Bøger af Randall L. Eubank

Provides a unified account of the most popular approaches to nonparametric regression smoothing. This edition contains discussions of boundary corrections for trigonometric series estimators; detailed asymptotics for polynomial regression; testing goodness-of-fit; estimation in partially linear models; practical aspects, problems and methods for co

This text provides a self-contained, "no frills," mathematically rigorous derivation from first principles of all basic Kalman filter recursions. This approach relies on a pared-down version of more general state-space models found most often in the literature. Such simplification saves notational complexity without sacrificing conceptual understanding. The rigor found in the book ensures a fundamental understanding of how the Kalman filter actually works, which builds confidence for those employing the filter in their research and writing code to implement it in practice. The author provides implementations of the Kalman filter in Java available for download from his Web site.

The Kalman filter is an important tool for estimating the variables in a system in the presence of noise. This title offers a fundamental understanding of how the Kalman filter actually works, which builds confidence for those employing the filter in their research and writing code to implement it in practice.

Presents an account of popular approaches to nonparametric regression smoothing. This book discusses boundary corrections for trigonometric series estimators; asymptotics for polynomial regression; testing goodness-of-fit; estimation in partially linear models; and practical aspects, problems and methods for confidence intervals and bands.