linear least-squares analysis
Finite-Sample Analysis of Least-Squares Policy Iteration
... is linear, and 2) the distribution used to evaluate the performance is the stationary distribution of the Markov chain induced by the policy, and obtain a better bound both in terms of 1) estimation error, a rate ...
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Error Source Analysis of Target Localization Based on Weighted Linear Least Squares in Wireless Acoustic Sensor Network
... Target localization algorithm based on weighted least squares is widely used in energy-constrained wireless acoustic sensor networks. This paper analyzes the main error sources that affect the positioning ...
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A predictor corrector iterative method for solving linear least squares problems and perturbation error analysis
... predictor-corrector iterative method of convergence order p = 45 requiring 10 matrix by matrix multiplications per iteration is proposed for computing the Moore–Penrose inverse of a nonzero matrix of rank = r. ...
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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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Teaching Least Squares in Matrix Notation
... The least squares method, a fundamental piece of knowledge for students of all scientific tracks, is often introduced considering the simple linear regression with only two parameters to be ...of ...
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Partially linear models
... partially linear regression smoothing in ...partially linear regression ...partially linear regression techniques to various statistical ...include least squares regression, ...
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No penalty no tears : least squares in high dimensional linear models
... on least-squares type of fitting and hard thresholding, based on a high-dimensional generalization of ...dimensional linear model and recover its ...generalized linear models and models for ...
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The Equivalence of the Maximum Likelihood and a Modified Least Squares for a Case of Generalized Linear Model
... the analysis of statistical data, one of the most important steps is the estimation of the considered parameters ...not linear, we make use of iterative methods (Newton-Raphson algorithm, Fisher, etc ...not ...
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Classification of Hungarian medieval silver coins using x-ray fluorescent spectroscopy and multivariate data analysis
... component analysis, linear discriminant analysis, partial least squares discriminant analysis, classification and regression trees and multivariate curve resolution with ...
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The Additional Dynamics of the Least Squares Completions of Linear Differential Algebraic Equations
... of λ might require a technical analysis based on the structure of the specific problem, area of integration and performance goals. Also, Theorem 10 was proved using Gron- wall and some related inequalities, which ...
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A Derivative-free Algorithm for Finding Least Squares Solutions of Quasi-linear and Linear Systems
... of analysis of variance ( ANOVA ) [ 4 ] , [ 11 ] , [ 14 ] , but to facilitate analy- sis of results, we construct confidence intervals for estimates of proportions and present them ...
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Analysis of SOSTTC-OFDM based on Least Squares Method
... a least squares based channel estimation technique to SOSTTC-OFDM in a quasi-static frequency selective fading ...performance analysis of the SOSTTC-OFDM, with imperfect channel estimation by us- ing ...
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Least squares estimation of a shift in linear processes
... the least squares method (LS) to the estimation of a shift ...The least squares procedure also allows a broader specification of correlation structure in the data than MLE can typically ...a ...
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Combined l1 and Greedy l0 Penalized Least Squares for Linear Model Selection
... of linear segments of the LARS regularization path is typically O(n), so LARS execution requires O(np min(n, p)) flops, see Rosset and Zhu (2007) and B¨ uhlmann and van de Geer (2011), chapter ...
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An Algorithm for Non Linear Data Fit by the Least Squares Method EUR 4959
... 16/11/49 DATE 72353 FORTRAN IV G LEVEL MINIMI SUBROUTINE MINIMI FUN,FM,X,G,H,N,M,IRIT,EPSF,EPSX,EPSG,I MAX,NIT, MINI 0001 liNVERT mm C MINI c MINI .MINI c MINI PROGRAM CALCULATES MINIMUM[r] ...
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Role of benchmark technology in sustainable value analysis : an application to Finnish dairy farms
... empirical analysis reveal the critical role of parametric functional form assumptions on one hand, and the importance of accounting for stochastic noise on the ...
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The Dual of the Least Squares Method
... the least-squares estimates of a linear system’s parameters were thought to have been discovered, there surfaced an intriguing question: What is the dual specification of the ...
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Approximate Least Squares Accelerator
... Gillani et al. [4] improved the ARC framework described above by introducing adaptive statistical ap- proximation model (ASAM). In addition to the original three parameters of SAM, namely error mean (EM), error ...
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Quantity aggregation and quality price adjustment : problems with measuring Indonesian food demand : a thesis presented in partial fulfilment of the requirements for the degree of Masters in Agricultural Economics at Massey University
... Ordinary Least Squares Estimation Model: VLNV Dependent variable: VLNV Analysis of Variance.. Sum of Squares.[r] ...
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On the Equivalence of the Weighted Least Squares and the Generalised Least Squares Estimators, with Applications to Kernel Smoothing
... Up to now we have proved that if theorem 1 holds, then HΣK and ΣKH share p eigenvectors (the same of ΣK that are linear combinations of the columns of X) associated to equal eigenvalues. If we show that HΣK and ...
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