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1
Jones, Caroline Erin. "Least squares Gaussian quadrature." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape9/PQDD_0017/MQ54628.pdf.
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Hassel, Per Anker. "Nonlinear partial least squares." Thesis, University of Newcastle Upon Tyne, 2003. http://hdl.handle.net/10443/465.
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Partial Least Squares (PLS) has been shown to be a versatile regression technique with an increasing number of applications in the areas of process control, process monitoring and process analysis. This Thesis considers the area of nonlinear PLS; a nonlinear projection based regression technique. The nonlinearity is introduced as a univariate nonlinear function between projections, or to be more specific, linear combinations of the predictor and the response variables. As for the linear case, the method should handle multicollinearity, underdetermined and noisy systems. Although linear PLS is accepted as an empirical regression method, none of the published nonlinear PLS algorithms have achieved widespread acceptance. This is confirmed from a literature survey where few real applications of the methodology were found. This Thesis investigates two nonlinear PLS methodologies, in particular focusing on their limitations. Based on these studies, two nonlinear PLS algorithms are proposed. In the first of the two existing approaches investigated, the projections are updated by applying an optimization method to reduce the error of the nonlinear inner mapping. This ensures that the error introduced by the nonlinear inner mapping is minimized. However, the procedure is limited as a consequence of problems with the nonlinear optimisation. A new algorithm, Nested PLS (NPLS), is developed to address these issues. In particular, a separate inner PLS is used to update the projections. The NPLS algorithm is shown to outperform existing algorithms for a wide range of regression problems and has the potential to become a more widely accepted nonlinear PLS algorithm than those currently reported in the literature. In the second of the existing approaches, the projections are identified by examining each variable independently, as opposed to minimizing the error of the nonlinear inner mapping directly. Although the approach does not necessary identify the underlying functional relationship, the problems of overfitting and other problems associated with optimization are reduced. Since the underlying functional relationship may not be established accurately, the reliability of the nonlinear inner mapping will be reduced. To address this problem a new algorithm, the Reciprocal Variance PLS (RVPLS), is proposed. Compared with established methodology, RVPLS focus more on finding the underlying structure, thus reducing the difficulty of finding an appropriate inner mapping. RVPLS is shown to perform well for a number of applications, but does not have the wide-ranging performance of Nested PLS.
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Ganssle, Graham. "Stabilized Least Squares Migration." ScholarWorks@UNO, 2015. http://scholarworks.uno.edu/td/2074.
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Before raw seismic data records are interpretable by geologists, geophysicists must process these data using a technique called migration. Migration spatially repositions the acoustic energy in a seismic record to its correct location in the subsurface. Traditional migration techniques used a transpose approximation to a true acoustic propagation operator. Conventional least squares migration uses a true inverse operator, but is limited in functionality by the large size of modern seismic datasets. This research uses a new technique, called stabilized least squares migration, to correctly migrate seismic data records using a true inverse operator. Contrary to conventional least squares migration, this new technique allows for errors over ten percent in the underlying subsurface velocity model, which is a large limitation in conventional least squares migration. The stabilized least squares migration also decreases the number of iterations required by conventional least squares migration algorithms by an average of about three iterations on the sample data tested in this research.
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Young, William Ronald. "Total least squares and constrained least squares applied to frequency domain system identification." Ohio : Ohio University, 1993. http://www.ohiolink.edu/etd/view.cgi?ohiou1176315127.
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Guo, Hengdao. "Frequency Tracking and Phasor Estimation Using Least Squares and Total Least Squares Algorithms." UKnowledge, 2014. http://uknowledge.uky.edu/ece_etds/57.
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System stability plays an important role in electric power systems. With the development of electric power system, the scale of the electric grid is now becoming larger and larger, and many renewable energy resources are integrated in the grid. However, at the same time, the stability and safety issues of electric power system are becoming more complicated. Frequency and phasors are two critical parameters of the system stability. Obtaining these two parameters have been great challenges for decades. Researchers have provided various kinds of algorithms for frequency tracking and phasor estimation. Among them, Least Squares (LS) algorithm is one of the most commonly used algorithm. This thesis studies the LS algorithm and the Total Least Squares (TLS) algorithm working on frequency tracking and phasor estimation. In order to test the performance of the two algorithms, some simulations have been made in the Matlab. The Total Vector Error (TVE) is a commonly used performance criteria, and the TVE results of the two algorithms are compared. The TLS algorithm performs better than LS algorithm when the frequencies of all harmonic components are given.
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Santiago, Claudio Prata. "On the nonnegative least squares." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31768.
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Thesis (Ph.D)--Industrial and Systems Engineering, Georgia Institute of Technology, 2010.<br>Committee Chair: Earl Barnes; Committee Member: Arkadi Nemirovski; Committee Member: Faiz Al-Khayyal; Committee Member: Guillermo H. Goldsztein; Committee Member: Joel Sokol. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Müller, Werner. "On Least Squares Variogram Fitting." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 1997. http://epub.wu.ac.at/370/1/document.pdf.
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Yao, Gang. "Least-squares reverse-time migration." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/14575.
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Conventional migration methods, including reverse-time migration (RTM) have two weaknesses: first, they use the adjoint of forward-modelling operators, and second, they usually apply a crosscorrelation imaging condition to extract images from reconstructed wavefields. Adjoint operators, which are an approximation to inverse operators, can only correctly calculate traveltimes (phase), but not amplitudes. To preserve the true amplitudes of migration images, it is necessary to apply the inverse of the forward-modelling operator. Similarly, crosscorrelation imaging conditions also only correct traveltimes (phase) but do not preserve amplitudes. Besides, the examples show crosscorrelation imaging conditions produce strong sidelobes. Least-squares migration (LSM) uses both inverse operators and deconvolution imaging conditions. As a result, LSM resolves both problems in conventional migration methods and produces images with fewer artefacts, higher resolution and more accurate amplitudes. At the same time, RTM can accurately handle all dips, frequencies and any type of velocity variation. Combining RTM and LSM produces least-squares reverse-time migration (LSRTM), which in turn has all the advantages of RTM and LSM. In this thesis, we implement two types of LSRTM: matrix-based LSRTM (MLSRTM) and non-linear LSRTM (NLLSRTM). MLSRTM is a matrix formulation of LSRTM and is more stable than conventional LSRTM; it can be implemented with linear inversion algorithms but needs a large amount of computer memory. NLLSRTM, by contrast, directly expresses migration as an optimisation which minimises the 2 norm of the residual between the predicted and observed data. NLLSRTM can be implemented using non-linear gradient inversion algorithms, such as non-linear steepest descent and non-linear conjugated-gradient solvers. We demonstrate that both MLSRTM and NLLSRTM can achieve better images with fewer artefacts, higher resolution and more accurate amplitudes than RTM using three synthetic examples. The power of LSRTM is also further illustrated using a field dataset. Finally, a simple synthetic test demonstrates that the objective function used in LSRTM is sensitive to errors in the migration velocity. As a result, it may be possible to use NLLSRTM to both refine the migrated image and estimate the migration velocity.
