Category:Estimation theory

E

• ► Estimation for specific distributions‎ (2 P)
• ► Estimation for specific parameters‎ (3 P)

F

• ► Fitting probability distributions‎ (5 P)

I

• ► Informal estimation‎ (7 P)

L

• ► Least squares‎ (25 P)

M

• ► M-estimators‎ (6 P)
• ► Monte Carlo methods‎ (7 C, 48 P)

P

• ► Point estimation performance‎ (8 P)

R

• ► Regression analysis‎ (10 C, 134 P)

S

• ► Signal estimation‎ (9 P)

U

• ► U-statistics‎ (4 P)

• Estimation theory
• Relaxed intersection
• Set estimation

A

• Adaptive estimator
• Auxiliary particle filter

B

• Backcasting
• Backus–Gilbert method
• Bayes estimator
• Bayesian 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
• Empirical likelihood
• Empirical probability
• Ensemble Kalman filter
• Estimating equations
• Estimation
• Estimation statistics
• Estimator
• Expectation–maximization algorithm
• Extended Kalman filter
• Extremum estimator

F

• Filtering problem (stochastic processes)
• Fisher information
• Fixed effects model
• Forecast error
• Formation matrix
• Fraction of variance unexplained

G

• Generalized canonical correlation

G cont.

• Generalized method of moments

H

• Helmert–Wolf blocking
• Hodges' estimator

I

• Identifiability
• Interval estimation
• Invariant estimator
• Invariant extended Kalman filter
• Inverse-variance weighting

J

• James–Stein estimator

K

• Kaplan–Meier estimator
• Karhunen–Loève theorem
• Kullback's inequality
• Kushner equation

L

• L-estimator
• Lag windowing
• 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
• Maximum spacing estimation
• Mean and predicted response
• Mean squared error
• Method of simulated moments
• Minimax estimator
• Minimum chi-square estimation
• Minimum distance estimation
• Minimum mean square error
• Minimum-variance unbiased estimator
• MINQUE

M cont.

• Motion estimation

N

• Nuisance parameter

O

• Observed information
• Ordinary least squares
• Orthogonality principle

P

• Particle filter
• Pitman closeness criterion
• Point estimation
• Pyrrho's lemma

Q

• Quasi-maximum likelihood

R

• Rao–Blackwell theorem
• Recursive Bayesian estimation
• Regularization perspectives on support vector machines
• Restricted maximum likelihood
• Richardson–Lucy deconvolution
• Risk function

S

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

T

• Testimator
• Tikhonov regularization
• Trend estimation
• Trimmed estimator

U

• U-statistic

V

• V-statistic

W

• Wiener deconvolution
• Wiener filter

Z

• Zakai equation