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Добірка наукової літератури з теми "Least Square Regression Method"

Автор: Grafiati

Опубліковано: 10 січня 2023

Оновлено: 28 січня 2023

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Статті в журналах з теми "Least Square Regression Method"

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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Frank, Ildiko E. "Intermediate least squares regression method." Chemometrics and Intelligent Laboratory Systems 1, no. 3 (July 1987): 233–42. http://dx.doi.org/10.1016/0169-7439(87)80067-9.

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Abdi, Hamdan, Sajaratud Dur, Rina Widyasar, and Ismail Husein. "Analysis of Efficiency of Least Trimmed Square and Least Median Square Methods in The Estimation of Robust Regression Parameters." ZERO: Jurnal Sains, Matematika dan Terapan 4, no. 1 (August 16, 2020): 21. http://dx.doi.org/10.30829/zero.v4i1.7933.

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<span lang="EN">Robust regression is a regression method used when the remainder's distribution is not reasonable, or there is an outreach to observational data that affects the model. One method for estimating regression parameters is the Least Squares Method (MKT). The method is easily affected by the presence of outliers. Therefore we need an alternative method that is robust to the presence of outliers, namely robust regression. Methods for estimating robust regression parameters include Least Trimmed Square (LTS) and Least Median Square (LMS). These methods are estimators with high breakdown points for outlier observational data and have more efficient algorithms than other estimation methods. This study aims to compare the regression models formed from the LTS and LMS methods, determine the efficiency of the model formed, and determine the factors that influence the production of community oil palm in Langkat District in 2018. The results showed that in testing, the estimated model of the regression parameters showed the same results. Compared to the efficiency estimator and the error square value, it was concluded that the LTS method was more efficient. Variable land area and productivity influence the production of palm oil smallholders in Langkat District in 2018. as well as the comparison of the efficiency estimator and the error square value, it was concluded that the LTS method was more efficient. Variable land area and productivity are factors that influence the production of palm oil smallholders in Langkat District in 2018. as well as the comparison of the efficiency estimator and the error square value, it was concluded that the LTS method was more efficient. Variable land area and productivity are factors that influence the production of palm oil smallholders in Langkat District in 2018</span>

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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 coefficient and the fuzzy degree function determination method by using the least square method, studies the imitation degree question between the observed value and the forecast value.

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Zhu, Xue Jun, and Zhi Wen Zhu. "Modeling of Piezoelectric Ceramics Based on Partial Least-Square Regression Method." Advanced Materials Research 139-141 (October 2010): 13–16. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.13.

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In this paper, a kind of piezoelectric ceramics model based on hysteretic nonlinear theory has been developed. Van de Pol nonlinear difference item was introduced to interpret the hysteresis phenomenon of the voltage-strain curve of piezoelectric ceramics. The coupling relationship between voltage and stress was obtained in partial least-square regression method to describe the driftage phenomenon of the voltage-strain curve in different stress. Based on above, the final relationship among strain, stress and voltage was set up. The results of significance test showed that the new model could describe the hysteresis characteristics of piezoelectric ceramics in different stress well. The new piezoelectric ceramics model considers the effect of stress, and is easy to be analyzed in theory, which is helpful to vibration control.

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Shankar, S. Vishnu, G. Padmalakshmi, and M. Radha. "Estimation and Comparison of Support Vector Regression with Least Square Method." International Journal of Current Microbiology and Applied Sciences 8, no. 02 (February 10, 2019): 1186–91. http://dx.doi.org/10.20546/ijcmas.2019.802.137.

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Dimovski, Marko. "REGULARIZED LEAST-SQUARE OPTIMIZATION METHOD FOR VARIABLE SELECTION IN REGRESSION MODELS." Математички билтен/BULLETIN MATHÉMATIQUE DE LA SOCIÉTÉ DES MATHÉMATICIENS DE LA RÉPUBLIQUE MACÉDOINE, no. 1 (2017): 80–100. http://dx.doi.org/10.37560/matbil11700080d.

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Su, Moting, Zongyi Zhang, Ye Zhu, and Donglan Zha. "Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm." Energies 12, no. 6 (March 21, 2019): 1094. http://dx.doi.org/10.3390/en12061094.