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Kim, Donggeon. "Least squares mixture decomposition estimation." Diss., This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-02132009-171622/.
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Dugger, Katie M. "Foraging ecology and reproductive success of Least terns nesting on the lower Mississippi River /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9841279.
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Chu, Ka Lok 1975. "Inequalities and equalities associated with ordinary least squares and generalized least squares in partitioned linear models." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=85140.
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The motivation for this thesis is the paper by Paul L. Canner [The American Statistician, vol. 23, no. 5, pp. 39--40 (1969)] in which it was noted that in simple linear regression it is possible for the generalized least squares regression line to lie either entirely above or entirely below all of the observed data points.<br>Chapter I builds on the observation that in Canner's model the ordinary least squares and generalized least squares regression lines are parallel, which led us to introduce a new measure of efficiency of ordinary least squares and to find conditions for which the total Watson efficiency of ordinary least squares in a partitioned linear model exceeds or is less than the product of the two subset Watson efficiencies, i.e., the product of the Watson efficiencies associated with the two subsets of parameters in the underlying partitioned linear model.<br>We introduce the notions of generalized efficiency function, efficiency factorization multiplier, and determinantal covariance ratio, and obtain several inequalities and equalities. We give special attention to those partitioned linear models for which the total Watson efficiency of ordinary least squares equals the product of the two subset Watson efficiencies. A key characterization involves the equality between the squares of a certain partial correlation coefficient and its associated ordinary correlation coefficient.<br>In Chapters II and IV we suppose that the underlying partitioned linear model is weakly singular in that the column space of the model matrix is contained in the column space of the covariance matrix of the errors in the linear model. In Chapter III our results are specialized to partitioned linear models where the partitioning is orthogonal and the covariance matrix of the errors is positive definite.
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Srar, Jalal Abdulsayed. "Adaptive antenna array beamforming using a concatenation of recursive least square and least mean square algorithms." Thesis, Curtin University, 2011. http://hdl.handle.net/20.500.11937/618.
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In recent years, adaptive or smart antennas have become a key component for various wireless applications, such as radar, sonar and cellular mobile communications including worldwide interoperability for microwave access (WiMAX). They lead to an increase in the detection range of radar and sonar systems, and the capacity of mobile radio communication systems. These antennas are used as spatial filters for receiving the desired signals coming from a specific direction or directions, while minimizing the reception of unwanted signals emanating from other directions.Because of its simplicity and robustness, the LMS algorithm has become one of the most popular adaptive signal processing techniques adopted in many applications, including antenna array beamforming. Over the last three decades, several improvements have been proposed to speed up the convergence of the LMS algorithm. These include the normalized-LMS (NLMS), variable-length LMS algorithm, transform domain algorithms, and more recently the constrained-stability LMS (CSLMS) algorithm and modified robust variable step size LMS (MRVSS) algorithm. Yet another approach for attempting to speed up the convergence of the LMS algorithm without having to sacrifice too much of its error floor performance, is through the use of a variable step size LMS (VSSLMS) algorithm. All the published VSSLMS algorithms make use of an initial large adaptation step size to speed up the convergence. Upon approaching the steady state, smaller step sizes are then introduced to decrease the level of adjustment, hence maintaining a lower error floor. This convergence improvement of the LMS algorithm increases its complexity from 2N in the case of LMS algorithm to 9N in the case of the MRVSS algorithm, where N is the number of array elements.An alternative to the LMS algorithm is the RLS algorithm. Although higher complexity is required for the RLS algorithm compared to the LMS algorithm, it can achieve faster convergence, thus, better performance compared to the LMS algorithm. There are also improvements that have been made to the RLS algorithm families to enhance tracking ability as well as stability. Examples are, the adaptive forgetting factor RLS algorithm (AFF-RLS), variable forgetting factor RLS (VFFRLS) and the extended recursive least squares (EX-KRLS) algorithm. The multiplication complexity of VFFRLS, AFF-RLS and EX-KRLS algorithms are 2.5N2 + 3N + 20 , 9N2 + 7N , and 15N3 + 7N2 + 2N + 4 respectively, while the RLS algorithm requires 2.5N2 + 3N .All the above well known algorithms require an accurate reference signal for their proper operation. In some cases, several additional operating parameters should be specified. For example, MRVSS needs twelve predefined parameters. As a result, its performance highly depends on the input signal.In this study, two adaptive beamforming algorithms have been proposed. They are called recursive least square - least mean square (RLMS) algorithm, and least mean square - least mean square (LLMS) algorithm. These algorithms have been proposed for meeting future beamforming requirements, such as very high convergence rate, robust to noise and flexible modes of operation. The RLMS algorithm makes use of two individual algorithm stages, based on the RLS and LMS algorithms, connected in tandem via an array image vector. On the other hand, the LLMS algorithm is a simpler version of the RLMS algorithm. It makes use of two LMS algorithm stages instead of the RLS – LMS combination as used in the RLMS algorithm.Unlike other adaptive beamforming algorithms, for both of these algorithms, the error signal of the second algorithm stage is fed back and combined with the error signal of the first algorithm stage to form an overall error signal for use update the tap weights of the first algorithm stage.Upon convergence, usually after few iterations, the proposed algorithms can be switched to the self-referencing mode. In this mode, the entire algorithm outputs are swapped, replacing their reference signals. In moving target applications, the array image vector, F, should also be updated to the new position. This scenario is also studied for both proposed algorithms. A simple and effective method for calculate the required array image vector is also proposed. Moreover, since the RLMS and the LLMS algorithms employ the array image vector in their operation, they can be used to generate fixed beams by pre-setting the values of the array image vector to the specified direction.The convergence of RLMS and LLMS algorithms is analyzed for two different operation modes; namely with external reference or self-referencing. Array image vector calculations, ranges of step sizes values for stable operation, fixed beam generation, and fixed-point arithmetic have also been studied in this thesis. All of these analyses have been confirmed by computer simulations for different signal conditions. Computer simulation results show that both proposed algorithms are superior in convergence performances to the algorithms, such as the CSLMS, MRVSS, LMS, VFFRLS and RLS algorithms, and are quite insensitive to variations in input SNR and the actual step size values used. Furthermore, RLMS and LLMS algorithms remain stable even when their reference signals are corrupted by additive white Gaussian noise (AWGN). In addition, they are robust when operating in the presence of Rayleigh fading. Finally, the fidelity of the signal at the output of the proposed algorithms beamformers is demonstrated by means of the resultant values of error vector magnitude (EVM), and scatter plots. It is also shown that, the implementation of an eight element uniform linear array using the proposed algorithms with a wordlength of nine bits is sufficient to achieve performance close to that provided by full precision.
