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Estimation theory. Estimation theory is a branch of statistics 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 their value affects the distribution of the measured data. An estimator attempts to approximate ...
Fundamentals of Statistical Signal Processing: Estimation Theory. Prentice Hall. ISBN 0-13-042268-1. Moon, Todd K. (2000). Mathematical Methods and Algorithms for Signal Processing. Prentice-Hall. ISBN 0-201-36186-8
Spectral density estimation. In statistical signal processing, the goal of spectral density estimation (SDE) or simply spectral estimation is to estimate the spectral density (also known as the power spectral density) of a signal from a sequence of time samples of the signal. [1] Intuitively speaking, the spectral density characterizes the ...
Whittle likelihood. In statistics, Whittle likelihood is an approximation to the likelihood function of a stationary Gaussian time series. It is named after the mathematician and statistician Peter Whittle, who introduced it in his PhD thesis in 1951. [1] It is commonly used in time series analysis and signal processing for parameter estimation ...
Signal processing is an electrical engineering subfield that focuses on analyzing, modifying and synthesizing signals, such as sound, images, potential fields, seismic signals, altimetry processing, and scientific measurements. [1] Signal processing techniques are used to optimize transmissions, digital storage efficiency, correcting distorted ...
In estimation theory and statistics, the Cramér–Rao bound (CRB) relates to estimation of a deterministic (fixed, though unknown) parameter. The result is named in honor of Harald Cramér and C. R. Rao, [1][2][3] but has also been derived independently by Maurice Fréchet, [4] Georges Darmois, [5] and by Alexander Aitken and Harold ...
Like approximate entropy (ApEn), Sample entropy (SampEn) is a measure of complexity. [1] But it does not include self-similar patterns as ApEn does. For a given embedding dimension, tolerance and number of data points, SampEn is the negative natural logarithm of the probability that if two sets of simultaneous data points of length have distance < then two sets of simultaneous data points of ...
Ideal observer analysis is a method for investigating how information is processed in a perceptual system. [1] [2] [3] It is also a basic principle that guides modern research in perception. [4] [5] The ideal observer is a theoretical system that performs a specific task in an optimal way. If there is uncertainty in the task, then perfect ...