About the Book
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 118. Chapters: Likelihood function, Linear regression, Linear prediction, Likelihood principle, Maximum likelihood, Estimator, Point estimation, Interval estimation, Ordinary least squares, Expectation-maximization algorithm, Maximum spacing estimation, Fisher information, Ensemble Kalman filter, Delphi method, Bayesian spam filtering, Generalized method of moments, Bayes estimator, Cramer-Rao bound, Particle filter, M-estimator, Wiener filter, Matched filter, Mean squared error, Invariant estimator, Simple linear regression, Data assimilation, Tikhonov regularization, Trend estimation, James-Stein estimator, Fixed effects model, Extended Kalman filter, Location estimation in sensor networks, Minimax estimator, V-statistic, Consistent estimator, Rao-Blackwell theorem, Orthogonality principle, Stochastic optimization, Filtering problem, Maximum a posteriori estimation, Identifiability, U-statistic, Confidence region, Wiener deconvolution, Efficient estimator, Minimum mean square error, Kullback's inequality, Chebyshev center, Minimum-variance unbiased estimator, Kaplan-Meier estimator, Score, Recursive Bayesian estimation, Invariant extended Kalman filter, Hodges' estimator, Minimum distance estimation, Blind deconvolution, Extremum estimator, Best linear unbiased prediction, Mean and predicted response, Empirical probability, Backcasting, Stein's unbiased risk estimate, Restricted maximum likelihood, Nuisance parameter, Shrinkage estimator, Chapman-Robbins bound, Richardson-Lucy deconvolution, Motion estimation, Observed information, Zakai equation, Fraction of variance unexplained, Estimating equations, Adaptive estimator, Quasi-maximum likelihood, Risk function, Helmert-Wolf blocking, Forecast error, Auxiliary particle filter, Small area estimation, Spectral density estimation, Scoring algorithm, Testimator, L-estimator, Lehman...
