Relevant bibliographies by topics / Method of least square

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Academic literature on the topic 'Method of least square'

Author: Grafiati

Published: 3 June 2025

Last updated: 19 July 2025

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Journal articles on the topic "Method of least square"

张, 熙. "Modified Moving Least Square Method." Advances in Applied Mathematics 05, no. 04 (2016): 662–71. http://dx.doi.org/10.12677/aam.2016.54078.

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Dokoupil, Jakub, and Vladimír Burlak. "Variable Regularized Square Root Recursive Least Square Method." IFAC Proceedings Volumes 45, no. 7 (2012): 78–82. http://dx.doi.org/10.3182/20120523-3-cz-3015.00017.

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Jumaah, Al-Nussairi Ahmed Kateb. "Enhanced Least Square Method for Indoor Positioning System Using UWB Technology." Webology 19, no. 1 (2022): 3815–34. http://dx.doi.org/10.14704/web/v19i1/web19251.

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One of the main radio technologies that could be used for indoor localization is Ultra-wideband, (UWB). It is a short-range RF technology for wireless communication that can be leveraged to detect the location of people, devices, and assets with significant precision. But, it has a major limitation which is the need for a non-line-of-sight (NLOS) identification and mitigation approach to precise location a target in a hard indoor environment. The NLOS approach will complicate the positioning approach. The goals of this work are; i- for saving cost and time of installation of anchor nodes, the minimum required number of anchor nodes have been installed, ii- the accuracy of the created system should be compatible with most various indoor environments. In this work, we create a novel algorithm of Indoor positioning system named Enhanced Linearized Least Square (ELLS) using UWB technology without using an NLOS identification approach. We evaluate and validate the created system by implementing real experiments. The created system has an average positioning accuracy reaching about 0.45 𝑚2of mean square error in a hard environment. It outperforms most indoor positioning systems in the market with less complexity, cost, and more accuracy.

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Yeniay, Özgür, Öznur İşçi, Atilla Göktaş, and M. Niyazi Çankaya. "Time Scale in Least Square Method." Abstract and Applied Analysis 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/354237.

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Study of dynamic equations in time scale is a new area in mathematics. Time scale tries to build a bridge between real numbers and integers. Two derivatives in time scale have been introduced and called as delta and nabla derivative. Delta derivative concept is defined as forward direction, and nabla derivative concept is defined as backward direction. Within the scope of this study, we consider the method of obtaining parameters of regression equation of integer values through time scale. Therefore, we implemented least squares method according to derivative definition of time scale and obtained coefficients related to the model. Here, there exist two coefficients originating from forward and backward jump operators relevant to the same model, which are different from each other. Occurrence of such a situation is equal to total number of values of vertical deviation between regression equations and observation values of forward and backward jump operators divided by two. We also estimated coefficients for the model using ordinary least squares method. As a result, we made an introduction to least squares method on time scale. We think that time scale theory would be a new vision in least square especially when assumptions of linear regression are violated.

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Lieb, Shannon G. "Simplex Method of Nonlinear Least-Squares - A Logical Complementary Method to Linear Least-Square Analysis of Data." Journal of Chemical Education 74, no. 8 (1997): 1008. http://dx.doi.org/10.1021/ed074p1008.

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Zhen, Chen, Xu Jing, Xu Wenhui, Guo Zihan, and Qin Chuan. "Precise Locating Algorithm by Modified Least Square Method for CES Switch Operating." International Journal of Electronics and Electrical Engineering 9, no. 1 (2021): 11–15. http://dx.doi.org/10.18178/ijeee.9.1.11-15.

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Accurate positioning method of three dimensional pose of space circle is a typical problem of binocular vision. The correlation algorithm is particularly important for the positioning accuracy in the case of strong interference of edge feature information. According to the precise positioning requirements of the distribution cabinet handcart in the distribution room, this paper establishes the elliptic curve optimization method based on the European distance minimization feature. Based on the least square method, the information model of the outside edge of the switch is established by using the circular contour of the cylindrical groove, and the three-dimensional pose positioning method of the spatial circle is studied. Finally, the effectiveness of the algorithm is verified by a case study.