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Kubitz, Jörg. "Gemischte Least-Squares-FEM für Elastoplastizität." [S.l.] : [s.n.], 2007. http://deposit.ddb.de/cgi-bin/dokserv?idn=983832625.
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Kolev, Tzanio Valentinov. "Least-squares methods for computational electromagnetics." Texas A&M University, 2004. http://hdl.handle.net/1969.1/1115.
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The modeling of electromagnetic phenomena described by the Maxwell's equations is of critical importance in many practical
applications. The numerical simulation of these equations is challenging and much more involved than initially believed. Consequently, many discretization techniques, most of them quite complicated, have been proposed.
In this dissertation, we present and analyze a new methodology for approximation of the time-harmonic Maxwell's equations. It is an extension of the negative-norm least-squares finite element approach which has been applied successfully to a variety of other problems.
The main advantages of our method are that it uses simple, piecewise polynomial, finite element spaces, while giving quasi-optimal approximation, even for solutions with low
regularity (such as the ones found in practical applications). The numerical solution can be efficiently computed using standard and well-known tools, such as iterative methods
and eigensolvers for symmetric and positive definite
systems (e.g. PCG and LOBPCG) and reconditioners for second-order problems (e.g. Multigrid).
Additionally, approximation of varying polynomial degrees is allowed and spurious eigenmodes are provably avoided.
We consider the following problems related to the Maxwell's equations in the frequency domain: the magnetostatic problem, the electrostatic problem, the eigenvalue problem and the full time-harmonic system. For each of these problems, we present a natural (very) weak
variational formulation assuming minimal regularity of the solution. In each case, we prove error estimates for the approximation
with two different discrete least-squares methods. We also show how to deal with problems posed on domains that are multiply connected or have multiple boundary components.
Besides the theoretical analysis of the methods, the dissertation provides various numerical results in two and three dimensions
that illustrate and support the theory.
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Baykal, Buyurman. "Underdetermined recursive least-squares adaptive filtering." Thesis, Imperial College London, 1995. http://hdl.handle.net/10044/1/7790.
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Fraley, Christina. "Solution of nonlinear least-squares problems /." Stanford, CA : Dept. of Computer Science, Stanford University, 1987. http://doi.library.cmu.edu/10.1184/OCLC/19613955.
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Thesis (Ph. D.)--Stanford University, 1987.<br>"June 1987." This research was supported in part by Joseph Oliger under Office of Naval Research contract N00014-82-K-0335, by Stanford Linear Accelerator Center and the Systems Optimization Laboratory under Army Research Office contract DAAG29-84-K-0156. Includes bibliographies.
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Silva, Aristeguieta Maria. "Optimization of seismic least-squares inversion /." Access abstract and link to full text, 1993. http://0-wwwlib.umi.com.library.utulsa.edu/dissertations/fullcit/9325432.
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Han, Qing 1980. "Solving constrained integer least squares problems." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=98720.
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The objective of this thesis is to design efficient algorithms for solving constrained integer least squares (ILS) problems. These problems may arise from many applications, such as communications and cryptography. In this thesis, we mainly consider two kinds of constrained ILS problems: box-constrained integer least squares (BILS) problem and ellipsoid-constrained integer least squares (EILS) problem.<br>Solving a constrained ILS problem usually has two stages: reduction (or preprocessing) and search. We first present a reduction algorithm and a search algorithm for solving the BILS problem. Unlike the usual reduction algorithms, which use only the information of the generator matrix, the new reduction algorithm also uses the information of the given input vector and the box constraint. The new search algorithm overcomes some shortcomings of the existing search algorithms and gives some other improvements. Then, for solving the EILS problem, we dynamically transfer it to a BILS problem and extend the above new search algorithm. In addition, we suggest using the well-known LLL reduction for preprocessing. For both problems, simulation results indicate the combination of our reduction algorithms and search algorithms can be (much) more efficient than the existing algorithms.
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Hawes, Anthony H. "Least squares and adaptive multirate filtering." Thesis, Monterey, California. Naval Postgraduate School, 2012.
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Approved for public release; distribution in unlimited.<br>This thesis addresses the problem of estimating a random process from two observed signals sampled at different rates. The case where the low-rate observation has a higher signal-to- noise ratio than the high-rate observation is addressed. Both adaptive and non-adaptive filtering techniques are explored. For the non-adaptive case, a multirate version of the Wiener-Hopf optimal filter is used for estimation. Three forms of the filter are described. It is shown that using both observations with this filter achieves a lower mean-squared error than using either sequence alone. Furthermore, the amount of training data to solve for the filter weights is comparable to that needed when using either sequence alone. For the adaptive case, a multirate version of the LMS adaptive algorithm is developed. Both narrowband and broadband interference are removed using the algorithm in an adaptive noise cancellation scheme. The ability to remove interference at the high rate using observations taken at the low rate without the high-rate observations is demonstrated.
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RENTERIA, RAUL PIERRE. "ALGORITHMS FOR PARTIAL LEAST SQUARES REGRESSION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2003. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=4362@1.
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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>Muitos problemas da área de aprendizagem automática tem por
objetivo modelar a complexa relação existente num
sisitema , entre variáveis de entrada X e de saída Y na
ausência de um modelo teórico. A regressão por mínimos
quadrados parciais PLS ( Partial Least Squares) constitui
um método linear para resolução deste tipo de
problema , voltado para o caso de um grande número de
variáveis de entrada quando comparado com número de
amostras. Nesta tese , apresentamos uma variante do
algoritmo clássico PLS para o tratamento de grandes
conjuntos de dados , mantendo um bom poder preditivo.
