normal equations X'DXi8 =X'R'Y where the nonsingular k x k matrix D = R'R. Since the p x p matrix X'DXa has rank k ... The model can be rewritten in matrix form as Y = RX4/3 = E where the 6 x 3 replication matrix R, the 3 x 4 matrix Xd ...
In Section 3.2, we have measured the model's goodness of fit by the sum of squared errors S(3). ... (3.42) We will denote the covariance matrix of an estimator 3 by V(6): V(3) = E(3–E(3)(3 – E(3))'. (3.43) If E(3) = 3, then 3 will be ...
(98) Albert, A. (1972) Regression and the Moore-Penrose pseudoinverse, Mathematics in Science and Engineering, 94, Academic, New York. (30) Anderson, T.W. (1971) The Statistical Analysis of Time Series, Wiley Series in Probability and ...
SHRINKAGE ESTIMATION OF REGRESSION PARAMETERS AND VARIANCE COMPONENTS We shall consider shrinkage estimators of regression parameters and of variance components. First, we explain what shrinkage estimators are and how they can be more ...
Provides an easy-to-understand guide to statistical linear models and its uses in data analysis This book defines a broad spectrum of statistical linear models that is useful in the analysis of data.
Dieser Band beschreibt allgemeine Verfahren zur Abschätzung und Hypothesenprüfung für lineare statistische Modelle. Besonderen Wert legt das Buch auf die Interpretation unausgeglichener Daten.
Linear Models: An Introduction
... Plane Answers to Complex Questions: The Theory of Linear Models, Springer-Verlag, New York. Clarke, B. R. (1983). Uniqueness and Fréchet differentiability of functional solutions to maximum likelihood equations, Ann. Statist. 11: 1196 ...