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Wang, Qiao, Wei Zhou, Yonggang Cheng, et al. "Regularized moving least-square method and regularized improved interpolating moving least-square method with nonsingular moment matrices." Applied Mathematics and Computation 325 (May 2018): 120–45. http://dx.doi.org/10.1016/j.amc.2017.12.017.

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Kim, Nam, and Jung-Hun Choi. "Least Square Channel Estimation Scheme of OFDM System using Fuzzy Inference Method." Journal of the Korea Contents Association 9, no. 5 (2009): 84–90. http://dx.doi.org/10.5392/jkca.2009.9.5.084.

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Zulkifli, Raudhah, Nazim Aimran, Sayang Mohd Deni, and Fatin Najihah Badarisam. "A comparative study on the performance of maximum likelihood, generalized least square, scale-free least square, partial least square and consistent partial least square estimators in structural equation modeling." International Journal of Data and Network Science 6, no. 2 (2022): 391–400. http://dx.doi.org/10.5267/j.ijdns.2021.12.015.

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Structural equation modeling offers various estimation methods for estimating parameters. The most used method in covariance-based structural equation modeling (CB-SEM) is the maximum likelihood (ML) estimator. The ML estimator is typically used when fitting models with normally distributed data. The growth of partial least squares path modeling (PLS-PM), including consistent partial least squares (PLSc), has also been noticed by researchers in the SEM fields. The PLSc has elevated interest in the scholastic setting in measuring the performance of various estimation methods in structural equation modeling. The choice of estimation methods has substantial impact in yielding parameter estimates. There could be a trade-off among the estimation methods’ ability to deal with different types of data based on the model tested. Accordingly, this study aims to compare the performance of ML, generalized least squares (GLS), and scale-free least squares (SFLS) for CB-SEM as well as partial least squares (PLS) and consistent partial least squares (PLSc). Multivariate normal data were generated using Monte Carlo simulation with pre-determined population parameters and sample sizes using R Programming packages. To produce the estimated values, data analysis was performed using AMOS and SmartPLS for CB-SEM and PLS-SEM, respectively. The findings illustrate notable similarities between CB-SEM (ML) and PLS-SEM results when the true indicator loading is certainly high.

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Zhu, Qiang, Guo Hui Zhang, and Zhen Xu. "Radius Constraint Least-Square Circle Fitting Method." Advanced Materials Research 411 (November 2011): 241–44. http://dx.doi.org/10.4028/www.scientific.net/amr.411.241.

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Short arc usually lost the most part of information in measurement, therefore the fitting center can not be found accurately. The shortage of least-square method is analyzed in short arc fitting. The uncertainty of fitting center direction and fitting radius is illustrated. And we derived the solution to estimation of fitting center direction. Base on the testing environment, radius constraint least-square fitting circle method is proposed. Simulations demonstrated excellent performance of this algorithm.

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Dissertations / Theses on the topic "Method of least square"

Sandnes, Pål Grøthe. "Meshfree Least Square-based Finite Difference method in CFD applications." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for marin teknikk, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-15454.

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Most commercial computational fluid dynamics (CFD) packages available today are based on the finite volume- or finite element method. Both of these methods have been proven robust, efficient and appropriate for complex geometries. However, due to their crucial dependence on a well constructed grid, extensive preliminary work have to be invested in order to obtain satisfying results. During the last decades, several so-called meshfree methods have been proposed with the intension of entirely eliminating the grid dependence. Instead of a grid, meshfree methods use the nodal coordinates directly in order to calculate the spatial derivatives. In this master thesis, the meshfree least square-based finite difference (LSFD) method has been considered. The method has initially been thoroughly derived and tested for a simple Poisson equation. With its promising numerical performance, it has further been applied to the full Navier- Stokes equations, describing fluid motions in a continuum media. Several numerical methods used to solve the incompressible Navier-Stokes equations have been proposed, and some of them have also been presented in this thesis. However, the temporal discretization has finally been done using a 1st order semi-implicit projection method, for which the primitive variables (velocity and pressure) are solved directly. In order to verify the developed meshfree LSFD code, in total four flow problems have been considered. All of these cases are well known due to their benchmarking relevance, and LSFD performs well compared to both earlier observations and theory. Even though the developed program in this thesis only supports two dimensional, incompressible and laminar flow regimes, the idea of meshfree LSFD is quite general and may very well be applied to more complex flows, including turbulence

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Filippov, V., and A. Rodionov. "On the justification of the least square method for nonpotential, nonlinear operators." Pontificia Universidad Católica del Perú, 2014. http://repositorio.pucp.edu.pe/index/handle/123456789/97171.