Dentre os principais resultados destacamos um versão
paralela PPLS (Parallel PLS ) exata para o caso de apenas
um variável de saída e um versão rápida e aproximada DPLS
(DIRECT PLS) para o caso de mais de uma variável de saída.
Por outro lado ,apresentamos também variantes para o
aumento da qualidade de predição graças à formulação não
linear. São elas o LPLS ( Lifted PLS ), algoritmo para o
caso de apenas uma variável de saída, baseado na teoria
de funções de núcleo ( kernel functions ), uma
formulação kernel para o DPLS e um algoritmo multi-kernel
MKPLS capaz de uma modelagemmais compacta e maior poder
preditivo, graças ao uso de vários núcleos na geração do
modelo.<br>The purpose of many problems in the machine learning
field isto model the complex relationship in a system
between the input X and output Y variables when no
theoretical model is available. The Partial Least Squares
(PLS)is one linear method for this kind of problem, for the
case of many input variables when compared to the number of
samples. In this thesis we present versions of the
classical PLS algorithm designed for large data sets while
keeping a good predictive power. Among the main results we
highlight PPLS (Parallel PLS), a parallel version for the
case of only one output variable, and DPLS ( Direct PLS), a
fast and approximate version, for the case fo more than one
output variable. On the other hand, we also present some
variants of the regression algorithm that can enhance the
predictive quality based on a non -linear formulation. We
indroduce LPLS (Lifted PLS), for the case of only one
dependent variable based on the theory of kernel functions,
KDPLS, a non-linear formulation for DPLS, and MKPLS, a
multi-kernel algorithm that can result in a more compact
model and a better prediction quality, thankas to the use
of several kernels for the model bulding.
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Bittau, A. "Toward least-privilege isolation for software." Thesis, University College London (University of London), 2009. http://discovery.ucl.ac.uk/18902/.
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Hackers leverage software vulnerabilities to disclose, tamper with, or destroy sensitive data. To protect sensitive data, programmers can adhere to the principle of least-privilege, which entails giving software the minimal privilege it needs to operate, which ensures that sensitive data is only available to software components on a strictly need-to-know basis. Unfortunately, applying this principle in practice is dif- cult, as current operating systems tend to provide coarse-grained mechanisms for limiting privilege. Thus, most applications today run with greater-than-necessary privileges. We propose sthreads, a set of operating system primitives that allows ne-grained isolation of software to approximate the least-privilege ideal. sthreads enforce a default-deny model, where software components have no privileges by default, so all privileges must be explicitly granted by the programmer. Experience introducing sthreads into previously monolithic applications|thus, partitioning them|reveals that enumerating privileges for sthreads is dicult in practice. To ease the introduction of sthreads into existing code, we include Crowbar, a tool that can be used to learn the privileges required by a compartment. We show that only a few changes are necessary to existing code in order to partition applications with sthreads, and that Crowbar can guide the programmer through these changes. We show that applying sthreads to applications successfully narrows the attack surface by reducing the amount of code that can access sensitive data. Finally, we show that applications using sthreads pay only a small performance overhead. We applied sthreads to a range of applications. Most notably, an SSL web server, where we show that sthreads are powerful enough to protect sensitive data even against a strong adversary that can act as a man-in-the-middle in the network, and also exploit most code in the web server; a threat model not addressed to date.
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Jennings, Anthony. "Economic problems of least developed countries." Thesis, University of Leicester, 1987. http://hdl.handle.net/2381/35499.
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The United Nations Conference on Least Developed Countries (UNCLDC) held in Paris in 1981, consolidated the category of least developed countries as a major issue at the international level. The creation of the category, and the theory and practice of least development are examined, and the results of the UNCLDC are assessed. Malawi is taken as a case study, to examine its response to the Substantial New Programme of Action, agreed to at the UNCLDC, and to analyse the extent to which the international community has fulfilled its commitment to substantially improve the volume and quality of assistance. The effects and causes of the recurrent cost problem in least developed countries arc analysed, at the micro and macro levels, and proposals made to ease this constraint. Attention is given to the scope of food aid to support recurrent costs, and a set of guidelines are proposed. At the UNCLDC it was suggested that very large projects (transformational investments) should be undertaken in least developed countries. The methodology for estimating the benefits of such projects is discussed, and a case study presented of the use of project appraisal and the multiplier in Malawi. A significant increase in aid was agreed as a key international support measure at the UNCLDC. As yet there is no systematic aid evaluation process. The results of an experiment arc presented, using a qualitatively based system of evaluation, which is then assessed across sets of data with quantitative summations, to measure aid effectiveness.
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Hawes, Anthony H. "Least squares and adaptive multirate filtering /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03sep%5FHawes.pdf.
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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, September 2003.<br>Thesis advisor(s): Charles W. Therrien, Roberto Cristi. Includes bibliographical references (p. 45). Also available online.
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Hazra, Rajeeb. "Constrained least-squares digital image restoration." W&M ScholarWorks, 1995. https://scholarworks.wm.edu/etd/1539623865.
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The design of a digital image restoration filter must address four concerns: the completeness of the underlying imaging system model, the validity of the restoration metric used to derive the filter, the computational efficiency of the algorithm for computing the filter values and the ability to apply the filter in the spatial domain. Consistent with these four concerns, this dissertation presents a constrained least-squares (CLS) restoration filter for digital image restoration. The CLS restoration filter is based on a comprehensive, continuous-input/discrete- processing/continuous-output (c/d/c) imaging system model that accounts for acquisition blur, spatial sampling, additive noise and imperfect image reconstruction. The c/d/c model-based CLS restoration filter can be applied rigorously and is easier to compute than the corresponding c/d/c model-based Wiener restoration filter. The CLS restoration filter can be efficiently implemented in the spatial domain as a small convolution kernel. Simulated restorations are used to illustrate the CLS filter's performance for a range of imaging conditions. Restoration studies based, in part, on an actual Forward Looking Infrared (FLIR) imaging system, show that the CLS restoration filter can be used for effective range reduction. The CLS restoration filter is also successfully tested on blurred and noisy radiometric images of the earth's outgoing radiation field from a satellite-borne scanning radiometer used by the National Aeronautics and Space Administration (NASA) for atmospheric research.