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Natural gas is often described as the cleanest fossil fuel. The consumption of natural gas is increasing rapidly. Accurate prediction of natural gas spot prices would significantly benefit energy management, economic development, and environmental conservation. In this study, the least squares regression boosting (LSBoost) algorithm was used for forecasting natural gas spot prices. LSBoost can fit regression ensembles well by minimizing the mean squared error. Henry Hub natural gas spot prices were investigated, and a wide range of time series from January 2001 to December 2017 was selected. The LSBoost method is adopted to analyze data series at daily, weekly and monthly. An empirical study verified that the proposed prediction model has a high degree of fitting. Compared with some existing approaches such as linear regression, linear support vector machine (SVM), quadratic SVM, and cubic SVM, the proposed LSBoost-based model showed better performance such as a higher R-square and lower mean absolute error, mean square error, and root-mean-square error.

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Nurbaroqah, Ana, Budi Pratikno, and Supriyanto Supriyanto. "PENDEKATAN REGRESI ROBUST DENGAN FUNGSI PEMBOBOT BISQUARE TUKEY PADA ESTIMASI-M DAN ESTIMASI-S." Jurnal Ilmiah Matematika dan Pendidikan Matematika 14, no. 1 (June 30, 2022): 19. http://dx.doi.org/10.20884/1.jmp.2022.14.1.5669.

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Least Square Method is one of methods for estimating of parameters of regression model. Model of least square methods is not valid if there are some disobeydiance in classical assumptions, for example, there are outliers. To resolve the problem, robust regression method is usually used. The method is used because it can detect the outliers and give stable results. In this research, data used is data for human development index of districts in Central Java from 2019 to 2020. Estimation for robust regression method chosen is estimation-M and estimation-s with Tukey Bisquare as a weight function. Criterions for choosing the best model are based on adjusted R-Squared value and mean square error (MSE). The result shows that robust regression model with estimation-M is a better model since it has adjusted R-Squared value tending to one and the least MSE.

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Choi, Seung Hoe, and Jin Hee Yoon. "General fuzzy regression using least squares method." International Journal of Systems Science 41, no. 5 (May 2010): 477–85. http://dx.doi.org/10.1080/00207720902774813.

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Дисертації з теми "Least Square Regression Method"

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 analyzing complicated data sets. Despite the success, NMF and NTF have been actively developed only in the recent decade, and algorithmic strategies for computing NMF and NTF have not been fully studied. In this thesis, computational challenges regarding NMF, NTF, and related least squares problems are addressed. First, efficient algorithms of NMF and NTF are investigated based on a connection from the NMF and the NTF problems to the nonnegativity-constrained least squares (NLS) problems. A key strategy is to observe typical structure of the NLS problems arising in the NMF and the NTF computation and design a fast algorithm utilizing the structure. We propose an accelerated block principal pivoting method to solve the NLS problems, thereby significantly speeding up the NMF and NTF computation. Implementation results with synthetic and real-world data sets validate the efficiency of the proposed method. In addition, a theoretical result on the classical active-set method for rank-deficient NLS problems is presented. Although the block principal pivoting method appears generally more efficient than the active-set method for the NLS problems, it is not applicable for rank-deficient cases. We show that the active-set method with a proper starting vector can actually solve the rank-deficient NLS problems without ever running into rank-deficient least squares problems during iterations. Going beyond the NLS problems, it is presented that a block principal pivoting strategy can also be applied to the l1-regularized linear regression. The l1-regularized linear regression, also known as the Lasso, has been very popular due to its ability to promote sparse solutions. Solving this problem is difficult because the l1-regularization term is not differentiable. A block principal pivoting method and its variant, which overcome a limitation of previous active-set methods, are proposed for this problem with successful experimental results. Finally, a group-sparsity regularization method for NMF is presented. A recent challenge in data analysis for science and engineering is that data are often represented in a structured way. In particular, many data mining tasks have to deal with group-structured prior information, where features or data items are organized into groups. Motivated by an observation that features or data items that belong to a group are expected to share the same sparsity pattern in their latent factor representations, We propose mixed-norm regularization to promote group-level sparsity. Efficient convex optimization methods for dealing with the regularization terms are presented along with computational comparisons between them. Application examples of the proposed method in factor recovery, semi-supervised clustering, and multilingual text analysis are presented.

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Oravcová, Lenka. "Determinanty cien automobilov." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-205901.