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Ahmed, Ilyas. "Importance Sampling for Least-Square Monte Carlo Methods." Thesis, KTH, Matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-193080.

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Pricing American style options is challenging due to early exercise opportunities. The conditional expectation in the Snell envelope, known as the continuation value is approximated by basis functions in the Least-Square Monte Carlo-algorithm, giving robust estimation for the options price. By change of measure in the underlying Geometric Brownain motion using Importance Sampling, the variance of the option price can be reduced up to 9 times. Finding the optimal estimator that gives the minimal variance requires careful consideration on the reference price without adding bias in the estimator. A stochastic algorithm is used to find the optimal drift that minimizes the second moment in the expression of the variance after change of measure. The usage of Importance Sampling shows significant variance reduction in comparison with the standard Least-Square Monte Carlo. However, Importance Sampling method may be a better alternative for more complex instruments with early exercise opportunity. Prissättning av amerikanska optioner är utmanande på grund av att man har rätten att lösa in optionen innan och fram till löptidens slut. Det betingade väntevärdet i Snell envelopet, känd som fortsättningsvärdet approximeras med basfunktioner i Least-Square Monte Carlo-algoritmen som ger robust uppskattning av optionspriset. Genom att byta mått i den underliggande geometriska Browniska rörelsen med Importance Sampling så kan variansen av optionspriset minskas med upp till 9 gånger. Att hitta den optimala skattningen som ger en minimal varians av optionspriset kräver en noggrann omtanke om referenspriset utan att skattningen blir för skev. I detta arbete används en stokastisk algoritm för att hitta den optimala driften som minimerar det andra momentet i uttrycket av variansen efter måttbyte. Användningen av Importance Sampling har visat sig ge en signifikant minskning av varians jämfört med den vanliga Least-Square Monte Carlometoden. Däremot kan importance sampling vara ett bättre alternativ för mer komplexa instrument där man har rätten at lösa in instrumentet fram till löptidens slut.

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Clack, Jhules. "Theoretical Analysis for Moving Least Square Method with Second Order Pseudo-Derivatives and Stabilization." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1418910272.

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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. 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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Sequeira, Bernardo Pinto Machado Portugal. "American put option pricing : a comparison between neural networks and least-square Monte Carlo method." Master's thesis, Instituto Superior de Economia e Gestão, 2019. http://hdl.handle.net/10400.5/19631.

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Mestrado em Mathematical Finance Esta tese compara dois métodos de pricing de opções de venda Americanas. Os métodos estudados são redes neurais (NN), um método de Machine Learning, e Least-Square Monte Carlo Method (LSM). Em termos de redes neurais foram desenvolvidos dois modelos diferentes, um modelo mais simples, Model 1, e um modelo mais complexo, Model 2. O estudo depende dos preços das opões de 4 gigantes empresas norte-americanas, de Dezembro de 2018 a Março de 2019. Todos os métodos mostram uma precisão elevada, no entanto, uma vez calibradas, as redes neuronais mostram um tempo de execução muito inferior ao LSM. Ambos os modelos de redes neurais têm uma raiz quadrada do erro quadrático médio (RMSE) menor que o LSM para opções de diferentes maturidades e preço de exercício. O Modelo 2 supera substancialmente os outros modelos, tendo um RMSE ca. 40% inferior ao do LSM. O menor RMSE é consistente em todas as empresas, níveis de preço de exercício e maturidade. This thesis compares two methods to evaluate the price of American put options. The methods are the Least-Square Monte Carlo Method (LSM) and Neural Networks, a machine learning method. Two different models for Neural Networks were developed, a simple one, Model 1, and a more complex model, Model 2. It relies on market option prices on 4 large US companies, from December 2018 to March 2019. All methods show a good accuracy, however, once calibrated, Neural Networks show a much better execution time, than the LSM. Both Neural Network end up with a lower Root Mean Square Error (RMSE) than the LSM for options of different levels of maturity and strike. Model 2 substantially outperforms the other models, having a RMSE ca. 40% lower than that of LSM. The lower RMSE is consistent across all companies, strike levels and maturities. info:eu-repo/semantics/publishedVersion

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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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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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Johnsen, Eivind. "Application method of the least squares finite element method to fracture mechanics." Thesis, Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/16435.

