Relevant bibliographies by topics / Linear Least Squares Regression Method

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Linear Least Squares Regression Method'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Linear Least Squares Regression Method"

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Shi, Hongmei, Xingbo Zhang, Yuzhen Gao, Shuai Wang, and Yufu Ning. "Robust Total Least Squares Estimation Method for Uncertain Linear Regression Model." Mathematics 11, no. 20 (2023): 4354. http://dx.doi.org/10.3390/math11204354.

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In data analysis and modeling, least squares and total least squares are both mathematical optimization techniques. It is noteworthy that both the least squares method and the total least squares method are used to deal with precise and random data. However, when the given data are not random, or when the data are imprecise, and only the range of the data is available, the traditional linear regression method cannot be used. This paper presents an uncertain total least squares estimation method and an uncertain robust total least squares linear regression method based on uncertainty theory and
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Ogunbona, Babafemi D., Folorunsho O. Balogun, and Kayode S. Famuagun. "Solving Multicolinearity Problem in a Linear Regression: A Comparative Study of Ordinary Least Squares and Partial Least Squares Regression." Journal of Institutional Research, Big Data Analytics and Innovation 1, no. 1 (2024): 66–75. https://doi.org/10.5281/zenodo.15556948.

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Ordinary Least Squares (OLS) estimator usually yields inefficient estimates when multicollinearity is present in a Linear Regression Model. The inefficiency of OLS can be mitigated by Partial Least Squares Regression (PLSR). However, this method requires selecting latent variables in order to yield efficient estimates of regression parameters. This paper proposes using weighted standard errors and ranking standard errors of regression coefficients for latent variable extraction, alongside model selection methods such as Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC),
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Yu, Yan Hua, Li Xia Song, and Kun Lun Zhang. "Fuzzy C-Regression Models." Applied Mechanics and Materials 278-280 (January 2013): 1323–26. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.1323.

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Fuzzy linear regression has been extensively studied since its inception symbolized by the work of Tanaka et al. in 1982. As one of the main estimation methods, fuzzy least squares approach is appealing because it corresponds, to some extent, to the well known statistical regression analysis. In this article, a restricted least squares method is proposed to fit fuzzy linear models with crisp inputs and symmetric fuzzy output. The paper puts forward a kind of fuzzy linear regression model based on structured element, This model has precise input data and fuzzy output data, Gives the regression
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Høiseth, Lars Øivind, and Jostein S. Hagemo. "Comparing measurement methods using linear least squares regression." Journal of Clinical Monitoring and Computing 32, no. 2 (2016): 373–74. http://dx.doi.org/10.1007/s10877-016-9955-0.

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Wu, Ju. "Research on Several Problems in Partial Least Squares Regression Analysis." Open Electrical & Electronic Engineering Journal 8, no. 1 (2014): 754–58. http://dx.doi.org/10.2174/1874129001408010754.

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Purpose: preliminary discussion on model prediction precision in the partial least squares regression analysis method; Method: introduce current development conditions of partial least squares regression analysis, analyze problems of traditional regression analysis method such as multiple linear regression analysis, introduce the mathematic principle and modeling method of the partial least squares regression analysis method, and conduct detailed analysis on the partial least squares regression analysis modeling and prediction by using the classical Linnerud data. Result: The partial least squ
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Sanchez, Juan. "Estimating Detection Limits in Chromatography from Calibration Data: Ordinary Least Squares Regression vs. Weighted Least Squares." Separations 5, no. 4 (2018): 49. http://dx.doi.org/10.3390/separations5040049.

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It is necessary to determine the limit of detection when validating any analytical method. For methods with a linear response, a simple and low labor-consuming procedure is to use the linear regression parameters obtained in the calibration to estimate the blank standard deviation from the residual standard deviation (sres), or the intercept standard deviation (sb0). In this study, multiple experimental calibrations are evaluated, applying both ordinary and weighted least squares. Moreover, the analyses of replicated blank matrices, spiked at 2–5 times the lowest calculated limit values with t
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Klen, Kateryna, Vadym Martynyuk, and Mykhailo Yaremenko. "Prediction of the wind speed change function by linear regression method." Computational Problems of Electrical Engineering 9, no. 2 (2019): 28–33. http://dx.doi.org/10.23939/jcpee2019.02.028.

