Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 1689 for “"Least-Squares"”.
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Least squares mixture decomposition estimation
The Least Squares Mixture Decomposition Estimator (LSMDE) is a new nonparametric density estimation technique developed by modifying the ordinary kernel density estimators. While the ordinary kernel density estimator assumes equal weight (l/<i>n</i>) for each data point, LSMDE assigns the optimized …
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A Geometric Approach to Least Squares
A routine problem is to best fit a line to planar data. The goal of this thesis is to view linear regression in a geometric light. Lines in the plane can be identified with points on the cylinder, and fitting a line to a data set can be defined by mapping from that cylinder to R. The best-fit line …
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Completely Recursive Least Squares and Its Applications
<p>The matrix-inversion-lemma based recursive least squares (RLS) approach is of a recursive form and free of matrix inversion, and has excellent performance regarding computation and memory in solving the classic least-squares (LS) problem. It is important to generalize RLS for generalized LS …
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Distributed Singular Value Decomposition Through Least Squares
… by implementing parallelized alternating least squares and prove theoretical guarantees for its convergence and empirical results, which allow for the development of a simple framework for solving SVD in a correct, scalable, and easily optimizable manner.
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Methods of non-linear least squares estimation
… of the published techniques for determining the least squares estimates of the parameters in non-linear statistical models. It is first briefly indicated why non-linear models require iterative numerical estimating procedures, unlike the straightforward algebraic estimation possibly in the linear …
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A Comparison Of Ordinary Least Squares, Weighted Least Squares, And Other Procedures When Testing For The Equality Of Regression
… equality of regression slopes based on ordinary least squares (OLS) estimation, extant research has shown that the standard F performs poorly when the critical assumption of homoscedasticity is violated, resulting in increased Type I error rates and reduced statistical power (Box, 1954; DeShon & …
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Iterative least squares algorithms for digital filter design.
… These algorithms are based on the Iterative Least Squares (ILS) approach. We first review the various Iterative Reweighted Least Squares (IRLS) methods used to design Chebyshev and $L\sb{p}$ linear phase FIR filters. Then we focus on the ILS design of IIR filters and filter banks. For the …
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Accelerated Least Squares Network Adjustments and Graph Decompositions
… this project by reviewing the standard method, least squares adjustment, and several variants each of which is purported to be faster than the standard method. We apply asymptotic analysis to each of these methods and compare them to determine which method should be considered further. The …
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Incomplete factorization preconditioning for linear least squares problems
… iterative methods applied to large sparse linear least squares problems, $min\Vert Ax-b\Vert\sb2$, is proposed. The family is based on incomplete Gram-Schmidt (IGS) factorizations of A. Particular attention has been given to the following members of the family: Incomplete Classical Gram-Schmidt …
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The construction of a partial least squares biplot
… data. Another possible employment is in Partial Least Squares (PLS). First introduced as a regression method, PLS is more flexible than multivariate regression, but better suited than Principal Component Regression (PCR) for the prediction of a set of response variables from a large set of …
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Parallel multigrid for large-scale least squares sensitivity
… approach deals with the recently developed Least Squares Sensitivity (LSS) method. A multigrid algorithm is developed that can, in parallel, solve the discrete LSS system. This generic algorithm can be applied to ordinary differential equations such as the Lorenz System. Additionally, this …
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Some aspects of discrete least squares polynomial approximation
… was to investigate the method of discrete least squares polynomial approximation and to provide an extension of the method which would allow for a reasonable data-fit while lowering the number of undetermined coefficients. Also, computer programs were to be provided so that one may use the …
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A Least Squares Closure Approximation for Liquid Crystalline Polymers
… A new closure scheme is devised based on a least squares fit of a linear combination of the Doi, Tsuji-Rey, Hinch-Leal I, and Hinch-Leal II closure schemes. The orientation tensor and rate-of-strain tensor are fit separately using data generated from the kinetic solution of the Smoluchowski …
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Conservation and efficiency in least squares finite element methods
… conservation is often sought after. However, least-squares finite element methods are known to be not mass conserving when solving fluid flow problems. In this dissertation we develop mass conservative least-squares finite element methods for the Stokes and Navier-Stokes equations through the …
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Optimizing Local Least Squares Regression for Short Term Wind Prediction
… would makes it ideal for industry use. Local Least Squares Regression satisfies these constraints by using a predetermined time window over which a model can be trained, then at each time step trains a new model to predict wind speed values which could subsequently be transmitted to utilities …
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Least-Squares Approximation and Polyphase Decomposition for Pipelining Recursive filters
… exact frequency response and instead considers a least-squares formulation in conjunction with the pipelined architecture. The benefit of this design is that it reduces the complexity of the pipelined circuit immensely, while enabling a simple pipelined architecture based on a polyphase …
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Sequentially-fit alternating least squares algorithms in nonnegative matrix factorization
… matrix factorization (NMF) and nonnegative least squares regression (NNLS regression) are widely used in the physical sciences; this thesis explores the often-overlooked origins of NMF in the psychometrics literature. Another method originating in psychometrics is sequentially-fit factor …
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Least Squares Shadowing for sensitivity analysis of chaotic dynamical systems
… modelisations. A recently developed method, Least Squares Shadowing or simply LSS, tackles this problem and proposes an alternative approach to compute the desired sensitivities. The results are very promising and this thesis is intended to lay the mathematical foundations of this new …
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