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Kattelans, Thorsten. "The least squares spectral collocation method for incompressible flows." Berlin Köster, 2009. http://d-nb.info/997987812/04.

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Books on the topic "Method of least square"

Bartlett, Dana P. General principles of the method of least squares. Dover, 2006.

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Wu, Sean F. The Helmholtz Equation Least Squares Method. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-1640-5.

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Jiang, Bo-nan. The Least-Squares Finite Element Method. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-03740-9.

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Jiang, Bo-Nan. Least-squares finite elements for Stokes problem. ICOMP, 1988.

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Bochev, Pavel B. Least-squares finite element methods. Springer, 2009.

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Merriman, Mansfield. Elements of the method of least squares. Nabu Press, 2010.

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Bochev, Pavel B. Least-squares finite element methods. Springer, 2009.

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Anciant, M. Optimisation methods for solving separable nonlinear least squares problems. University of Southampton, Institute of Sound and Vibration Research, 1993.

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D, Gunzburger Max, ed. Least-squares finite element methods. Springer, 2009.

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A, Povinelli Louis, and United States. National Aeronautics and Space Administration., eds. Optimal least-squares finite element method for elliptic problems. National Aeronautics and Space Administration, 1991.

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Book chapters on the topic "Method of least square"

Xu, Yejun. "Weighted Least Square Method." In Deriving Priorities from Incomplete Fuzzy Reciprocal Preference Relations. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3169-9_8.

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Demasi, Luciano. "Review of the Least Square Method." In Introduction to Unsteady Aerodynamics and Dynamic Aeroelasticity. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-50054-1_5.

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Kong, Xiangyu, and Dazheng Feng. "Total Least Square Methods." In Engineering Applications of Computational Methods. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1765-1_3.

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Abdi, Hervé, and Lynne J. Williams. "Partial Least Squares Methods: Partial Least Squares Correlation and Partial Least Square Regression." In Methods in Molecular Biology. Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-059-5_23.

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Demaison, Jean, and Natalja Vogt. "Least-Squares Method." In Lecture Notes in Chemistry. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60492-9_9.

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Gooch, Jan W. "Least Squares Method." In Encyclopedic Dictionary of Polymers. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15272.

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Uncini, Aurelio. "Least Squares Method." In Fundamentals of Adaptive Signal Processing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02807-1_4.

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Kong, Xiangyu, and Dazheng Feng. "Total Least Square Problems." In Engineering Applications of Computational Methods. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1765-1_2.

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Wuthrich, Rolf, and Carole El Ayoubi. "Linear Least Square Regression." In Numerical Methods for Engineering and Data Science. CRC Press, 2025. https://doi.org/10.1201/9781003262121-7.

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Grabe, Michael. "Method of Least Squares." In Generalized Gaussian Error Calculus. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03305-6_8.

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Conference papers on the topic "Method of least square"

Haiyan Shi, Zhongliang Jing, and H. Leung. "A constrained least square and trimmed least square method for multisensor data fusion." In Proceedings of 2003 International Conference on Neural Networks and Signal Processing. IEEE, 2003. http://dx.doi.org/10.1109/icnnsp.2003.1279413.

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Dengen, Nataniel, Haviluddin, Lia Andriyani, Masna Wati, Edy Budiman, and Faza Alameka. "Medicine Stock Forecasting Using Least Square Method." In 2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT). IEEE, 2018. http://dx.doi.org/10.1109/eiconcit.2018.8878563.

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Jaswanth, Mandava, Namburi K. L. Narayana, Sreedharreddy Rahul, R. Subramani., and K. Murali. "Product Price Optimization using Least Square Method." In 2022 IEEE 2nd International Conference on Mobile Networks and Wireless Communications (ICMNWC). IEEE, 2022. http://dx.doi.org/10.1109/icmnwc56175.2022.10031834.

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Kumar, Arvind, and Vipin Solanki. "Product Price Optimization using Least Square Method." In 2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART). IEEE, 2022. http://dx.doi.org/10.1109/smart55829.2022.10046892.