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In the article the approximation of the function of wind speed changes by linear functions based on Walsh functions and the prediction of function values by linear regression method is made. It is shown that under the condition of a linear change of the internal resistance of the wind generator over time, it is advisable to introduce the wind speed change function with linear approximation. The system of orthonormal linear functions based on Walsh functions is given. As an example, the approximation of the linear-increasing function with a system of 4, 8 and 16 linear functions based on the Wa
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Li, Siming, Yao Sheng, and Yong Li. "The quasi-stochastically constrained least squares method for ill linear regression." Communications in Statistics - Theory and Methods 45, no. 2 (2015): 217–25. http://dx.doi.org/10.1080/03610926.2013.828076.

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Abdelhadi, Yaser. "Linear modeling and regression for exponential engineering functions by a generalized ordinary least squares method." International Journal of Engineering & Technology 3, no. 2 (2014): 174. http://dx.doi.org/10.14419/ijet.v3i2.2023.

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Linear transformations are performed for selected exponential engineering functions. The Optimum values of parameters of the linear model equation that fits the set of experimental or simulated data points are determined by the linear least squares method. The classical and matrix forms of ordinary least squares are illustrated. Keywords: Exponential Functions; Linear Modeling; Ordinary Least Squares; Parametric Estimation; Regression Steps.
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Justo, Claudio Eduardo, and María Valeria Calandra. "Evaluation of Robust Linear Regression Methods for the Measurement of a Topographic Altimetric Network." Journal of Scientific and Engineering Research 8, no. 4 (2021): 30–39. https://doi.org/10.5281/zenodo.10579587.

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<strong>Abstract</strong> This work presents the comparison of different adjustment methods for the analysis of observations made to provide heights to a topographic altimetric network. The methods evaluated were the traditional Weighted Least Squares, compared with the results obtained using two other alternative adjustment methods, these being the Robust Regression Methods called M-Estimators and MM-Estimators. The incidence of outliers was assessed with all three methods. It was found that the traditional method presents good results in the range of outliers present. Since the observations
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Dissertations / Theses on the topic "Linear Least Squares Regression Method"

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Kim, Jingu. "Nonnegative matrix and tensor factorizations, least squares problems, and applications." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42909.

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Nonnegative matrix factorization (NMF) is a useful dimension reduction method that has been investigated and applied in various areas. NMF is considered for high-dimensional data in which each element has a nonnegative value, and it provides a low-rank approximation formed by factors whose elements are also nonnegative. The nonnegativity constraints imposed on the low-rank factors not only enable natural interpretation but also reveal the hidden structure of data. Extending the benefits of NMF to multidimensional arrays, nonnegative tensor factorization (NTF) has been shown to be successful in
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Ulgen, Burcin Emre. "Estimation In The Simple Linear Regression Model With One-fold Nested Error." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/3/12606171/index.pdf.

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In this thesis, estimation in simple linear regression model with one-fold nested error is studied. To estimate the fixed effect parameters, generalized least squares and maximum likelihood estimation procedures are reviewed. Moreover, Minimum Norm Quadratic Estimator (MINQE), Almost Unbiased Estimator (AUE) and Restricted Maximum Likelihood Estimator (REML) of variance of primary units are derived. Also, confidence intervals for the fixed effect parameters and the variance components are studied. Finally, the aforesaid estimation techniques and confidence intervals are applied to a real-life
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Haubeltova, Libuse. "Case study of Airbnb listings in Berlin : Hedonic pricing approach to measuring demand for tourist accommodation characteristics." Thesis, Högskolan Dalarna, Nationalekonomi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:du-29979.