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The aim of the thesis Determinants of car prices is to create econometric model for price predictions of new and used cars. The prediction is based on the data provided by website of Slovak retailer of new and used cars. The model should detect statistically significant variables and determine their impact on final price. In the first part of this study, there is theoretical description of automobile industry and factors influencing price of car. The second part is devoted on developing the predictive model, suitable transformation of explanatory variables, interpretation of results and the car price classification in form of decision tree.

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Tang, Tian. "Infrared Spectroscopy in Combination with Advanced Statistical Methods for Distinguishing Viral Infected Biological Cells." Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/math_theses/59.

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Fourier Transform Infrared (FTIR) microscopy is a sensitive method for detecting difference in the morphology of biological cells. In this study FTIR spectra were obtained for uninfected cells, and cells infected with two different viruses. The spectra obtained are difficult to discriminate visually. Here we apply advanced statistical methods to the analysis of the spectra, to test if such spectra are useful for diagnosing viral infections in cells. Logistic Regression (LR) and Partial Least Squares Regression (PLSR) were used to build models which allow us to diagnose if spectral differences are related to infection state of the cells. A three-fold, balanced cross-validation method was applied to estimate the shrinkages of the area under the receiving operator characteristic curve (AUC), and specificities at sensitivities of 95%, 90% and 80%. AUC, sensitivity and specificity were used to gauge the goodness of the discrimination methods. Our statistical results shows that the spectra associated with different cellular states are very effectively discriminated. We also find that the overall performance of PLSR is better than that of LR, especially for new data validation. Our analysis supports the idea that FTIR microscopy is a useful tool for detection of viral infections in biological cells.

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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 data and the results are presented

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Potůčková, Lenka. "Detekce odlehlých a vlivných pozorování v lineární regresi v rámci metody nejmenších čtverců. Kvalitativní porovnání s postupy založenými na robustní regresi." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-165078.

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This Thesis deals with the methods for detection of the outliers and influential points based on method of least squares. The first part of the thesis summarizes the teoretical findings of the method of least squares and both methods for detection of the outliers and influential points based on the method of least squares and also based on robust regression. The practical part of this thesis deals with the application of classic methods for detection of the outliers and influential points on three types of datasets (artifical data, data from specialized literature and real data). The results of the application are subject to qualitative comparisson with the results produced by the methods for detection of the outliers and influentials point based on the robust regression.

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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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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) - Universidade Federal de Goiás, Jataí, 2017. Certo: FERREIRA, Wellington Vieira. Regressão linear simples aplicado na física experimental do ensino médio. 2017. 92 f. Dissertação ( Mestrado em Matemática em Rede Nacional) - Universidade Federal de Goiás, Jataí, 2017. on 2017-10-03T13:18:55Z (GMT)
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In this work we approached an interdisciplinary proposal between mathematics and physics, from the mathematical modeling of some basic physics experiments, using the Least Square Method and QtiPlot software.
Neste trabalho abordamos uma proposta interdisciplinar entre a Matemática e a Física, a partir da modelagem matemática de alguns experimentos de física básica, utilizando o Método dos Mínimos Quadrados e o software QtiPlot.

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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 variables on market price. This is accomplished by model approach using hedonic pricing method, which is used to estimate the economic value of particular attribute. Surprisingly, our data indicates the negative impact of sharing economy indicator on price. The set of motivations of consumers, which determine their valuation of Airbnb listings, was identified. The trade-off between encompass and parsimony of the set was desired in order to build an effective model. Calculation of proportion of explained variance showed that the price is affected mainly by number of accommodated persons, degree of privacy, number of bedrooms, cancellation policy, distance from the city centre and sharing economy indicator in decreasing order.

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Luo, Shan. "Advanced Statistical Methodologies in Determining the Observation Time to Discriminate Viruses Using FTIR." Digital Archive @ GSU, 2009. http://digitalarchive.gsu.edu/math_theses/86.

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Fourier transform infrared (FTIR) spectroscopy, one method of electromagnetic radiation for detecting specific cellular molecular structure, can be used to discriminate different types of cells. The objective is to find the minimum time (choice among 2 hour, 4 hour and 6 hour) to record FTIR readings such that different viruses can be discriminated. A new method is adopted for the datasets. Briefly, inner differences are created as the control group, and Wilcoxon Signed Rank Test is used as the first selecting variable procedure in order to prepare the next stage of discrimination. In the second stage we propose either partial least squares (PLS) method or simply taking significant differences as the discriminator. Finally, k-fold cross-validation method is used to estimate the shrinkages of the goodness measures, such as sensitivity, specificity and area under the ROC curve (AUC). There is no doubt in our mind 6 hour is enough for discriminating mock from Hsv1, and Coxsackie viruses. Adeno virus is an exception.

