Category:Estimation theory

• Estimation theory

A

• Adaptive estimator

B

• Backcasting
• Bayes estimator
• Naive Bayes spam filtering
• Best linear unbiased prediction
• Blind deconvolution

C

• Chapman–Robbins bound
• Chebyshev center
• Confidence region
• Consistent estimator
• Cramér–Rao bound

D

• Data assimilation
• Delphi method

E

• Efficient estimator
• Ensemble Kalman filter
• Estimand
• Estimating equations
• Estimator
• Expectation–maximization algorithm
• Extended Kalman filter

F

• Fisher information
• Fixed effects model
• Forecast error
• Formation matrix
• Fraction of variance unexplained

G

• Generalized canonical correlation

H

• Helmert–Wolf blocking

I

• Interval estimation
• Invariant estimator

J

• James–Stein estimator

K

• Kaplan–Meier estimator
• Karhunen–Loève theorem

L

• L-estimator
• Least absolute deviations
• Lehmann–Scheffé theorem
• Likelihood function
• Likelihood principle
• Linear prediction
• Linear regression
• Location estimation in sensor networks

M

• M-estimator
• Matched filter
• Maximum a posteriori estimation
• Maximum likelihood
• Mean and predicted response
• Mean squared error
• Minimax estimator
• Minimum mean square error
• MINQUE
• Motion estimation

N

• Nuisance parameter

O

• Ordinary least squares
• Orthogonality principle

P

• Particle filter
• Point estimation

Q

• Quasi-maximum likelihood

R

• Recursive Bayesian estimation
• Richardson–Lucy deconvolution

S

• Score (statistics)
• Sequential estimation
• Shrinkage (statistics)
• Simple linear regression
• Small area estimation
• Spectral density estimation
• Stein's unbiased risk estimate
• Stochastic optimization

T

• Tikhonov regularization
• Linear trend estimation
• Trimmed estimator

U

• U-statistic

W

• Wiener deconvolution
• Wiener filter