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Guo, Ronggang. "Systematical analysis of the transformation procedures in Baden-Württemberg with Least Squares and Total Least Squares methods." Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2007. http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-33293.
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Schwalbach, Monica J. "Conservation of least terns and piping plovers along the Missouri River and its major western tributaries in South Dakota." Connect to this title online, 1988. http://www.fs.fed.us/r2/nebraska/gpng/lt%5Fplover/ltppmissouri/LTPPMissouriSchwalbach1988b.pdf.
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Maquignon, Axel. "Testing for Predictability Methods of Least Autocorrelation /." St. Gallen, 2006. http://www.biblio.unisg.ch/org/biblio/edoc.nsf/wwwDisplayIdentifier/02914406001/$FILE/02914406001.pdf.
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Furrer, Marc. "Numerical Accuracy of Least Squares Monte Carlo." St. Gallen, 2008. http://www.biblio.unisg.ch/org/biblio/edoc.nsf/wwwDisplayIdentifier/01650217002/$FILE/01650217002.pdf.
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Petra, Stefania. "Semismooth least squares methods for complementarity problems." Doctoral thesis, [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=98174558X.
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Pei, Sun. "Noise Resistant Least Squares Based Adaptive Control." Thesis, KTH, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-92628.
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Kong, Seunghyun. "Linear programming algorithms using least-squares method." Diss., Available online, Georgia Institute of Technology, 2007, 2007. http://etd.gatech.edu/theses/available/etd-04012007-010244/.
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Thesis (Ph. D.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2007.<br>Martin Savelsbergh, Committee Member ; Joel Sokol, Committee Member ; Earl Barnes, Committee Co-Chair ; Ellis L. Johnson, Committee Chair ; Prasad Tetali, Committee Member.
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Titley-Péloquin, David. "Backward pertubation analysis of least squares problems." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=94973.
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This thesis is concerned with backward perturbation analyses of the linear least squares (LS) and related problems. Two theoretical measures are commonly used for assessing the backward errors that arise in the approximate solution of such problems. These are called the normwise relative backward error (NRBE) and the minimal backward error (MBE). An important new relationship between these two measures is presented, which shows that the two are essentially equivalent. New upper bounds on the NRBE and MBE for the LS problem are given and related to known bounds and estimates. One important use of backward perturbation analysis is to design stopping criteria for iterative methods. In this thesis, minimum-residual iterative methods for solving LS problems are studied. Unexpected convergence behaviour in these methods is explained and applied to show that commonly used stopping criteria can in some situations be much too conservative. More reliable stopping criteria are then proposed, along with an efficient implementation in the iterative algorithm LSQR. In many applications the data in the LS problem come from a statistical linear model in which the noise follows a multivariate normal distribution whose mean is zero and whose covariance matrix is the scaled identity matrix. A description is given of typical convergence of the error that arises in minimum-residual iterative methods when the data come from such a linear model. Stopping criteria that use the information from the linear model are then proposed and compared to others that appear in the literature. Finally, some of these ideas are extended to the scaled total least squares problem.<br>Nous effectuons une analyse de l'erreur rétrograde des problèmes de moindres carrés. Nous analysons deux méthodes habituellement utilisées pour mesurer l'erreur rétrograde et démontrons que celles-ci sont en fait équivalentes. Nous présentons de nouvelles estimations de l'erreur rétrograde des problèmes de moindres carrés, et nous les comparons aux estimations connues. L'un des usages de ce type d'analyse consiste à établir des critères d'arrêt pour les méthodes itératives. Nous expliquons des phénomènes de convergence inattendus que nous avons observés dans les méthodes itératives de type résidu minimal. Nous démontrons ensuite que les critères d'arrêt habituellement utilisés avec ces méthodes peuvent être trop prudents dans certaines circonstances. Nous proposons donc de nouveaux critères d'arrêt plus fiables, et présentons une implémentation efficace de ceux-ci dans l'algorithme LSQR. La méthode des moindres carrés est souvent utilisée en statistique lorsque les données proviennent d'un modèle linéaire et que le bruit est distribué selon une loi normale dont l'espérance est zéro et la variance est la matrice identité proportionnée. Nous décrivons la convergence de l'erreur qui résulte de ce type de données et proposons des critères d'arrêt adaptés à cette situation. Enfin, nous appliquons une partie de cette analyse aux problèmes de moindres carrés proportionnés.
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Breen, Stephen. "Integer least squares search and reduction strategies." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=106561.
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This thesis is concerned with integer least squares problems, also referred to as closest vector problems. One often used approach to solving these problems is the discrete search method, which typically involves two stages, the reduction and the search. The main purpose of the reduction is to make the search faster. Reduction strategies for box-constrained integer least squares problems involve column reordering of the input matrix. There are currently two algorithms for column reordering that are most effective for the search stage, referred to here as SW and CH. Although both use all available information in the problem, the SW and CH algorithms look different and were derived respectively from geometric and algebraic points of view. In this thesis we modify the SW algorithm to make it more computationally efficient and easier to comprehend. We then prove that the SW and CH algorithms actually give the same column reordering in theory. Finally, we propose a new mathematically equivalent algorithm, which is more computationally efficient and is still easy to understand. This thesis also extends the column permutation idea to ordinary integer least squares problems. A new reduction algorithm which combines the well-known Lenstra–Lenstra–Lovász (LLL) reduction and the new column reordering strategy is proposed. The new reduction can be much more effective than the LLL reduction in some cases. The thesis also reviews some common search algorithms. A new one is proposed, which is based on two previous algorithms, the depth-first search and the best-first search. This hybrid algorithm makes use of the advantages of both originals, is more efficient than either and is easier to implement than other previous hybrid algorithms.<br>Cette thèse s'intéresse aux problèmes de moindres carrés entiers (ILS), ou les problèmes du vecteur le plus proche. Une approche souvent utilisée pour résoudre ces problèmes est la méthode de recherche discrète, qui implique deux étapes: la réduction et la recherche. Le but principal de la réduction est de rendre l'étape de recherche plus rapide. Les stratégies de réduction des problèmes ILS sous contrainte de boîte impliquent la réorganisation de colonnes de la matrice de données. Il existe actuellement deux algorithmes pour la réorganisation des colonnes, appelés ici les algorithmes SW et CH, qui sont les plus efficaces pour la phase de recherche. Bien que les deux utilisent toutes les informations disponibles dans le problème, les algorithmes SW et CH sont différents en apparence, et ont été obtenus respectivement à partir d'une point de vue géométrique et algébrique de vue. Dans cette thèse, nous modifions l'algorithme SW pour rendre son calcul plus efficace et plus facile à comprendre. Nous démontrons ensuite qu'en théorie, les algorithmes SW et CH donne effectivement la même réorganisation de colonnes. Enfin, nous proposons un nouveau algorithme mathématiquement équivalent qui est plus efficace, tout en demeurant facile à comprendre. Cette thèse étend également l'idée de permutation de colonnes aux problèmes ordinaires de moindres carrés entiers. Un nouveau algorithme de réduction qui combine le célèbre agorithme Lenstra-Lenstra-Lovász (LLL) avec la nouvelle stratégie de réorganisation de colonnes est proposé. La nouvelle réduction peut être plus efficace que la réduction LLL dans certains cas.Cette thèse examine également certains algorithmes de recherche d'usage courant. Un nouveau est proposé qui est basé sur deux algorithmes précédents: l'algorithme de parcours en profondeur et celui de la recherche au meilleur d'abord. Notre algorithme hybride détient les avantages des deux originaux, tout en étant plus efficace et plus facile à utiliser que d'autres algorithmes hybrides déjà existants.