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Muratori, Giacomo. "Application of multivariate statistical methods to the modelling of a flue gas treatment stage in a waste-to-energy plant." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/17262/.

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Among all the flue gas components produced in waste-to-energy plants, acid airborne pollutants such as SO2 and HCl have the most rigorous emission standards provided by the European Parliament. Their removal is thus a key step of the flue gas treatment which is mainly achieved with the Dry Treatment Systems (DTS), technologies based on the direct injection of dry solid sorbents which is capable to subtract the acid from the gas stream with several important advantages and high removal efficiencies. However, the substantial lack of a deeper industrial knowledge makes difficult to determine accurately an optimal operating zone which should be required for the design and operation of these systems. The aim of this study has been therefore the exploration, while basing on an essential engineering expertise, of some of the possible solutions which the application of multivariate statistical methods on process data obtained from real plants can give in order to identify all those phenomena which rule dry treatment systems. In particular, a key task of this work has been the seeking for a general procedure which can be possibly applied for the characterization of any type of DTS system, regardless of the specific duty range or design configuration. This required to overcome the simple mechanical application of the available techniques and made necessary to tailor and even redefine some of the available standard procedures in order to guarantee specific and objective results for the studied case. Specifically, in this so called chemometric analysis, after a pre-treatment and quality assessment, the process data obtained from a real working plant was analyzed with basic and advanced techniques in order to characterize the relations among all the available variables. Then, starting from the results of the data analysis, a linear model has been produced in order to be employed to predict with a certain grade of accuracy the operating conditions of the system.

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Ramalho, Guilherme Matiussi. "Uma abordagem estatística para o modelo do preço spot da energia elétrica no submercado sudeste/centro-oeste brasileiro." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/3/3139/tde-26122014-145848/.

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O objetivo deste trabalho e o desenvolvimento de uma ferramenta estatistica que sirva de base para o estudo do preco spot da energia eletrica do subsistema Sudeste/Centro-Oeste do Sistema Interligado Nacional, utilizando a estimacao por regressao linear e teste de razao de verossimilhanca como instrumentos para desenvolvimento e avaliacao dos modelos. Na analise dos resultados estatsticos descritivos dos modelos, diferentemente do que e observado na literatura, a primeira conclusao e a verificacao de que as variaveis sazonais, quando analisadas isoladamente, apresentam resultados pouco aderentes ao preco spot PLD. Apos a analise da componente sazonal e verificada a influencia da energia fornecida e a energia demandada como variaveis de entrada, com o qual conclui-se que especificamente a energia armazenada e producao de energia termeletrica sao as variaveis que mais influenciam os precos spot no subsistema estudado. Entre os modelos testados, o que particularmente ofereceu os melhores resultados foi um modelo misto criado a partir da escolha das melhores variaveis de entrada dos modelos testados preliminarmente, alcancando um coeficiente de determinacao R2 de 0.825, resultado esse que pode ser considerado aderente ao preco spot. No ultimo capitulo e apresentada uma introducao ao modelo de predicao do preco spot, possibilitando dessa forma a analise do comportamento do preco a partir da alteracao das variaveis de entrada.
The objective of this work is the development of a statistical method to study the spot prices of the electrical energy of the Southeast/Middle-West (SE-CO) subsystem of the The Brazilian National Connected System, using the Least Squares Estimation and Likelihood Ratio Test as tools to perform and evaluate the models. Verifying the descriptive statistical results of the models, differently from what is observed in the literature, the first observation is that the seasonal component, when analyzed alone, presented results loosely adherent to the spot price PLD. It is then evaluated the influence of the energy supply and the energy demand as input variables, verifying that specifically the stored water and the thermoelectric power production are the variables that the most influence the spot prices in the studied subsystem. Among the models, the one that offered the best result was a mixed model created from the selection of the best input variables of the preliminarily tested models, achieving a coeficient of determination R2 of 0.825, a result that can be considered adherent to the spot price. At the last part of the work It is presented an introduction to the spot price prediction model, allowing the analysis of the price behavior by the changing of the input variables.