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Arzani, H., and M. H. Afshar. "Solving Poisson’s equations by the Discrete Least Square meshless method." In BOUNDARY ELEMENT METHOD 2006. WIT Press, 2006. http://dx.doi.org/10.2495/be06003.

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Rao, Lingru, Jun Yang, and Ziwen Wei. "A Wireless Node Location Method Combining Least Absolute Deviation and Least Square Method*." In 2023 35th Chinese Control and Decision Conference (CCDC). IEEE, 2023. http://dx.doi.org/10.1109/ccdc58219.2023.10327303.

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Seok-Hwan, Moon, Kim Ji-Won, and Park Byung-Gun. "Adaptive PLL method using recursive least square algorithm." In 2015 IEEE International Conference on Industrial Technology (ICIT). IEEE, 2015. http://dx.doi.org/10.1109/icit.2015.7125211.

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Jian Zhang, Bin Wu, and Tao Wang. "Adaptive substructure testing method based on least square." In 2011 International Conference on Electric Technology and Civil Engineering (ICETCE). IEEE, 2011. http://dx.doi.org/10.1109/icetce.2011.5775338.

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Li, Fengping, and Haiyan Yang. "Least square regularization inversion of transient electromagnetic method." In 7th International Conference on Environment and Engineering Geophysics & Summit Forum of Chinese Academy of Engineering on Engineering Science and Technology. Atlantis Press, 2016. http://dx.doi.org/10.2991/iceeg-16.2016.44.

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Latry, Christophe, Vincent Despringre, and Christophe Valorge. "Automatic MTF measurement through a least square method." In Remote Sensing, edited by Roland Meynart, Steven P. Neeck, and Haruhisa Shimoda. SPIE, 2004. http://dx.doi.org/10.1117/12.564796.

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Reports on the topic "Method of least square"

Desbarats, A. J. An iterative least-square method for the inversion of spectral radiometric data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128069.

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Dennis, Jr, Songbai J. E., Vu Sheng, and Phuong A. A Memoryless Augmented Gauss-Newton Method for Nonlinear Least-Squares Problems. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada454936.

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GOSSLER, ALBERT A. Moving Least-Squares: A Numerical Differentiation Method for Irregularly Spaced Calculation Points. Office of Scientific and Technical Information (OSTI), 2001. http://dx.doi.org/10.2172/782717.

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Yao, Stephen E., Fred McCartney Dickey, and Sara North Pecak. A least squares method for CVT calibration in a RLC capacitor discharge circuit. Office of Scientific and Technical Information (OSTI), 2003. http://dx.doi.org/10.2172/918301.

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Kevorkian, A. K. A Direct Decomposition Method for the Solution of Sparse Linear Least Squares Problems. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada284060.

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Reister, D. B., and M. D. Morris. A method for obtaining a least squares fit of a hyperplane to uncertain data. Office of Scientific and Technical Information (OSTI), 1994. http://dx.doi.org/10.2172/10153960.

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Koester, Jacob, Michael R. Tupek, and Scott A. Mitchell. An Agile Design-to-Simulation Workflow Using a New Conforming Moving Least Squares Method. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1569655.

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Chen, Zhenpeng, and Yeying Sun. A Global Fitting Method with hte R-Matrix Code RAC. IAEA Nuclear Data Section, 2019. http://dx.doi.org/10.61092/iaea.zr3b-121v.

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This report introduces the evaluation method RAC-CERNGEPLIS and the results obtained for the project “R-matrix Codes for Charged-particle Induced Reactions in the Resolved Resonance Region” that is coordinated by the Nuclear Data Section. In fact, this method has been used before in the evaluation of the compound systems n+6Li and n+10B, for the IAEA Neutron Standards (2006 and 2017 release). The main characteristics of the RAC code are that i) the eliminated channel width is included in the R-matrix algorithm and ii) the Generalized-Least Square method is used in the fitting procedure. In this report we discuss different approaches to R-Matrix fitting that are used in nuclear data evaluation. Practice shows that the RAC-CERNGEPLIS method is a reasonable, useful and powerful tool for evaluation of nuclear data.

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Dennis, J. E., H. J. Martinez, and R. A. Tapia. A Convergence Theory for the Structured BFGS Secant Method With an Application to Nonlinear Least Squares. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada455135.

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