Clouse, Ginny (2012) Estimation theory and applications. Research World, Delhi, India. ISBN 978-81-323-3050-9
Preview |
Text
GinnyClouse2012_EstimationTheoryandApplications.pdf - Published Version Download (2MB) | Preview |
Abstract
Estimation theory is a branch of statistics and signal processing that deals with estimating the values of parameters based on measured/empirical data that has a random component. The parameters describe an underlying physical setting in such a way that the value of the parameters affects the distribution of the measured data. An estimator attempts to approximate the unknown parameters using the measurements. For example, it is desired to estimate the proportion of a population of voters who will vote for a particular candidate. That proportion is the unobservable parameter; the estimate is based on a small random sample of voters. Or, for example, in radar the goal is to estimate the range of objects (airplanes, boats, etc.) by analyzing the two-way transit timing of received echoes of transmitted pulses. Since the reflected pulses are unavoidably embedded in electrical noise, their measured values are randomly distributed, so that the transit time must be estimated. In estimation theory, it is assumed the measured data is random with probability distribution dependent on the parameters of interest. For example, in electrical communication theory, the measurements which contain information regarding the parameters of interest are often associated with a noisy signal. Without randomness, or noise, the problem would be deterministic and estimation would not be needed.
| Item Type: | Book |
|---|---|
| Subjects: | Q Science > QA Mathematics |
| Divisions: | Electronic Books |
| Depositing User: | Practical Student 02 |
| Date Deposited: | 20 Feb 2022 14:46 |
| Last Modified: | 27 Jul 2022 04:43 |
| URI: | http://odlsystem2.utm.my/id/eprint/2835 |
Actions (login required)
 |
View Item |