that it unifies the underlying theory and reduces it to a relatively small collection of coherent, generally applicable principles and notions.
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.
""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)