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Книги з теми "Least Square Regression Method"

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

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Tiedeman, Claire R. Application of nonlinear-regression methods to a ground-water flow model of the Albuquerque Basin, New Mexico. Menlo Park, Calif: U.S. Dept. of the Interior, U.S. Geological Survey, 1998.

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

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Yang, Keming, ed. Categorical Data Analysis. Los Angeles, USA: SAGE Publications Ltd, 2014.

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Частини книг з теми "Least Square Regression Method"

Abdi, Hervé, and Lynne J. Williams. "Partial Least Squares Methods: Partial Least Squares Correlation and Partial Least Square Regression." In Methods in Molecular Biology, 549–79. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-059-5_23.

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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, 33–93. Cham: 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 square loss function and a regularization term. This latter can take various forms, leading to several variants which have been applied extensively in theory as well as in practical applications. In this chapter, we will focus on these methods and introduce their fundamentals. In the first part of the appendix to this chapter, we also report some basic results of linear algebra useful for the reading.

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Roe, Byron P. "Method of Least Squares (Regression Analysis)." In Probability and Statistics in the Physical Sciences, 129–43. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53694-7_13.

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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, 255–81. Heidelberg: Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1800-0_17.

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Wen, Xiangjun, Xiaoming Xu, and Yunze Cai. "Least-Squares Wavelet Kernel Method for Regression Estimation." In Lecture Notes in Computer Science, 582–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539087_74.

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Wang, Jing, Jinglin Zhou, and Xiaolu Chen. "Locality-Preserving Partial Least Squares Regression." In Intelligent Control and Learning Systems, 173–88. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8044-1_10.

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AbstractThis chapter proposes another nonlinear PLS method, named as locality-preserving partial least squares (LPPLS), which embeds the nonlinear degenerative and structure-preserving properties of LPP into the PLS model. The core of LPPLS is to replace the role of PCA in PLS with LPP. When extracting the principal components of $$\boldsymbol{t}_i$$ t i and $$\boldsymbol{u}_i$$ u i , two conditions must satisfy: (1) $$\boldsymbol{t}_i$$ t i and $$\boldsymbol{u}_i$$ u i retain the most information about the local nonlinear structure of their respective data sets. (2) The correlation between $$\boldsymbol{t}_i$$ t i and $$\boldsymbol{u}_i$$ u i is the largest. Finally, a quality-related monitoring strategy is established based on LPPLS.

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Guo, Yang, Jiang Cao, Qi Ouyang, and Shaochi Cheng. "Weighted Least Square Support Vector Regression Method with GGP-Based Sequential Sampling." In Lecture Notes in Electrical Engineering, 212–19. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0187-6_24.

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Van Keilegom, Ingrid, and Michael G. Akritas. "The Least Squares Method in Heteroscedastic Censored Regression Models." In Asymptotics in Statistics and Probability, edited by Madan L. Puri, 379–92. Berlin, Boston: De Gruyter, 2000. http://dx.doi.org/10.1515/9783110942002-026.

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Stanojević, Bogdana, and Milan Stanojević. "Extension-Principle-Based Approach to Least Square Fuzzy Linear Regression." In Intelligent Methods Systems and Applications in Computing, Communications and Control, 219–28. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16684-6_18.

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Kusuoka, Shigeo, and Yusuke Morimoto. "Least Square Regression Methods for Bermudan Derivatives and Systems of Functions." In Advances in Mathematical Economics, 57–89. Tokyo: Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-55489-9_3.

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Тези доповідей конференцій з теми "Least Square Regression Method"

Chen, Huali, and Peng Wang. "A sampling method of dependent variable for least square regression." In International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), edited by Ke Chen, Nan Lin, Romeo Meštrović, Teresa A. Oliveira, Fengjie Cen, and Hong-Ming Yin. SPIE, 2022. http://dx.doi.org/10.1117/12.2627638.

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Kakkar, Deepti, Aditi Bharmaik, Ankita Sharma, Eshwari S. S. Dagar, Parul Rattanpal, and Shefali Sharma. "Hata Model Path Loss Optimization using Least Mean Square Regression." In International Conference on Women Researchers in Electronics and Computing. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.114.34.

