Results 151 to 160 of about 430,573 (198)

Review of Ordinary Least Squares and Generalized Least Squares

1984
The purpose of this chapter is to review the fundamentals of ordinary least squares and generalized least squares in the context of linear regression analysis. The presentation here is somewhat condensed given our objective of focusing on more advanced topics in econometrics.
Thomas B. Fomby, Stanley R. Johnson, R. Carter Hill   +2 more
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Generalized Least Square Estimation

2002
In this chapter the fundamental concepts of the growth curve model (GCM) are introduced and several commonly encountered forms of the GCM are described through a variety of practical examples in biology, agriculture, and medical research. Some basic statistical inference of the GCM, such as generalized least square estimate (GLSEs) and the ...
Jian-Xin Pan, Kai-Tai Fang
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Generalized Least Squares

1998
This chapter considers a more general variance covariance matrix for the disturbances. In other words, u ~ (0, s2In) is relaxed so that u ~ (0, σ2Ω) where Ω is a positive definite matrix of dimension (n×n). First Ω is assumed known and the BLUE for β is derived.
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Generalized damped least squares algorithm

Computers & Chemical Engineering, 2003
We propose a new algorithm for adaptive control and self tuning control, referred to as the generalized damped least squares (GDLS) algorithm. This algorithm is constructed by adding a multi-step penalty for parameter variations to the objective function of the normal least squares algorithm to prevent the singularity problem that leads to estimation ...
Yoo, CK, Sung, SW, Lee, IB
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Generalized and Robust Least Squares Regression

IEEE Transactions on Neural Networks and Learning Systems
As a simple yet effective method, least squares regression (LSR) is extensively applied for data regression and classification. Combined with sparse representation, LSR can be extended to feature selection (FS) as well, in which l1 regularization is often applied in embedded FS algorithms.
Jingyu Wang, Fangyuan Xie, Feiping Nie, Xuelong Li   +3 more
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A generalized least squares approach

International Journal of Educational Research, 1990
Goldstein (1986, 1987) in England has proposed that the statistical model presented in Equation (6.4) should be used in order to analyze multilevel data. The coefficients of the model can be considered as either fixed or random and the number of levels involved in a multilevel model is not necessarily limited to two.
K.C. Cheung, J.P. Keeves, S.C. Tsoi
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Approximate Normality of Generalized Least Squares Estimates

Econometrica, 1984
Summary: When the error covariance matrix in a linear model depends on a few unknown parameters, the regression coefficients can be estimated by a two-step procedure. Consistent estimates of the covariance parameters are first obtained and then used in a generalized least squares regression.
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Backpropagation using generalized least squares

IEEE International Conference on Neural Networks, 2002
The backpropagation algorithm is essentially a steepest gradient descent type of optimization routine minimizing a quadratic performance index at each step. The backpropagation algorithm is re-cast in the framework of generalized least squares. The main advantage is that it eliminates the need to predict an optimal value for the step size required in ...
A.P. Loh, K.F. Fong
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Prediction in Generalized Least Squares

The American Statistician, 1977
Abstract An alternative derivation of the best linear unbiased predictor for the general linear model (in which the errors are nonspherical) is provided. This derivation is simpler than the original version due to Goldberger [3], and may be preferable for instructional purposes.
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A generalized least-squares model

Studia Geophysica et Geodaetica, 1970
A interpolation method is given for the case in which a function consisting of a systematic and a random part is to be estimated from measurements affected by errors. This is a combined problem of parameter estimation, filtering and prediction, which has applications in different fields of geodesy and gravimetry.
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