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The main purpose of this degree project is to reveal the Airbnb customer’s preferences and quantify the impact of non-market factors on the market price of tourist accommodation in Berlin, Germany. The data retrieved from Airbnb listings, publicly available on Inside Airbnb (2017), was supplemented on indicator of sharing economy accommodation using machine learning method in order to distinguish between amateur and business-running professional hosts. The main aim is to examine the consumers’ preferences and quantify the marginal effect of "real sharing economy" accommodation and other key va
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Ferreira, Wellington Vieira. "Regressão linear simples aplicado na física experimental do ensino médio." Universidade Federal de Goiás, 2017. http://repositorio.bc.ufg.br/tede/handle/tede/7842.

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Submitted by Marlene Santos (marlene.bc.ufg@gmail.com) on 2017-10-02T16:50:28Z No. of bitstreams: 2 Dissertacao - Wellington Vieira Ferreira - 2017.pdf: 6954589 bytes, checksum: 5abbfbaabae1b612e006f3f6cb213cf1 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)<br>Rejected by Luciana Ferreira (lucgeral@gmail.com), reason: Não estamos usando a expressão "Profissional" na citação Errado: FERREIRA, Wellington Vieira. Regressão linear simples aplicado na física experimental do ensino médio. 2017. 92 f. Dissertação ( Mestrado Profissional em Matemática em Rede Nacional) -
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PAGLIANA, NICOLO'. "On the Role of Regularization in Machine Learning: Classical Theory, Computational Aspects and Modern Regimes." Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1081700.

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In this work we study performances of different machine learning models by focusing on regularization properties in order to explain different phenomena that are observed in practice. We consider linear models on possibly infinite dimensionale feature space that are trained by optimizing an empirical mean squared errror. We study regularization properties of accelerated method like Nesterov or $ u$-method and properties of interpolating estimator where the main regularization sources vanish to zero and explain different behaviour which can be seen in practical applications.
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Krba, Martin. "Identifikace počítače na základě časových značek paketů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236536.

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Basic way how to identify a device in computer network is by MAC address and IP address. Main goal of this work is to create an application capable of clear identification of devices in computer network regardless change of their MAC address or IP address. This is done by exploiting tiny deviations in hardware clock known as clock skew. They appear in every clock based on quartz oscillator. Using clock skew is beneficial, because there is no need of any changes in fingerprinted device nor their cooperation. Accessing these values is done by capturing packets with timestamps included. Applicati
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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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Studený, Marek. "Modelování tržní ceny nemovitosti mnohonásobnou lineární regresí." Master's thesis, Vysoké učení technické v Brně. Ústav soudního inženýrství, 2013. http://www.nusl.cz/ntk/nusl-232776.

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The main subject of the diploma thesis is a market price modeling by real estates. As a tool for modeling, is used a multiple linear regression. As starting points, are used an econometrical theory and knowledge about real estate valuation. The main goal is to find optimal model for best capture in the time and place.
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Sardy, Sylvain. "Regularization techniques for linear regression with a large set of carriers / by Sylvain Sardy." Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/8973.

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Barker, Ethan Jefferson. "Evaluating and improvement of tree stump volume prediction models in the eastern United States." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/77935.

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Forests are considered among the best carbon stocks on the planet. After forest harvest, the residual tree stumps persist on the site for years after harvest continuing to store carbon. A bigger concern is that the component ratio method requires a way to get stump volume to obtain total tree aboveground biomass. Therefore, the stump volumes contribute to the National Carbon Inventory. Agencies and organizations that are concerned with carbon accounting would benefit from an improved method for predicting tree stump volume. In this work, many model forms are evaluated for their accuracy in pre
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Books on the topic "Linear Least Squares Regression Method"

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Grafarend, Erik. Linear and Nonlinear Models: Fixed effects, random effects, and total least squares. Springer Berlin Heidelberg, 2012.

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Yang, Keming, ed. Categorical Data Analysis. SAGE Publications Ltd, 2014.

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Duan, Naihua. A bias bound for least squares linear regression. RAND, 1991.

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Bac-Van, Nguyen. Consistency of least squares estimates in a system of linear correlation models. Polska Akademia Nauk, Instytut Matematyczny, 2001.

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Racine, J. S. Semiparamteric estimation in the presence of heteroskedasticity of unknown form. Dept. of Economics, York University, 1989.

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Q, Pan J., and United States. National Aeronautics and Space Administration., eds. Frequency analysis via the method of moment functionals. National Aeronautics and Space Administration, 1990.