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Saadi, Kamel. "Efficient regularisation of least-squares kernel machines." Thesis, University of East Anglia, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.522281.
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Tuyishimire, Emmanuel. "Internet of Things: Least Interference Beaconing Algorithms." Thesis, University of Cape Town, 2014. http://pubs.cs.uct.ac.za/archive/00000997/.
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The emerging sensor networking applications are predicting the deployment of sensor devices in thousands of computing elements into multi-technology and multi-protocol platforms. Access to information will be available not only anytime and anywhere, but also using anything in a first-mile of the Internet referred to as the internet-of-things (IoT).
The management of such a large-scale and heterogeneous network, would benefit from some of the traditional IP-based network management techniques such as load and energy balancing, which can be re-factored to achieve efficient routing of sensor network traffic.
Research has shown that minimizing the path interference on nodes was necessary to improve traffic engineering in connection oriented networks. The same principle has been applied in past research in the context of the IoT to reveal that the least interference beaconing protocol (LIBP); a protocol derived
from the least interference beaconing algorithm (LIBA) outperforms the Collection Tree Protocol (CTP) and Tiny OS Beaconing (ToB) protocol, in terms of energy efficiency and lifetime of the sensor network. However for the purpose of efficiency and accuracy, it is relevant, useful and critical to revisit or re-examine the LIBA algorithm in terms of correctness and investigate potential avenues for improvement.
The main contributions of this research work are threefold. Firstly, we build upon formal methods to verify the correctness of the main principles underlying the LIBA, in terms of energy efficiency and interference minimization. The interference is here defined at each node by the number of routing paths carrying the sensor readings from the motes to the sink of the network that traverse the node. Our findings reveal the limitations in LIBA. Secondly, building upon these limitations, we propose two improvements to the algorithm: an algorithm called LIBA+ that improves the algorithm performance by keeping track of the energy usage of the sensor nodes, and a multi-sink version of the algorithm called LIBAMN that extends the algorithm to account for multiple sinks or gateways. These enhancements present preventive mechanisms to include in IoT platforms in order to improve traffic engineering, the security of network protocols and network stability. Lastly, we present analytical results, which reveal that the LIBA algorithm can be improved by more than 84% in terms of energy balancing. These results reveal that formal methods remain essential in the evaluation and performance improvement of wireless sensor network algorithms and protocols.
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Rogers, C. A. "Partial least squares (PLS) : a comparative assessment." Thesis, University of Bath, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.235583.
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Cho, Youngjae. "Least squares estimation of acoustic reflection coeffficient." Thesis, University of Southampton, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.420208.
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TORTURELA, ALEXANDRE DE MACEDO. "NOVEL SPARSE SYSTEMS LEAST SQUARES ESTIMATION METHODS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=26712@1.
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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>INSTITUTO MILITAR DE ENGENHARIA<br>CENTRO TECNOLÓGICO DO EXÉRCITO<br>INSTITUTO DE PESQUISA E DESENVOLVIMENTO<br>Neste trabalho, quatro métodos projetados especificamente para a estimação de sistemas esparsos são originalmente elaborados e apresentados.
São eles: Encolhimentos Sucessivos, Expansões Sucessivas, Minimização da
Norma l1 e Ajuste Automático do fator de regularização do Custo LS. Os
quatro métodos propostos baseiam-se na técnica de estimação de sistemas
lineares e invariantes no tempo pelo critério dos mínimos quadrados, universalmente
conhecida por sua denominação em inglês - Least Squares (LS)
Estimation, e incorporam técnicas relacionadas a otimização convexa e à
teoria de compressive sensing. Os resultados obtidos em simulações mostram
que os métodos em questão têm desempenho superior que a estimação LS
convencional e que o algoritmo Recursive Least Squares (RLS) com regularização convexa denominado l1-RLS, em muitos casos alcançando o desempenho
ótimo apresentado pelo método de estimação LS Oráculo, no qual
o suporte da resposta ao impulso em tempo discreto do sistema estimado
é conhecido a priori. Além disso, os métodos propostos apresentam custo
computacional menor que do algoritmo l1-RLS.<br>In this thesis, four methods specifically designed for sparse systems
estimation are originally developed and presented, which were called here:
Relaxations method, Successive Expansions method, l1-norm Minimization
method and Automatic Adjustment of the Regularization Factor method.
The four proposed methods are based on the Least Squares (LS) Estimation
method and incorporate techniques related to convex optimization and to
the theory of compressive sensing. The simulation results show that the
proposed methods herein present superior performance than the ordinary
LS estimation method and the Recursive Least Squares (RLS) with convex
regularization method (l1-RLS), in many cases achieving the same optimal
performance presented by the LS Oracle method. Furthermore, the proposed
methods demand lower computational cost than the l1-RLS method.
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Ynosencio, Lucille D. (Lucille Diane). "Flow Garden : on paths of least resistance." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/49733.
