Fundamentals of Statistical Signal Processing, Volume I¶
Kay, S. M. (1993). Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory. Prentice Hall.
Cited by¶
4 citations across 4 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Central Limit Theorem
- Finance — portfolio-return and risk machinery rests on aggregate-return normality, and its failures (heavy tails, dependence) are central failure modes. Metrology — measurement-error budgets sum many tiny independent error sources and treat the residual as Gaussian, which is what makes error bars meaningful. Signal processing — summed independent noise sources at a sensor are treated as additive white Gaussian noise, enabling matched filters, Kalman filters, and the entire Gaussian-noise toolkit.
This sourceJustifies modeling summed independent sensor noise as additive white Gaussian noise, underpinning matched and Kalman filters.
- Finance — portfolio-return and risk machinery rests on aggregate-return normality, and its failures (heavy tails, dependence) are central failure modes. Metrology — measurement-error budgets sum many tiny independent error sources and treat the residual as Gaussian, which is what makes error bars meaningful. Signal processing — summed independent noise sources at a sensor are treated as additive white Gaussian noise, enabling matched filters, Kalman filters, and the entire Gaussian-noise toolkit.
- Precision Weighting
- This is provably the minimum-variance unbiased linear combination, so "weight by reliability" is not a heuristic but the structural target of an optimality theorem.
This sourceProves that inverse-variance weighting yields the minimum-variance unbiased linear combination of independent estimates.
- This is provably the minimum-variance unbiased linear combination, so "weight by reliability" is not a heuristic but the structural target of an optimality theorem.
- Signal Extraction
- In engineering signal processing, low-pass, band-pass, and Kalman filters separate a signal of known shape from broadband noise, and matched filters recover a known waveform from a noisy channel.
This sourceStandard reference for matched filtering, Wiener/Kalman estimation, and the signal-to-noise and integration-time results of signal extraction.
- In engineering signal processing, low-pass, band-pass, and Kalman filters separate a signal of known shape from broadband noise, and matched filters recover a known waveform from a noisy channel.
Domain-specific¶
Verification¶
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