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Relevant bibliographies by topics / Weight Least Square Regression

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Academic literature on the topic 'Weight Least Square Regression'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Weight Least Square Regression"

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Zuo, Yijun, and Hanwen Zuo. "Weighted Least Squares Regression with the Best Robustness and High Computability." Axioms 13, no. 5 (2024): 295. http://dx.doi.org/10.3390/axioms13050295.

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A novel regression method is introduced and studied. The procedure weights squared residuals based on their magnitude. Unlike the classic least squares which treats every squared residual as equally important, the new procedure exponentially down-weights squared residuals that lie far away from the cloud of all residuals and assigns a constant weight (one) to squared residuals that lie close to the center of the squared-residual cloud. The new procedure can keep a good balance between robustness and efficiency; it possesses the highest breakdown point robustness for any regression equivariant

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Çankaya, Soner, Samet Eker, and Samet Hasan Abacı. "Comparison of Least Squares, Ridge Regression and Principal Component Approaches in the Presence of Multicollinearity in Regression Analysis." Turkish Journal of Agriculture - Food Science and Technology 7, no. 8 (2019): 1166. http://dx.doi.org/10.24925/turjaf.v7i8.1166-1172.2515.

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The aim of this study was to compare estimation methods: least squares method (LS), ridge regression (RR), Principal component regression (PCR) to estimate the parameters of multiple regression model in situations when the underlying assumptions of least squares estimation are untenable because of multicollinearity. For this aim, the effect of some body measurements on body weights (height at withers and rumps, body length, chest width, chest girth and chest depth, front, middle and hind rump width) obtained from totally 85 Karayaka lambs at weaning period raised at Research Farm of Ondokuz Ma

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Li, Yanting, Junwei Jin, Jiangtao Ma, et al. "Imbalanced least squares regression with adaptive weight learning." Information Sciences 648 (November 2023): 119541. http://dx.doi.org/10.1016/j.ins.2023.119541.

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Virgantari, Fitria, Maya Widyastiti, and Natalia Ir Seno. "Comparison of Weights in Weighted Least Square Method For Handling Heteroscedasticity on Multiple Regression Model." International Journal of Mathematics, Statistics, and Computing 2, no. 2 (2024): 60–67. http://dx.doi.org/10.46336/ijmsc.v2i2.93.

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Regression analysis is the most popular and commonly used to determine causality between two or more variables. In regression analysis there are several assumptions that must be held, so that the property of the best linear unbiased estimator (BLUE) is still guaranteed. In fact, we often found violations of the assumptions. One of them was violations of the homoscedasticity or occurs heteroscedasticity. The impact of heteroscedasticity in the regression model is that the ordinary least square (OLS) estimator no longer has a minimum variance although still linear and unbiased. To handle this, w

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Prasetya, Rizka Pradita. "Unpacking Outlier with Weight Least Square (Implemented on Pepper Plantations Data)." Parameter: Journal of Statistics 2, no. 3 (2023): 24–31. http://dx.doi.org/10.22487/27765660.2022.v2.i3.16138.

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Outliers in regression analysis can cause large residuals, the diversity of the data becomes greater, causing the data to be heterogenous. If an outlier is caused by an error in recording observations or an error in preparing equipment, the outlier can be ignored or discarded before data analysis is carried out. However, if outliers exist not because of the researcher's error, but are indeed information that cannot be provided by other data, then the outlier data cannot be ignored and must be included in data analysis. There are several methods to deal with outliers. The Weight Least Square me

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Kalina, Jan, and Jan Tichavský. "On Robust Estimation of Error Variance in (Highly) Robust Regression." Measurement Science Review 20, no. 1 (2020): 6–14. http://dx.doi.org/10.2478/msr-2020-0002.