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Thesis (M. Arch.)--Massachusetts Institute of Technology, Dept. of Architecture, 2009.<br>Includes bibliographical references (p. 64).<br>Flow Garden is a proposal for a park architecture in which building becomes pathway. The project conceives of the building as an instrument within its larger urban context which has the capacity to strengthen and unify fragmented public space. The project does so by collecting and articulating all possible wanderings within a public park strategically located adjacent to Downtown Orlando, Florida. The strategy employed in Flow Garden is an inversion of the architectural strategies of its many neighboring theme parks which set a precedent for the building to operate both as an obstacle and as a spectacle within its broader urban context. This thesis represents a search for an architecture in which the building, rather than being a monumental rupture in the public realm, is instead, a formula for its completion.<br>by Lucille D. Ynosencio.<br>M.Arch.
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Bian, Xiaomeng. "Completely Recursive Least Squares and Its Applications." ScholarWorks@UNO, 2012. http://scholarworks.uno.edu/td/1518.
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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 (GLS) problem. It is also of value to develop an efficient initialization for any RLS algorithm.
In Chapter 2, we develop a unified RLS procedure to solve the unconstrained/linear-equality (LE) constrained GLS. We also show that the LE constraint is in essence a set of special error-free observations and further consider the GLS with implicit LE constraint in observations (ILE-constrained GLS).
Chapter 3 treats the RLS initialization-related issues, including rank check, a convenient method to compute the involved matrix inverse/pseudoinverse, and resolution of underdetermined systems. Based on auxiliary-observations, the RLS recursion can start from the first real observation and possible LE constraints are also imposed recursively. The rank of the system is checked implicitly. If the rank is deficient, a set of refined non-redundant observations is determined alternatively.
In Chapter 4, base on [Li07], we show that the linear minimum mean square error (LMMSE) estimator, as well as the optimal Kalman filter (KF) considering various correlations, can be calculated from solving an equivalent GLS using the unified RLS.
In Chapters 5 & 6, an approach of joint state-and-parameter estimation (JSPE) in power system monitored by synchrophasors is adopted, where the original nonlinear parameter problem is reformulated as two loosely-coupled linear subproblems: state tracking and parameter tracking. Chapter 5 deals with the state tracking which determines the voltages in JSPE, where dynamic behavior of voltages under possible abrupt changes is studied. Chapter 6 focuses on the subproblem of parameter tracking in JSPE, where a new prediction model for parameters with moving means is introduced. Adaptive filters are developed for the above two subproblems, respectively, and both filters are based on the optimal KF accounting for various correlations. Simulations indicate that the proposed approach yields accurate parameter estimates and improves the accuracy of the state estimation, compared with existing methods.
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Seegmiller, Lindsi. "Modeling and optimization of least-cost corridors." Licentiate thesis, KTH, Geoinformatik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291279.
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Given a grid of cells, each having a value indicating its cost per unit area, a variant of the least-cost path problem is to find a corridor of a specified width connecting two termini such that its cost-weighted area is minimized. A computationally efficient method exists for finding such corridors, but as is the case with conventional raster-based least-cost paths, their incremental orientations are limited to a fixed number of (typically eight orthogonal and diagonal) directions, and therefore, regardless of the grid resolution, they tend to deviate from those conceivable on the Euclidean plane. Additionally, these methods are limited to problems found on two-dimensional grids and ignore the ever-increasing availability and necessity of three-dimensional raster based geographic data. This thesis attempts to address the problems highlighted above by designing and testing least-cost corridor algorithms. First a method is proposed for solving the two-dimensional raster-based least-cost corridor problem with reduced distortion by adapting a distortion reduction technique originally designed for least-cost paths and applying it to an efficient but distortionprone least-cost corridor algorithm. The proposed method for distortion reduction is, in theory, guaranteed to generate no less accurate solutions than the existing one in polynomial time and, in practice, expected to generate more accurate solutions, as demonstrated experimentally using synthetic and real-world data. A corridor is then modeled on a threedimensional grid of cost-weighted cubic cells or voxels as a sequence of sets of voxels, called ‘neighborhoods,’ that are arranged in a 26-hedoral form, design a heuristic method to find a sequence of such neighborhoods that sweeps the minimum cost-weighted volume, and test its performance with computer-generated random data. Results show that the method finds a low-cost, if not least-cost, corridor with a specified width in a threedimensional cost grid and has a reasonable efficiency as its complexity is O(n2) where n is the number of voxels in the input cost grid and is independent of corridor width. A major drawback is that the corridor found may self-intersect, which is often not only an undesirable quality but makes the estimation of its cost-weighted volume inaccurate.<br>Med tanke på ett rutnät av celler, som vart och ett har ett värde som indikerar dess kostnad per areaenhet, är en variant av det billigaste banproblemet att hitta en korridor med en specificerad bredd som förbinder två terminaler så att dess kostnadsviktade område minimeras. Det finns en beräkningseffektiv metod för att hitta sådana korridorer, men som är fallet med konventionella rasterbaserade lägsta kostnadsspår är deras inkrementella orienteringar begränsade till ett fast antal (vanligtvis åtta ortogonala och diagonala) riktningar, och därför, oavsett nätupplösning tenderar de att avvika från de tänkbara på det euklidiska planet. Dessutom är dessa metoder begränsade till problem som finns i tvådimensionella nät och ignorerar den ständigt ökande tillgängligheten och nödvändigheten av tredimensionell rasterbaserad geografisk data. Denna avhandling försöker ta itu med problemen som belyses ovan genom att utforma och testa korridoralgoritmer till lägsta kostnad. Först föreslås en metod för att lösa det tvådimensionella rasterbaserade problemet med billigaste korridorer med minskad förvrängning genom att anpassa en distorsionsminskningsteknik som ursprungligen utformades för billigaste vägar och tillämpa den på en effektiv men distorsionsbenägen billigaste korridoralgoritm. Den föreslagna metoden för distorsionsminskning är i teorin garanterad att generera inte mindre exakta lösningar än den befintliga i polynomtid och i praktiken förväntas generera mer exakta lösningar, vilket demonstreras experimentellt med syntetiska och verkliga data. En korridor modelleras sedan på ett tredimensionellt rutnät av kostnadsvägda kubikceller eller voxels som en sekvens av uppsättningar av voxels, kallade "stadsdelar", som är ordnade i en 26-hedoral form, designar en heuristisk metod för att hitta en sekvens av sådana stadsdelar som sveper den lägsta kostnadsviktade volymen och testar dess prestanda med datorgenererade slumpmässiga data. Resultaten visar att metoden hittar en låg kostnad, om inte minst kostnad, korridor med en specificerad bredd i ett tredimensionellt kostnadsnät och har en rimlig effektivitet eftersom dess komplexitet är O (n2) där n är antalet voxlar i ingångskostnadsnätet och är oberoende av korridorbredd En stor nackdel är att korridoren som hittas kan korsa sig själv, vilket ofta inte bara är en oönskad kvalitet utan gör uppskattningen av dess kostnadsviktade volym felaktig.<br><p>QC 20210309</p>
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Schwab, Devin. "Hierarchical Sampling for Least-Squares Policy Iteration." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1441374844.