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Astochastic approach based optimization technique to optimize the Hata model path loss equation is presented in this paper. In this paper, the existing Hata model equation for determining path loss in medium urban city is optimized using Least Mean Square regression method. Out of various path loss models available, Hata model was chosen due to its accuracy and reliability in an urban propagation environment. The optimization technique proposed is applied to get the optimumcoefficients of Hata propagation model equation. This stochastic approach is based on reducing the mean square difference between the measured and predicted path loss by adjusting the error coefficients of MSE through regression. The MSE obtained after optimization is significantly lower than that obtained from the existing Hata model. For better planning and implementation of mobile cellular networks there is a need for modifying the existing path loss prediction models. This optimized model can be used to improve the quality of service in 900MHz band in a medium sized urban environment.

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Chen, Yao, Zhaoqing Song, and Zhenkai Guo. "A New Multi-class Classification Method Based On Least Square Support Vector Regression Machine." In The 5th International Conference on Computer Engineering and Networks. Trieste, Italy: Sissa Medialab, 2015. http://dx.doi.org/10.22323/1.259.0032.

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Katsuhiro Honda, Takahiro Ohyama, Hidetomo Ichihashi, and Akira Notsu. "FCM-type switching regression with alternating least squares method." In 2008 IEEE 16th International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2008. http://dx.doi.org/10.1109/fuzzy.2008.4630354.

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Si Chen, Ling Li-na, Yuan Rong-chang, and Sun Long-qing. "Classification model of seed cotton grade based on least square support vector machine regression method." In 2012 IEEE 6th International Conference on Information and Automation for Sustainability (ICIAfS). IEEE, 2012. http://dx.doi.org/10.1109/iciafs.2012.6419904.

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Yongzhong Xing, Xiaobei Wu, Zhiliang Xu, and Qi Cheng. "Least-squares auto-correlation wavelet kernel method for regression estimation." In 2008 Chinese Control and Decision Conference (CCDC). IEEE, 2008. http://dx.doi.org/10.1109/ccdc.2008.4598186.

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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. California: 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 projection matrix. The new method extends the least square regression model by rescaling the regression coefficients in the least square regression with a set of scale factors, which are used for ranking the features. It has shown that the new model can learn global and sparse solution. Moreover, the introduction of scale factors provides a theoretical explanation for why we can use the projection matrix to rank the features. A simple yet effective algorithm with proved convergence is proposed to optimize the new model. Experimental results on eight real-life data sets show the superiority of the method.

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Qu, Yonghua, Siong Jiao, and Xudong Lin. "A partial least square regression method to quantitatively retrieve soil salinity using hyper-spectral reflectance data." In Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images, edited by Lin Liu, Xia Li, Kai Liu, and Xinchang Zhang. SPIE, 2008. http://dx.doi.org/10.1117/12.813254.

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He, Xiang, Bin Jiang, Yueguang Sun, Jianjun Li, and Zemin Liu. "A Novel Direction Finding Method Based on Compressed Least-Squared Regression." In 2011 7th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2011. http://dx.doi.org/10.1109/wicom.2011.6040097.

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Zhang Yunyan, Cai Wei, Deng Chun, Xu Yonghai, and Gao Lin. "A harmonic measurement mode based on partial least-squares regression method." In Energy Conference (EPEC). IEEE, 2008. http://dx.doi.org/10.1109/epc.2008.4763384.

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Звіти організацій з теми "Least Square Regression Method"