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Roorda, Berend. Global total least squares: A method for the construction of open approximate models from vector time series. Thesis Publishers, 1995.

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Voronin, Evgeniy, Aleksandr Chibunichev, and Yuriy Blohinov. Reliability of solving inverse problems of analytical photogrammetry. INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/2010462.

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The monograph is devoted to computational aspects of photogrammetric reconstruction of narrow-angle bundles of projecting beams that existed during the survey. Methods of improving the conditionality of systems of linear equations, ensuring the convergence of iterative refinement of their roots, increasing the stability of calculations in finite precision machine arithmetic are considered. The main efforts are focused on solving the problem of establishing reliable measurement weights within the framework of the least squares method. The criteria for the reliability of the weights are determin
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NANDE-VÁZQUEZ, Edgard Alfredo, Teodoro REYES-FONG, and Omar Alejandro PÉREZ-CRUZ. The Generalized Least Squares Method (GMM) as a tool for causal analysis of spending, budget management and electoral results. ECORFAN, 2021. http://dx.doi.org/10.35429/b.2021.8.1.130.

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In the different fields of science, many times, there is a need to estimate the associations between variables, as an approach to understanding the interaction of one as a function of the others. It is usually done by applying restrictive models, such as analysis of variance and linear regression. This type of analysis requires that the dependent variable be continuous, have a normal and constant distribution of the mean and variance. However, when the dependent variable is discrete or categorical, the linear model is not viable. Faced with this impediment, the theory of linear models arises a
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Witkov, Carey, and Keith Zengel. Chi-Squared Data Analysis and Model Testing for Beginners. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198847144.001.0001.

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This book is the first to make chi-squared model testing, one of the data analysis methods used to discover the Higgs boson and gravitational waves, accessible to undergraduate students in introductory physics laboratory courses. By including uncertainties in the curve fitting, chi-squared data analysis improves on the centuries old ordinary least squares and linear regression methods and combines best fit parameter estimation and model testing in one method. A toolkit of essential statistical and experimental concepts is developed from the ground up with novel features to interest even those
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Book chapters on the topic "Linear Least Squares Regression Method"

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Finch, W. Holmes. "Ordinary Least Squares Linear Regression." In Applied Statistical Methods. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003379324-9.

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Pillonetto, Gianluigi, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao, and Lennart Ljung. "Regularization of Linear Regression Models." In Regularized System Identification. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95860-2_3.

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AbstractLinear regression models are widely used in statistics, machine learning and system identification. They allow to face many important problems, are easy to fit and enjoy simple analytical properties. The simplest method to fit linear regression models is least squares whose systematic treatment is available in many textbooks, e.g., [35, Chap. 4], [12]. Linear regression models can be fitted also in different way and a class of methods that we will consider in this chapter is the so-called regularized least squares. It is an extension of least squares which minimizes the sum of the squa
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Agterberg, Frits. "Correlation, Method of Least Squares, Linear Regression and the General Linear Model." In Quantitative Geology and Geostatistics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06874-9_4.

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Salas, Antonia, Norberto Corral, and Carlo Bertoluzza. "Linear regression in a fuzzy context. The least square method." In Statistical Modeling, Analysis and Management of Fuzzy Data. Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1800-0_17.

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Rosset, Julien, and Laurent Donzé. "Fuzziest Estimate by Least Squares: Another Method to Estimate Fuzzy Orthogonal Linear Regression Models." In Studies in Computational Intelligence. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-85252-7_6.

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Frühwirth, Rudolf, and Are Strandlie. "Statistics and Numerical Methods." In Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65771-0_3.

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AbstractThe chapter gives an outline of some statistical and numerical methods that will be applied in later chapters. The first section deals with the minimization of functions. Several gradient-based methods and a popular non-gradient method are discussed. The following section discusses statistical models and the estimation of model parameters. The basics of linear and nonlinear regression models and state space models are presented, including least-squares estimation and the (extended) Kalman filter. The final section gives a brief overview of clustering and different types of clustering a
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Yuan, Lanlan, Jie Wu, and Huihui Sun. "Statistical Diagnosis of Fuzzy Linear Regression Model Based on Weighted Least Square Method." In Springer Proceedings in Mathematics & Statistics. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2379-2_7.

