nonlinear least squares estimator
The large deviation for the least squares estimator of nonlinear regression model based on WOD errors
... regression model, one can refer to Ibregimov and Has’minskii [], Ivanov and Leonenko [], Ivanov [], and so on. In this paper, the large deviation results for the least squares estimator of the ...
11
Regularized Nonlinear Least Trimmed Squares Estimator in the Presence of Multicollinearity and Outliers
... Abstract: This study proposes a regularized robust Nonlinear Least Trimmed squares estimator that relies on an Elastic net penalty in nonlinear regression. Regularization parameter ...
7
Minimization of Error in Exponential Model Estimation via Jackknife Algorithm
... the least squares estimator, they proposed a class of weighted jackknife variance estimators for the least squares estimator by deleting any fixed number of observations at a ...
11
NLINLS: a Differential Evolution based nonlinear least squares Fortran 77 program
... Introduction: Curve fitting or estimation by nonlinear least squares is a difficult task. There are two types of algorithm that are often used for this purpose: those that need evaluation of ...
14
A plug in averaging estimator for regressions with heteroskedastic errors
... averaging estimator for the linear regression model with het- eroskedastic ...averaging estimator in a local asymptotic framework, and then choose the optimal weights by minimizing the ...plug-in ...
46
Development of a robust hybrid estimator using partial least squares regression and artificial neural networks.
... The aim of this paper is ro develop a robust inferentjal estimator by usjng hybrid PLS-ANN model based on on-line measuements of process variables, such as flow raies and temperatur[r] ...
8
Generalized Random Coefficient Estimators of Panel Data Models: Asymptotic and Small Sample Properties
... pooled least squares (classical pooling) estimator is the best linear unbiased estimator (BLUE) under the classical assumptions as in the general linear regression ...
24
Statistical inference in two non-standard regression problems
... Many estimators in statistics are defined as the maximizers of certain stochas- tic processes, called objective functions. This procedure for computing esti- mators is known as M-estimation and is quite common in modern ...
214
Newton Krylov Type Algorithm for Solving Nonlinear Least Squares Problems
... 7 M. J. D. Powell, “Convergence properties of a class of minimization algorithms,” in Nonlinear Programming, 2 (Proc. Sympos. Special Interest Group on Math. Programming, Univ. Wisconsin, Madison, Wis., 1974), O. ...
17
Solving Nonlinear Least Squares Problem Using Gauss-Newton Method
... and least squares approach [1]. For these methods, the method of least squares is a standard method to approximate the solution of over determined ...systems. Least squares is ...
5
Using wavelets to obtain a consistent ordinary least squares estimator of the long memory parameter
... MW estimator includes the maximum scaling coecient (a measure of the signal's average value) with its corresponding variance in the calculation of the likelihood ...
17
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 ...
68
Identification of some nonlinear systems by using least-squares support vector machines
... muscle activity triggers a range of deleterious adaptations. Muscle cross-sectional area declines by as much as 45 % in the first six weeks after injury, with further additional atrophy occurring for at least six ...
131
On determination of cointegration ranks
... ordinary least squares (Engle and Granger (1987)), nonlinear least squares (Stock (1987)), maximum likelihood in an error correction model (Johansen (1988)), principal components (Stock ...
25
Consistent estimation in an implicit quadratic measurement error model
... adjusted least squares estimator is derived that yields a consistent estimate of the parameters of an implicit quadratic measurement error ...consistent estimator for the measurement error ...
25
Comparison of fractional splines with polynomial splines; An Application on under-five year’s child mortality data in Pakistan (1960-2012)
... In nonlinear regression, Taylor Series Expansion has an important role to ...typical nonlinear regression estimation method is to expand the nonlinear regression equation in a Taylor series around ...
10
On the Equivalence of the Weighted Least Squares and the Generalised Least Squares Estimators, with Applications to Kernel Smoothing
... In this section we consider the inverse problem of reconstructing, if there exists, a covariance structure (i.e. some stochastic process) for which a given kernel estimator is BLUE. Hence, the starting point of ...
23
Adaptive Estimation of Heteroscedastic Money Demand Model of Pakistan
... In the presence of heteroscedasticity, ordinary least squares (OLS) does not result in biased and inconsistent parameter estimates. However, OLS estimates are no longer best linear unbiased estimators ...
7
Two stage weighted least squares estimator of the conditional mean of observation driven time series models
... We simulated N = 1000 independent replications of length n = 500 and n = 2000 of INARCH(q) models, and compared the finite-sample performance of the following estimators: the PQMLE (1.7), the NBQMLE (1.8) with r=1, the ...
44
NLINLS: a Differential Evolution based nonlinear least squares Fortran 77 program
... Introduction: Curve fitting or estimation by nonlinear least squares is a difficult task. There are two types of algorithm that are often used for this purpose: those that need evaluation of ...
14