21 |
Equality of minimum variance unbiased estimator under two different modelsToh, Keng Choo. January 1975 (has links)
No description available.
|
22 |
Total least squares and constrained least squares applied to frequency domain system identificationYoung, William Ronald January 1993 (has links)
No description available.
|
23 |
A comparison of algorithms for least squares estimates of parameters in the linear modelAhn, Chul H January 2010 (has links)
Typescript (photocopy). / Digitized by Kansas Correctional Industries
|
24 |
Curvelet-domain least-squares migration with sparseness constraints.Herrmann, Felix J., Moghaddam, Peyman P. January 2004 (has links)
A non-linear edge-preserving solution to the least-squares migration problem with sparseness constraints is introduced. The applied formalism explores Curvelets as basis functions that, by virtue of their sparseness and locality, not only allow for a reduction of the dimensionality of the imaging problem but which also naturally lead to a non-linear solution with significantly improved signalto-noise ratio. Additional conditions on the image are imposed by solving a constrained optimization problem on the estimated Curvelet coefficients initialized by thresholding. This optimization is designed to also restore the amplitudes by (approximately) inverting the normal operator, which is like-wise the (de)-migration operators, almost diagonalized by the Curvelet transform.
|
25 |
Minimum bias estimation of the slope of a response surfaceMatteson, Richard James 08 1900 (has links)
No description available.
|
26 |
Best least squares solution of two-point boundary value problemsGentile, Giorlando Enrico. January 1975 (has links)
No description available.
|
27 |
Statistical frequency analysis by optimization of density functions to histogramsGrant, James Lucius 12 1900 (has links)
No description available.
|
28 |
Model based parameter estimation for image analysisMorrison, Steven January 1999 (has links)
No description available.
|
29 |
Nonlinear partial least squaresHassel, Per Anker January 2003 (has links)
Partial Least Squares (PLS) has been shown to be a versatile regression technique with an increasing number of applications in the areas of process control, process monitoring and process analysis. This Thesis considers the area of nonlinear PLS; a nonlinear projection based regression technique. The nonlinearity is introduced as a univariate nonlinear function between projections, or to be more specific, linear combinations of the predictor and the response variables. As for the linear case, the method should handle multicollinearity, underdetermined and noisy systems. Although linear PLS is accepted as an empirical regression method, none of the published nonlinear PLS algorithms have achieved widespread acceptance. This is confirmed from a literature survey where few real applications of the methodology were found. This Thesis investigates two nonlinear PLS methodologies, in particular focusing on their limitations. Based on these studies, two nonlinear PLS algorithms are proposed. In the first of the two existing approaches investigated, the projections are updated by applying an optimization method to reduce the error of the nonlinear inner mapping. This ensures that the error introduced by the nonlinear inner mapping is minimized. However, the procedure is limited as a consequence of problems with the nonlinear optimisation. A new algorithm, Nested PLS (NPLS), is developed to address these issues. In particular, a separate inner PLS is used to update the projections. The NPLS algorithm is shown to outperform existing algorithms for a wide range of regression problems and has the potential to become a more widely accepted nonlinear PLS algorithm than those currently reported in the literature. In the second of the existing approaches, the projections are identified by examining each variable independently, as opposed to minimizing the error of the nonlinear inner mapping directly. Although the approach does not necessary identify the underlying functional relationship, the problems of overfitting and other problems associated with optimization are reduced. Since the underlying functional relationship may not be established accurately, the reliability of the nonlinear inner mapping will be reduced. To address this problem a new algorithm, the Reciprocal Variance PLS (RVPLS), is proposed. Compared with established methodology, RVPLS focus more on finding the underlying structure, thus reducing the difficulty of finding an appropriate inner mapping. RVPLS is shown to perform well for a number of applications, but does not have the wide-ranging performance of Nested PLS.
|
30 |
Solution of nonlinear least-squares problems /Fraley, Christina. January 1987 (has links)
Thesis (Ph. D.)--Stanford University, 1987. / "June 1987." This research was supported in part by Joseph Oliger under Office of Naval Research contract N00014-82-K-0335, by Stanford Linear Accelerator Center and the Systems Optimization Laboratory under Army Research Office contract DAAG29-84-K-0156. Includes bibliographies.
|
Page generated in 0.0343 seconds