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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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Tran, Thu-Hang. "Preliminary Regression Study on Air Quality Inside a Road Tunnel: A Case Study in Vietnam." In Lecture Notes in Civil Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1260-3_9.

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AbstractNormal air quality that ensures the visibility and brings no harmful impact to the health of the vehicle drivers is essential for all road tunnels. It is affected by various issues. In this paper, the air quality – meteorology – traffic volume correlations were quested in the case study of an opened road tunnel on Vietnam National Highway 1A. The linear regression modelling technics using the least squares method with 95% of confidence was executed. Four representative models of the total suspended particles and airborne lead concentration against the meteorological parameters (tempera
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Olive, David J. "WLS and Generalized Least Squares." In Linear Regression. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55252-1_4.

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Conference papers on the topic "Linear Least Squares Regression Method"

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Roy, Piya, and Debashis Chatterjee. "A Comparative Study for Forecasting of Fault Locations in DC Microgrid Using Ordinary Least Squares Method and Non-linear Regression Method." In 2024 4th International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET). IEEE, 2024. https://doi.org/10.1109/icefeet64463.2024.10866333.

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Tamas, Anca. "DETERMINANTS OF THE TRADE BETWEEN BRICS COUNTRIES A GRAVITY MODEL APPROACH." In 11th SWS International Scientific Conferences on SOCIAL SCIENCES - ISCSS 2024. SGEM WORLD SCIENCE, 2024. https://doi.org/10.35603/sws.iscss.2024/s03/22.

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The aim of this paper is to find out the main determinants of the bilateral trade between the BRICS countries. The BRICS countries are fast growing, emerging markets, relatively recently known as a group that counts for more than a third of the world�s land and more than 40% of the world�s population. The augmented gravity model was used because is the model that explains most of the international bilateral trade and a panel approach was chosen to address the heteroskedasticity issues. The linear regression using Panel Least Squares Method was performed, with bilateral trade as dependent varia
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Zhang, Cong, Tianrui Li, and Chongshou Li. "A hierarchical reconciliation least square method for linear regression." In Conference on Machine learning, Multi Agent and Cyber Physical Systems (FLINS 2022). WORLD SCIENTIFIC, 2023. http://dx.doi.org/10.1142/9789811269264_0005.

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Ying, Yulong, Bo Sun, Shuhong Peng, Keliang Wu, Lei He, and Qiang Xu. "Study on the Regression Method of Compressor Map Based on Partial Least Squares Regression Modeling." In ASME Turbo Expo 2014: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/gt2014-25705.

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One of the most troublesome problems in the development of a component-based engine model is the compressor modeling because of the strong dependence of its performance on rotational speed and the treatment of compressor characteristic curves plays a significant role in modeling and simulation of gas turbine for the analysis of its off-design performance and thus it is crucial to describe compressor map exactly. Usually part of rotational speed characteristic curves of compressor including on-design operating point are known during actual modeling and simulation, and reasonable interpolation a
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Joslin, Andrew J., and Chengying Xu. "A Hybrid Modeling Technique for Partially-Known Systems Using Linear Regression and Neural Network." In ASME 2009 International Manufacturing Science and Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/msec2009-84217.

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In this paper a hybrid modeling and system identification method, combining linear least squares regression and artificial neural network techniques, is presented to model a type of dynamic systems which have an incomplete analytical model description. This approach in modeling nonlinear, partially-understood systems is particularly useful to the study of manufacturing processes, where the linear regression portion of the hybrid model is established using a known mathematical model for the process and the neural network is constructed using the residuals from the least squares regression, ther
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Chen, Xiaojun, Guowen Yuan, Feiping Nie, and Joshua Zhexue Huang. "Semi-supervised Feature Selection via Rescaled Linear Regression." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/211.

