Written for applied scientists and engineers, this book covers the most important aspects of estimating parameters of expectation models of statistical observations. The author demonstrates that statistical parameter estimation has much more to offer than least squares estimation alone, and explains how a priori knowledge may be used more fully to improve the precision of estimating. Parameter Estimation for Scientists and Engineers presents:

• An explanation of statistical parametric models of observations, and why they are used

• A description of distributions of observations including exponential families of distributions

• Fisher information and the Cramér-Rao lower bound, and how they are used to judge the quality of parameter estimators and experimental designs

• The maximum likelihood method and the least squares method for estimating parameters of expectation models

• A discussion of model hypothesis testing

• Numerical methods suitable for the parameter estimation problems dealt with in this book, as well as an exploration of how to use these methods in practice

Complete with sixty-two examples, eighty-nine problems and solutions, and thirty-four figures, Parameter Estimation for Scientists and Engineers is an invaluable reference for professionals and an ideal text for advanced undergraduate and graduate-level students in all disciplines of engineering and applied science.