weighted least squares
NLOS Identification and Weighted Least-Squares Localization for UWB Systems Using Multipath Channel Statistics
... The objectives of this paper are three-fold. First, to model and characterize the amplitude and delay statistics of IEEE 802.15.4a channels. Second, to propose NLOS identification techniques based on the amplitude and ...
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Derivative Estimation Based on Difference Sequence via Locally Weighted Least Squares Regression
... A new method is proposed for estimating derivatives of a nonparametric regression function. By applying Taylor expansion technique to a derived symmetric difference sequence, we obtain a sequence of approximate linear ...
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Asymptotic Properties of the Weighted Least Squares Estimator Under Moments Restriction
... 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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An extension of RSS-based model comparison tests for weighted least squares
... The weighted least squares problem addressed in the above discussion arises from an observation process in which the errors are assumed to be independent but are not necessarily identically ...
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Asymptotic properties of weighted least squares estimation in weak parma models
... of least squares estimation for invertible and causal weak PARMA ...ordinary least squares (OLS), weighted least squares (WLS) for an arbitrary vector of weights, ...
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Particle Swarm Optimization and Differentiation Evolution –Based Weighted Least Squares State Estimation
... the Weighted Least Squares (WLS) State Estimation ...based Weighted Least Squares State Estimation Problem for IEEE 14 and IEEE 30 bus ...
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On the Equivalence of the Weighted Least Squares and the Generalised Least Squares Estimators, with Applications to Kernel Smoothing
... The Henderson filter (see Henderson, 1916, Kenny and Durbin, 1982, Loader, 1999, Ladiray and Quenneville, 2001) arises as the weighted least squares estimator of a local cubic trend at time t using ...
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Robust adaptive filtering using recursive weighted least squares with combined scale and variable forgetting factors
... In this article, we design a new robust adaptive finite impulse response (FIR) system for dealing with these problems simultaneously. To alleviate the effects of non- stationary and impulsive noise, this algorithm ...
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Two stage weighted least squares estimator of the conditional mean of observation driven time series models
... Important classes of count time series models, in particular the Poisson INteger GARCH INGARCH, the Negative Binomial INGARCH and the INteger AR INAR, that will be considered in Section [r] ...
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A weighted least squares solution for space intersection of spaceborne stereo SAR data
... The previous work on shuttle image radar (SIR-B) data and European remote sensing satellite (ERS-1) data at University College London (UCL), London, U.K., suggests that the stereo- scopic SAR approach is a promising tool ...
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Standard and proportional error model comparison for logistic growth of green algae (Raphidocelis subcapiala)
... A more general methodology motivated by the weighted least squares (as we have presented it in [2]) involves the so-called Generalized Least Squares (GLS) estimator.. Standard residuals [r] ...
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Robust Regression Analysis with LR Type Fuzzy Input Variables and Fuzzy Output Variable
... the Weighted Least Squares (WLS) ...the Least Median Squares and the Weighted Least Squares (LMS-WLS) approach, we give steps of the LMS-WLS estimation procedure ...
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Teaching Least Squares in Matrix Notation
... teaching least squares at the undergraduate level in matrix notation is ...The weighted least squares equations are first derived in matrix form; equivalence with the standard results ...
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Analysis and interpretation of forest fertilizer experiments
... FURTHER ASPECTS OF ANALYSIS OF FERTILIZER EXPERIMENTS ASSUMPTIONS IN ANALYSIS OF VARIANCE Introduction Underlying assumptions WEIGHTED LEAST-SQUARES ESTIMATION Weighted least-squ'ares es[r] ...
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Empirical likelihood based inference in Poisson autoregressive model with conditional moment restrictions
... The EL as an alternative to the bootstrap for constructing confidence regions was intro- duced by Owen [, ]. The method defines an EL ratio function to construct confidence regions. Important features of the empirical ...
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Approximation Effects Due to Diffuse Derivatives from Polynomial Basis
... In order to approximate multidimensional surfaces, we introduced the MLS approximation. In the MLS method, the approximation of a function is obtained by the solution of many small linear systems, rather than solving a ...
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Least-Squares Policy Iteration
... The LSTDQ algorithm in its simplest form involves the inversion of A e for solving the linear system. However, A e will not be full rank until a sufficient number of samples has been processed. One way to avoid such ...
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Floating point error analysis of recursive least squares and least means squares adaptive filters
... This sequence is a zero mean white independent random process which has a variance related to signal statistics, the weight vector covariance, and the floating point errorso The calculat[r] ...
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The Dual of the Least Squares Method
... the least-squares me- thod (for linear systems) which results as a consequence of the analysis that the mean observational error will fall within certain given ...the least-squares ...
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On weighted structured total least squares
... Abstract. In this contribution we extend our previous results on the structured total least squares problem to the case of weighted cost func- tions. It is shown that the computational complexity of ...
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