linear least-squares estimator
Communication efficient distributed weighted non linear least squares estimation
... Consider a networked setup with a hypothetical fusion center which has access to the samples collected at all nodes at all times. In such a setting, in lieu of the sensing model as described in (1), one of the classical ...
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On the computation of the structured total least squares estimator
... Unlike the methods mentioned above, which solve the STLS problem in its original for- mulation (4), the proposed methods solve an equivalent optimization problem, derived by analytically minimizing (4) over p , for a xed ...
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Enhanced hybrid positioning in wireless networks II: AoA-RSS
... AoA-RSS linear least squares (LLS) location ...LLS estimator, a closed form expression for the mean square error (MSE) is also ...weighted linear least squares (WLLS) ...
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Restricted estimator in two seemingly unrelated regression model
... This article is concerned with the estimation problem of multicollinearity in two seemingly unrelated regression (SUR) equations with linear restrictions. We propose a restricted feasible SUR estimates of the ...
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From KIDSCREEN-10 to CHU9D: creating a unique mapping algorithm for application in economic evaluation
... ordinary least squares estimator has been the most widely adopted ...Tobit estimator which takes into account bounding is- sues ...censored least absolute deviations estimator ...
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No penalty no tears : least squares in high dimensional linear models
... lasso estimator is biased, we also consider two variations of it by combining lasso with Stage 2 and 3 of our LAT and RAT algorithms, denoted as lasLAT (las1 in Figures) and lasRAT (las2 in Figures) ...
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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 Combination Method for Averaging OLS and GLS Estimators
... pretest estimator that has inferior properties, and its use can be harmful (see Danilov and Magnus, ...ordinary least-squares (OLS) estimator for linear regression models with ...
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The large deviation for the least squares estimator of nonlinear regression model based on WOD errors
... to the martingale difference, the ϕ-mixing sequence, the NA sequence, and the weakly stationary linear process. Recently, Yang and Hu [] obtained some large deviation re- sults based on ρ-mixing, asymptotically ...
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Linear least squares localization in sensor networks
... noise ratios (SNRs) and/or large signal bandwidths [9]. If the variance information about measured range is unavail- able, a nonlinear least squares (NLS) estimator can be adopted, which assumes all ...
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The consistency for estimator of nonparametric regression model based on NOD errors
... the least squares estimator of b and the nonparametric estimator of g(t) based on NA samples, Hu [12] obtained the consistency and complete consistency for these esti- mations based on the ...
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Least squares estimation of a shift in linear processes
... The least squares (LS) estimation of a shift is not new. Hawkins (1986) examined the LS method for a shift in an i.i.d. sequence. He proved that T 1/2−δ (ˆ τ − τ ) → 0 in probability for any δ > 0, where ...
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Strong consistency of estimators in partially linear models for longitudinal data with mixing dependent structure
... partially linear regression models for longitudinal data based on mixing error ’ s structure, the least squares estimator of parametric component b and the estimator of nonparametric ...
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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 ...
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Examining the Conditions that will strengthen the Success of the Iterative Stein-Rule Estimator of The Disturbance Variance in Proxy Model
... a linear regression model with proxy variables, when is the difference of variances of two estimators getting closer to each ...Stein-rule estimator (ISRE) of the disturbance variance which is obtained by ...
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Kernel methods for short-term spatio-temporal wind prediction
... popular linear al- gorithms are discussed, the least mean squares (LMS) and recursive least squares (RLS) algorithms, are presented in their conventional linear and kernelised ...
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Least Squares Solutions of Inconsistent Fuzzy Linear Matrix Equations
... fuzzy least squares solutions for this equation are fuzzy vector solu- ...fuzzy linear system equation. Then the fuzzy least squares solution is an approximation solutions for ...
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Combined state and parameter estimation for Hammerstein systems with time-delay using the Kalman filtering
... dynamic linear and nonlinear systems [20, 21, 22] and play an important part in system identification [23] and signal filtering [24, ...the least squares principle [27]; Xie and Yang derived a ...
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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 ...
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Hybrid Levenberg–Marquardt and weak-constraint ensemble Kalman smoother method
... a linear least-squares solver in ...dense linear algebra li- braries can be used; however, in high-dimensional systems or for a large lag, the storage requirements can be prohibitive ...the ...
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