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With the rapid increase of complex and high-dimensional sparse data, demands for new methods to select features by exploiting both labeled and unlabeled data have increased. Least regression based feature selection methods usually learn a projection matrix and evaluate the importances of features using the projection matrix, which is lack of theoretical explanation. Moreover, these methods cannot find both global and sparse solution of the projection matrix. In this paper, we propose a novel semi-supervised feature selection method which can learn both global and sparse solution of the project
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Sari, Yulia Retno, and Maha Rani. "The fuzzy logic analysis accuracy of the mamdani method with multiple linear regression of the least squares method in determining national salt production." In II INTERNATIONAL CONFERENCE “SUSTAINABLE DEVELOPMENT: AGRICULTURE, VETERINARY MEDICINE AND ECOLOGY”. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0130449.

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Almasov, Azad, and Mustafa Onur. "Life-Cycle Gradient-Based Production Optimization Including Well-Shutoff Option with Least-Squares Support Vector Regression." In SPE Annual Technical Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/215110-ms.

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Abstract The objective of this work is to present an efficient method based on gradient-based optimization using a least-squares support-vector regression (LS-SVR) model to solve well-shutoff and well-control optimization problems. We formulate a continuous differentiable NPV for the well shutoff optimization problem. In our approach, switching well on/off times are considered part of the design variables. Our parameterization is based on a fixed number of cycles, the length of each cycle, and the production time fraction in each cycle. The remaining fraction of each cycle is the shutoff time
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Nedomová, Lea, and Petr Doucek. "Gender Pay Gap in the Czech Economy with Accent on ICT Professionals." In Values, Competencies and Changes in Organizations. University of Maribor Press, 2021. http://dx.doi.org/10.18690/978-961-286-442-2.47.

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The subject of our article is a comparison of the level and development of the gender pay gap between selected EU countries (especially the V4 countries and other selected countries such as Austria and Slovenia) for the period 2009 - 2019. The analysis for the Czech Republic will be then supplemented by a comparison with the development of the gender pay gap in the economy and in ICT Professionals. To approximate the development of wages and GDP, we used the method of linear regression together with the method of least squares. All regression analysis calculations are performed at the 5% level
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A. L. de Andrade, Renato, and Péricles R. Barros. "Comparative Study of Three Output-Error Multi-Input Multi-Output Identification Methods." In Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1440.

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Multi-input multi-output (MIMO) systems have been a major concern for decades. However, due to the intrinsic complexity raised by the process interactions and optimization issues, MIMO approaches have not been developed as extensively as the single-input single-output ones. Recently, nevertheless, several algorithms have been proposed to address this problem, most of them based on recursive algorithms and many dependent on the assumption that the transfer function denominator polynomials are the same for all subsystems. In this article, an iterative least-squares-based algorithm, a pseudolinea
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Reports on the topic "Linear Least Squares Regression Method"

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Rokhlin, Vladimir, and Mark Tygert. A Fast Randomized Algorithm for Overdetermined Linear Least-Squares Regression. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada489855.

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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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Filipiak, Katarzyna, Dietrich von Rosen, Martin Singull, and Wojciech Rejchel. Estimation under inequality constraints in univariate and multivariate linear models. Linköping University Electronic Press, 2024. http://dx.doi.org/10.3384/lith-mat-r-2024-01.

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In this paper least squares and maximum likelihood estimates under univariate and multivariate linear models with a priori information related to maximum effects in the models are determined. Both loss functions (the least squares and negative log-likelihood) and the constraints are convex, so the convex optimization theory can be utilized to obtain estimates, which in this paper are called Safety belt estimates. In particular, the complementary slackness condition, common in convex optimization, implies two alternative types of solutions, strongly dependent on the data and the restriction. It
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Anderson, Gerald L., and Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7585193.bard.

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This research report describes a methodology whereby multi-spectral and hyperspectral imagery from remote sensing, is used for deriving predicted field maps of selected plant growth attributes which are required for precision cropping. A major task in precision cropping is to establish areas of the field that differ from the rest of the field and share a common characteristic. Yield distribution f maps can be prepared by yield monitors, which are available for some harvester types. Other field attributes of interest in precision cropping, e.g. soil properties, leaf Nitrate, biomass etc. are ob
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