Results 241 to 250 of about 536,991 (288)
Some of the next articles are maybe not open access.
Parameter Weighted Least Squares Fitting
IFAC Proceedings Volumes, 1985Abstract Parameter Weighted Least Squares (PWLS) fitting is a new approach to the linear-in-the-parameter fitting problem. PWLS is appropriate for parameter identification of dynamical systems. Algorithms for direct fitting and recursive fitting of the parameters are presented. A tuning of the fitting algorithm is provided by embracing scalar fitting
F.J. Kraus, M.F. Senning
openaire +1 more source
Weighted-Average Least Squares Prediction
Econometric Reviews, 2014Prediction under model uncertainty is an important and difficult issue. Traditional prediction methods (such as pretesting) are based on model selection followed by prediction in the selected model, but the reported prediction and the reported prediction variance ignore the uncertainty from the selection procedure.
Magnus, Jan R. +2 more
openaire +3 more sources
2013
Linear regression assumes that the spread of the outcome-values is the same for each predictor value. This assumption is, however, not warranted in many real life situations.
Ton J. Cleophas, Aeilko H. Zwinderman
openaire +1 more source
Linear regression assumes that the spread of the outcome-values is the same for each predictor value. This assumption is, however, not warranted in many real life situations.
Ton J. Cleophas, Aeilko H. Zwinderman
openaire +1 more source
Nonoptimally Weighted Least Squares
The American Statistician, 1988Abstract This research was supported in part by National Institutes of Health Grants AR20610 (Multipurpose Arthritis Center) and 2R01GM21215–12 (Adaptation of New Statistical Ideas for Medicine) awarded to Stanford University. Most of the ideas here originate with John Tukey, in published or unpublished work. The authors indicate that it was especially
Daniel A. Bloch, Lincoln E. Moses
openaire +1 more source
Weighted Least Squares Fitting Using Ordinary Least Squares Algorithms
Psychometrika, 1997A general approach for fitting a model to a data matrix by weighted least squares (WLS) is studied. This approach consists of iteratively performing (steps of) existing algorithms for ordinary least squares (OLS) fitting of the same model. The approach is based on minimizing a function that majorizes the WLS loss function.
openaire +1 more source
Optimized sample-weighted partial least squares
Talanta, 2007In ordinary multivariate calibration methods, when the calibration set is determined to build the model describing the relationship between the dependent variables and the predictor variables, each sample in the calibration set makes the same contribution to the model, where the difference of representativeness between the samples is ignored.
Lu, Xu +6 more
openaire +2 more sources
Weighted-average least squares: Improvements and extensions
The Stata Journal: Promoting communications on statistics and StataThis article presents version 3.0 of the wals command, which implements the weighted-average least-squares estimator of Magnus, Powell, and Prüfer (2010, Journal of Econometrics 154: 139–153).
Luca, Giuseppe De, Magnus, Jan R.
openaire +2 more sources
Weighted Least-Squares Smoothing Filters
IRE Transactions on Circuit Theory, 1955This paper differs from many others on least square filtering, in that no explicit note has been taken of the noise spectrum, at least no more than is taken when one fits a curve of least squares to a set of data. The concept of a least weighted error, not new in curve fitting, has, to the author's knowledge, never before been applied to a filter ...
openaire +1 more source