nonlinear least-squares regression
X-ray Characterization and Defect Control of III-Nitrides.
... data sets were fit simultaneously using a Genetic curve-fitting algorithm to determine global minimums in χ2 followed by a nonlinear least squares regression using[r] ...
124
The large deviation for the least squares estimator of nonlinear regression model based on WOD errors
... the least squares estimator in the nonlinear regression model are established, which extend the corresponding ones for independent errors and some dependent ...
11
Computer program of nonlinear, curved regression for ‘probacent’ probability equation in biomedicine
... of nonlinear, curved regression of the least sum of squares is designed to de- termine the constant values of γ in ...the least maximum-difference principle as suggested by the data ...
11
Estimation and Inference in Unstable Nonlinear Least Squares Models (Final)
... ever, it is plausible that, under the alternative, parameters follow a certain predefined process, such as a random walk. Nyblom and M¨akel¨ainen (1983) derive locally most powerful sup F-tests against this alternative. ...
164
Partially linear models
... linear regression smoothing in ...linear regression techniques to various statistical ...include least squares regression, asymptotically efficient estimation, bootstrap resampling, ...
216
Identification of nonlinear systems with non-persistent excitation using an iterative forward orthogonal least squares regression algorithm
... 3 optimal model on a global solution space. A more general iOFR algorithm is proposed in this paper and it will be shown that the new iOFR algorithm is robust to some non-persistent inputs. It is worth emphasising that ...
14
Estimation and inference in unstable nonlinear least squares models
... or nonlinear set- tings, most subsequent work - see inter alia Anderson and Mizon (1983), Andrews and Fair (1988), Ghysels and Hall (1990), Andrews (1993), Sowell (1996), Hall and Sen (1999) and Andrews (2003) - ...
56
Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space
... the least squares estimators of the regression ...the regression coefficients that are too large in absolute ...estimated regression coefficients may change considerably given different data ...
27
Local Regularization Assisted Orthogonal Least Squares Regression
... practical nonlinear data modeling is the parsimonious principle of ensuring the smallest possible model that explains the ...orthogonal least squares (OLS) algorithm [9,11] is an efficient learning ...
27
Ultra-Orthogonal Forward Regression Algorithms for the Identification of Non-Linear Dynamic Systems
... Ultra Least Squares (ULS) criterion is introduced for system ...standard least squares criterion which is based on the Euclidean norm of the residuals, the new ULS criterion is derived from ...
25
Orthogonal least squares regression with tunable kernels
... practical nonlinear data modelling is the parsimonious principle of ensuring the smallest possible model that explains the training ...orthogonal least squares (OLS) algorithm with tunable kernels is ...
5
Solving Nonlinear Least Squares Problem Using Gauss-Newton Method
... The iterative algorithm of the Gauss-Newton method that is used for solving the nonlinear least squares problem was discussed in this paper. In general, it is typically difficult to decide an ...
5
“Data Mining techniques differentiation between NPPLS and PLS”
... Partial Least Squares (PLS) and one nonlinear technique(NLPLS)) on two different and unique data sets: a collinear data set (called "the COL" data set in this paper) and a simulated data set ...
12
Hybrid Levenberg–Marquardt and weak-constraint ensemble Kalman smoother method
... the nonlinear least-squares problem is solved iteratively by solv- ing a succession of linearized least-squares ...for nonlinear least squares (Bell, 1994; Tshi- ...
15
Newton Krylov Type Algorithm for Solving Nonlinear Least Squares Problems
... The minimization of a quadratic function within an ellipsoidal trust region is an important subproblem for many nonlinear programming algorithms. When the number of variables is large, one of the most widely used ...
17
On the Consistency of Feature Selection using Greedy Least Squares Regression
... This paper studies the feature selection problem using a greedy least squares regression algorithm. We show that under a certain irrepresentable condition on the design matrix (but independent of the ...
14
Kernel methods for short-term spatio-temporal wind prediction
... Abstract—Two nonlinear methods for producing short-term spatio-temporal wind speed forecast are ...kernel least mean squares algorithm and kernel recursive least squares algorithm are ...
5
Speech Emotion Recognition with MPCA and Kernel Partial Least Squares Regression
... Abstract—Speech signal is one of the major means for communication which carries not only semantic, but personal information , such as genders and emotions. The researches about speech emotion have become more and more ...
7
ESTIMATION ACCURACY OF EXPONENTIAL DISTRIBUTION PARAMETERS
... the least squares method, relative least squares, ridge regression, moment and modified moment estimators, maximum and modified maximum likelihood estimators to estimate the two ...
16
Object Detection and Tracking Using Uncalibrated Cameras
... using nonlinear least squares algorithm the camera internal and external parameters are ...case, nonlinear least squares give the local minimum value with certain ...
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