Practical application of non-linear least squares
Communication efficient distributed weighted non linear least squares estimation
... weighted non-linear least squares problems in multi-agent ...Furthermore, non-linear models arise frequently in such systems, ...
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An Algorithm for Non Linear Data Fit by the Least Squares Method EUR 4959
... In this work the least squares estimation of the parameters of a non linear curve is accomplished by using the Taylor's series of the summed squares of the residues (Φ) and retaining a[r] ...
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Substitution elasticities in a CES production framework : an empirical analysis on the basis of Non-Linear least squares estimations
... of non-linear least squares estimation ...standard linear estimations using Kmenta approximations, non-linear estimation techniques perform significantly ...
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Mixed discrete least squares meshless method for solving the linear and non-linear propagation problems
... the least squares functional with respect to the nodal ...The least squares functional was dened as the sum of the squared residuals of the dierential equation and its boundary ...
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Bounded perturbation regularization for linear least squares estimation
... Bounded perturbation regularization (BPR) was introduced in this paper. In BPR, the linear model matrix is perturbed using a matrix with a bounded norm. Based on the perturbed model, a min-max formulation was ...
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The state-of-the-art of preconditioners for sparse linear least-squares problems
... sparse linear least-squares ...preconditioning least-squares problems is hard and that at present there is no single approach that works well for all problems; we thus conclude that ...
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Application of dynamic partial least squares to complex processes
... 6 variable correlations. Monitoring schemes for steady state process can be developed based on PCA and PLS and although these approaches show superior performance compared to traditional SPC methods in terms of ...
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Least-squares linear estimation of signals from observations with Markovian delays
... most practical cases, such as mobile communications or exploration seismology, the delay is random and can be modeled by a stochastic ...The practical application of systems affected by delays has ...
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Coordinate and subspace optimization methods for linear least squares with non-quadratic regularization
... 2.4. Asymptotic convergence rate Now that we have established the convergence of the PCD algorithm, it is worthwhile exploring the rate of con- vergence. In some cases, e.g. denoising, where a good-enough solution is ...
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Linear least squares localization in sensor networks
... both non-hybrid time-of-arrival (TOA) and hybrid TOA/received signal-strength (RSS) ...both non-hybrid and hybrid networks using their respective reference selection ...both non-hybrid and hybrid ...
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Least squares estimation of a shift in linear processes
... experiments show that the order estimation based on ˆ X t yields almost identical results as those based on X t , thus providing a practical solution to the problem reported by MacNeill and Duong (1984). This two ...
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On the generalization of linear least mean squares estimation to quantum systems with non-commutative outputs
... realizable least mean squares estimators was impossible when B = or we should consider some constraints on the matrix P which makes the problem hard and sometimes impossible to ...realizable least ...
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APPLICATION OF RESTRICTED LEAST SQUARES TO ECONOMETRIC DATA
... of non-spherical disturbances for the estimation and testing procedures under the spherical disturbance setting, that is, the procedures become invalid and can give rise to misleading ...restricted least ...
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A superfast method for solving Toeplitz linear least squares problems
... The problem with many fast and superfast algorithms is that their numerical per- formance heavily depends on the condition of certain submatrices. They even may breakdown if some of these submatrices are singular. One of ...
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No penalty no tears : least squares in high dimensional linear models
... sparse linear mod- els by reconsidering OLS and answering the following simple question: Can ordinary least squares consistently fit these models with some suitable algorithms? Our result provides an ...
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A Regularized Interior-Point Method for Constrained Linear Least Squares
... ∑ m i=1 f i (x) 2 subject to Ax = b, x ≥ 0, (6.3) where each function f i : R n → R is twice continuously differentiable. Numerical difficulties can arise when the matrix A and/or the Jacobian of F : R n → R m , F (x) := ...
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Numerical investigations of linear least squares methods for derivative estimation
... smallest non-zero singular value of the least squares matrix in the denominator of the error ...the least squares matrix considered by Belward, Turner and Ilic ...
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A Derivative-free Algorithm for Finding Least Squares Solutions of Quasi-linear and Linear Systems
... implementation and, together with simple and well defined computation of LS solution, re- duces numerical requirements. All these advantages have been gained at a rel- atively low cost that search directions are re- ...
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The Application of Partial Least Squares Method in Hedonic Modelling
... partial least squares regression might be an alternative to OLS/WLS methods of hedonic models estimation in cases of multicollinearity, especially when the deletion of correlated variables is problematic ...
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The Additional Dynamics of the Least Squares Completions of Linear Differential Algebraic Equations
... Chapter 7 Conclusions and Future Research 7.1 Conclusion Completing a DAE to an ODE has been a major approach for numerically solving DAEs for twenty years. This is especially advantageous for unstructured higher index ...
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