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Chatkupt, Chlump. "Least-squares regret and partially strategic players." Thesis, London School of Economics and Political Science (University of London), 2015. http://etheses.lse.ac.uk/3220/.
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Noncooperative game theory enjoys a vast canon of solution concepts. The predominant solution concept is Nash equilibrium (Nash, 1950a; Nash, 1951). Other solution concepts include generalizations and refinements of Nash equilibrium as well as alternatives to it. Despite their successes, the established solution concepts are in some ways unsatisfactory. In particular, for many games, such as the Centipede Game (Rosenthal, 1981), the p-Beauty Contest (Moulin, 1986; Simonsen, 1988), and the notorious Traveler’s Dilemma (Basu, 1994; Basu, 2007), many of the solution concepts yield solutions that are both unreasonable in theory and refuted by the experimental evidence. And when a solution concept manages to yield the expected or reasonable solutions for such games, it often suffers from other difficulties such as unwieldy complexity or reliance on ad hoc or game-specific constructions that may fail to be generalizable. We propose a new solution concept, which we call least-squares regret, that yields the expected or reasonable solutions for games that have thus far proved to be problematic, such as the Traveler’s Dilemma; that is simple; that involves no ad hoc or game-specific constructions and can thus be applied immediately and consistently to any arbitrary game; that exhibits nice properties; and that is grounded in human psychology. Intuitively, we suppose that a player chooses a strategy so as to minimize the divergence from perfect play overall. In particular, we suppose that a player is partially strategic and chooses a strategy so as to minimize the sum, across all partial profiles of strategies of the other players, of the squares of the regrets, where the regret of a strategy with respect to a partial profile is the difference of the best-response payoff with respect to the partial profile and the payoff from choosing the strategy with respect to the partial profile. The aim of this work is to develop the solution concept of least-squares regret; explore its properties; assess its performance with respect to various games of interest; determine its merits and demerits, especially in relation to other solution concepts; review its weaknesses; introduce a refinement, which we call mutual weighted least-squares regret, that addresses some of the weaknesses; and propose some questions for further research.
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Degtyarena, Anna Semenovna. "The window least mean square error algorithm." CSUSB ScholarWorks, 2003. https://scholarworks.lib.csusb.edu/etd-project/2385.
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In order to improve the performance of LMS (least mean square) algorithm by decreasing the amount of calculations this research proposes to make an update on each step only for those elements from the input data set, that fall within a small window W near the separating hyperplane surface. This work aims to describe in detail the results that can be achieved by using the proposed LMS with window learning algorithm in information systems that employ the methodology of neural network for the purposes of classification.
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Rosopa, Patrick. "A COMPARISON OF ORDINARY LEAST SQUARES, WEIGHTED LEAST SQUARES, AND OTHER PROCEDURES WHEN TESTING FOR THE EQUALITY OF REGRESSION." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2311.
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When testing for the 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 & Alexander, 1996; Wilcox, 1997). Overton (2001) recommended weighted least squares estimation, demonstrating that it outperformed OLS and performed comparably to various statistical approximations. However, Overton's method was limited to two groups. In this study, a generalization of Overton's method is described. Then, using a Monte Carlo simulation, its performance was compared to three alternative weight estimators and three other methods. The results suggest that the generalization provides power levels comparable to the other methods without sacrificing control of Type I error rates. Moreover, in contrast to the statistical approximations, the generalization (a) is computationally simple, (b) can be conducted in commonly available statistical software, and (c) permits post hoc analyses. Various unique findings are discussed. In addition, implications for theory and practice in psychology and future research directions are discussed.<br>Ph.D.<br>Department of Psychology<br>Sciences<br>Psychology
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Dirks, Brian J. "Distribution and productivity of least terns and piping plovers along the Missouri and Cheyenne rivers in South Dakota." Connect to this title online, 1990. http://www.fs.fed.us/r2/nebraska/gpng/lt%5Fplover/ltppmissouri/LTPPMissouriDirks1990.pdf.
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Martin, Holly R. "Intraspecific phylogeography of the Least brook lamprey (Lampetra aepyptera)." Ohio : Ohio University, 2006. http://www.ohiolink.edu/etd/view.cgi?ohiou1141746230.
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Holmes, Marion R. "Least squares approximation by G¹ piecewise parametric cubics /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA277978.
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Huang, Xuejun, and Xuewen Huang. "The Least-Squares Method for American Option Pricing." Thesis, Uppsala University, Department of Mathematics, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-119754.
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Munoz, Maldonado Yolanda. "Mixed models, posterior means and penalized least squares." Texas A&M University, 2005. http://hdl.handle.net/1969.1/2637.
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In recent years there has been increased research activity in the area of Func-
tional Data Analysis. Methodology from finite dimensional multivariate analysis has
been extended to the functional data setting giving birth to Functional ANOVA,
Functional Principal Components Analysis, etc. In particular, some studies have pro-
posed inferential techniques for various functional models that have connections to
well known areas such as mixed-effects models or spline smoothing. The methodol-
ogy used in these cases is computationally intensive since it involves the estimation of
coefficients in linear models, adaptive selection of smoothing parameters, estimation
of variances components, etc.
This dissertation proposes a wide-ranging modeling framework that includes
many functional linear models as special cases. Three widely used tools are con-
sidered: mixed-effects models, penalized least squares, and Bayesian prediction. We
show that, in certain important cases, the same numerical answer is obtained for these
seemingly different techniques. In addition, under certain assumptions, an applica-
tion of a Kalman filter algorithm is shown to improve the order of computations, by
two orders of magnitude, for point and interval estimates (with n being the sample
size). A functional data analysis setting is used to exemplify our results.