The Least Squares Estimator
THE LEAST SQUARES ESTIMATOR Q
... by least squares as a purely algebraic ...detail least squares as an estimator of the model parameters of the linear regression model (defined in Table ...use least ...
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Least Squares Estimator for Vasicek Model Driven by Fractional Levy Processes
... construct least squares estimator for drift parameters based on time‐continuous observations, the consistency and asymptotic distribution of these estimators are studied in the non‐ergodic ...
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Moment convergence of regularized least-squares estimator for linear regression model
... larized least-squares estimator for the linear regression ...of estimator-dependent statis- tics, such as the mean squared prediction error and the bias correction for AIC-type information ...
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Asymptotic Properties of the Weighted Least Squares Estimator Under Moments Restriction
... 1 Introduction The aim of this work is to review the paper by Hellerstein & Imbens (1982) focusing on the use of auxiliary data and a formal derivation of the asymptotic properties of the underlying Weighted Least ...
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A Robust Collaborative Recommendation Algorithm Based on Least Median Squares Estimator
... on least median squares ...the least median squares estimator (LMedS-estimator) of robust statistics, which can reduce the increment of target item’s feature vector caused by ...
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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 selection was done ...
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Consistency of the structured total least squares estimator in a multivariate errors in variables model
... total least squares estimator, defined via a constrained optimization problem, is a generalization of the total least squares estimator when the data matrix and the applied ...
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A globally consistent nonlinear least squares estimator for identification of nonlinear rational systems
... nonlinear least squares estimator for the unknown para- meters of nonlinear rational systems has been developed via a standard two-step estimator in the ...NLS estimator consists of two ...
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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 ...
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Using wavelets to obtain a consistent ordinary least squares estimator of the long memory parameter
... ordinary least squares estimator of the long-memory parameter from a fractionally integrated process that is an alternative to the Geweke and Porter-Hudak (1983) ...ordinary least ...
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A simulation study of the robustness of the least median of squares estimator of slope in a regression through the origin model
... the least median of squares estimator of the slope of a regression line through the ...origin. Least median of squares estimation was initially proposed as hopefully being more robust ...
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The multivariate least trimmed squares estimator.
... On the other hand, the MMIM improves the MSE of the initial MLTS in case of normal or exponential carriers, and can even be better than RMLTS, but it is much worse for the Cau[r] ...
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On the computation of the structured total least squares estimator
... We consider numerical methods for the solution of the optimization problem (5). One approach is to use standard algorithms for local optimization. The choice of the optimization method is inspired by the need to use as ...
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Test for parameter change in stochastic processes based on conditional least-squares estimator
... of squares test produces less size distortions than our ...of squares test perfectly taking into consideration the size distortion occurring when n o1000; but the gap was still enormous even at n ¼ 1000: In ...
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Iterative Least Squares Estimator of Binary Choice Models: a Semi Parametric Approach
... The method is based on a semi-parametric interpreta- tion of the Expectation and Maximization (EM) principle (Dempster et al, 1977) and the least squares approach. By using the least squ[r] ...
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The Maximum Likelihood Stage Least Squares Estimator in the Nonlinear Simultaneous Equations Model
... Research supported by NSF Grant DCR 70_03L56 A04 to the National Bureau of Economic Research, Inc... The consistency and the asymptotic normality of the maximum likelihood estintor in th[r] ...
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A maximum entropy-least squares estimator for elastic origin-destination trip matrix estimation
... ME-LS estimator is the objective ...the estimator, which results in a relatively simple LS term in the objective ...the estimator is heavily dependent on the weighting coefficient ݓ in the objective ...
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Two stage weighted least squares estimator of the conditional mean of observation driven time series models
... weighted least square estimators (WLSEs), which enjoy the same consis- tency property as the QMLEs when the conditional distribution is misspecified, but have simpler asymptotic distributions when components of θ ...
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Distribution Theory of the Least Squares Averaging Estimator
... the least squares estimator, the proposed averaging method can be easily extended to the generalized least squares ...of least squares averaging estimators and to study ...
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Least Squares Estimation
... T 2 = X ˆβ 0 − X ˆβ 2 /r ˆσ 2 , (23) where ˆ β 0 is the least squares estimator under H 0 : Aβ = 0. In the numerical example, this statistic takes the form given in (18). When the noise is normally ...
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