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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Anderson, Gerald L., and Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, December 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 obtained by manual sampling of the filed in a grid pattern. Maps of various field attributes are then prepared from these samples by the "Inverse Distance" interpolation method or by Kriging. An improved interpolation method was developed which is based on minimizing the overall curvature of the resulting map. Such maps are the ground truth reference, used for training the algorithm that generates the predicted field maps from remote sensing imagery. Both the reference and the predicted maps are stratified into "Prototype Plots", e.g. 15xl5 blocks of 2m pixels whereby the block size is 30x30m. This averaging reduces the datasets to manageable size and significantly improves the typically poor repeatability of remote sensing imaging systems. In the first two years of the project we used the Normalized Difference Vegetation Index (NDVI), for generating predicted yield maps of sugar beets and com. The NDVI was computed from image cubes of three spectral bands, generated by an optically filtered three camera video imaging system. A two dimensional FFT based regression model Y=f(X), was used wherein Y was the reference map and X=NDVI was the predictor. The FFT regression method applies the "Wavelet Based", "Pixel Block" and "Image Rotation" transforms to the reference and remote images, prior to the Fast - Fourier Transform (FFT) Regression method with the "Phase Lock" option. A complex domain based map Yfft is derived by least squares minimization between the amplitude matrices of X and Y, via the 2D FFT. For one time predictions, the phase matrix of Y is combined with the amplitude matrix ofYfft, whereby an improved predicted map Yplock is formed. Usually, the residuals of Y plock versus Y are about half of the values of Yfft versus Y. For long term predictions, the phase matrix of a "field mask" is combined with the amplitude matrices of the reference image Y and the predicted image Yfft. The field mask is a binary image of a pre-selected region of interest in X and Y. The resultant maps Ypref and Ypred aremodified versions of Y and Yfft respectively. The residuals of Ypred versus Ypref are even lower than the residuals of Yplock versus Y. The maps, Ypref and Ypred represent a close consensus of two independent imaging methods which "view" the same target. In the last two years of the project our remote sensing capability was expanded by addition of a CASI II airborne hyperspectral imaging system and an ASD hyperspectral radiometer. Unfortunately, the cross-noice and poor repeatability problem we had in multi-spectral imaging was exasperated in hyperspectral imaging. We have been able to overcome this problem by over-flying each field twice in rapid succession and developing the Repeatability Index (RI). The RI quantifies the repeatability of each spectral band in the hyperspectral image cube. Thereby, it is possible to select the bands of higher repeatability for inclusion in the prediction model while bands of low repeatability are excluded. Further segregation of high and low repeatability bands takes place in the prediction model algorithm, which is based on a combination of a "Genetic Algorithm" and Partial Least Squares", (PLS-GA). In summary, modus operandi was developed, for deriving important plant growth attribute maps (yield, leaf nitrate, biomass and sugar percent in beets), from remote sensing imagery, with sufficient accuracy for precision cropping applications. This achievement is remarkable, given the inherently high cross-noice between the reference and remote imagery as well as the highly non-repeatable nature of remote sensing systems. The above methodologies may be readily adopted by commercial companies, which specialize in proving remotely sensed data to farmers.

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Puttanapong, Nattapong, Arturo M. Martinez Jr, Mildred Addawe, Joseph Bulan, Ron Lester Durante, and Marymell Martillan. Predicting Poverty Using Geospatial Data in Thailand. Asian Development Bank, December 2020. http://dx.doi.org/10.22617/wps200434-2.

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This study examines an alternative approach in estimating poverty by investigating whether readily available geospatial data can accurately predict the spatial distribution of poverty in Thailand. It also compares the predictive performance of various econometric and machine learning methods such as generalized least squares, neural network, random forest, and support vector regression. Results suggest that intensity of night lights and other variables that approximate population density are highly associated with the proportion of population living in poverty. The random forest technique yielded the highest level of prediction accuracy among the methods considered, perhaps due to its capability to fit complex association structures even with small and medium-sized datasets.

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CAPACITY EVALUATION OF EIGHT BOLT EXTENDED ENDPLATE MOMENT CONNECTIONS SUBJECTED TO COLUMN REMOVAL SCENARIO. The Hong Kong Institute of Steel Construction, September 2021. http://dx.doi.org/10.18057/ijasc.2021.17.3.6.

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The extended stiffened endplate (8ES) connection is broadly used in the seismic load-resisting parts of steel structures. This connection is prequalified based on the AISC 358 standard, especially for seismic regions. To study this connection’s behaviors, in the event of accidental loss of a column, the finite element model results were verified against the available experimental data. A parametric study using the finite element method was then carried out to investigate these numerical models’ maximum capacity and effective parameters' effect on their maximum capacity in a column loss scenario. This parametric analysis demonstrated that these connections fail at the large displacement due to the catenary action mode at the rib stiffener's vicinity. The carrying capacity, PEEQ, Von-Mises stress, middle column force-displacement, critical bolt axial load, and the beam axial load curves were discussed. Finally, using the Least Square Method (LSM), a formula is presented to determine the displacement at the maximum capacity of these connections. This formula can be used in this study's presented method to determine the maximum load capacity of the 8ES connections in a column loss scenario.

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