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AbstractThe linear regression model requires robust estimation of parameters, if the measured data are contaminated by outlying measurements (outliers). While a number of robust estimators (i.e. resistant to outliers) have been proposed, this paper is focused on estimating the variance of the random regression errors. We particularly focus on the least weighted squares estimator, for which we review its properties and propose new weighting schemes together with corresponding estimates for the variance of disturbances. An illustrative example revealing the idea of the estimator to down-weight i

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Ogunmola, Adeniyi Oyewole, and Benjamin Ekene Okoye. "Application of Quantile Regression and Ordinary Least Squares Regression in Modeling Body Mass Index in Federal Medical Centre Jalingo, Nigeria." Journal of Multidisciplinary Science: MIKAILALSYS 3, no. 2 (2025): 552–58. https://doi.org/10.58578/mikailalsys.v3i2.5322.

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Body mass index is a measure of nutritional status of an individual. Malnutrition is a leading public health problem in developing countries like Nigeria, it is also a major cause of morbidity and mortality. In this study, Body mass index is modeled using ordinary least squares method and quantile regression method. Data is collected from Antiretroviral therapy Clinic in Federal Medical Centre, Jalingo. Variables in the data collected are the Body mass index, age, weight, height, sex and occupation of the patients. Results showed that the ordinary least square regression and quantile regressio

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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 (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.

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Lessman, Stefan, Ming-Chien Sung, and Johnnie E. V. Johnson. "ADAPTING LEAST-SQUARE SUPPORT VECTOR REGRESSION MODELS TO FORECAST THE OUTCOME OF HORSERACES." Journal of Prediction Markets 1, no. 3 (2012): 169–87. http://dx.doi.org/10.5750/jpm.v1i3.427.

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This paper introduces an improved approach for forecasting the outcome of horseraces. Building upon previous literature, a state-of-the-art modelling paradigm is developed which integrates least-square support vector regression and conditional logit procedures to predict horses’ winning probabilities. In order to adapt the least-square support vector regression model to this task, some free parameters have to be determined within a model selection step. Traditionally, this is accomplished by assessing candidate settings in terms of mean-squared error between estimated and actual finishing posi

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Idowu, Badmus Nofiu, and Ogundeji Rotimi Kayode. "Discriminating Between Ordinary Least Squares Estimation Method and Some Robust Estimation Regression Methods." International Journal of Computational and Applied Mathematics & Computer Science 3 (October 31, 2023): 72–79. http://dx.doi.org/10.37394/232028.2023.3.9.

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The lack of certain assumptions is common in ordinary least squares regression models whenever there is/are outliers and high leverage in the observations with an extreme value on a predictor variable. This could have a great effect on the estimate of regression coefficients. However, this research investigates the performance of the ordinary least squares estimator method and some robust regression methods which include: M-Huber, M-Bisquare, MM, and M-Hampel estimator methods. This study applies both methods to a secondary data set with 28 years (from 1900 to 2021) 200 meter races Summer Olym

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Dissertations / Theses on the topic "Weight Least Square Regression"

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Zhang, Zongjun. "Adaptive Robust Regression Approaches in data analysis and their Applications." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1445343114.

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Gaspard, Guetchine. "FLOOD LOSS ESTIMATE MODEL: RECASTING FLOOD DISASTER ASSESSMENT AND MITIGATION FOR HAITI, THE CASE OF GONAIVES." OpenSIUC, 2013. https://opensiuc.lib.siu.edu/theses/1236.

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This study aims at developing a model to estimate flood damage cost caused in Gonaives, Haiti by Hurricane Jeanne in 2004. In order to reach this goal, the influence of income, inundation duration and inundation depth, slope, population density and distance to major roads on the loss costs was investigated. Surveyed data were analyzed using Excel and ArcGIS 10 software. The ordinary least square and the geographically weighted regression analyses were used to predict flood damage costs. Then, the estimates were delineated using voronoi geostatistical map tool. As a result, the factors account

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Shulga, Yelena A. "Model-based calibration of a non-invasive blood glucose monitor." Digital WPI, 2006. https://digitalcommons.wpi.edu/etd-theses/58.

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This project was dedicated to the problem of improving a non-invasive blood glucose monitor being developed by the VivaScan Corporation. The company has made some progress in the non-invasive blood glucose device development and approached WPI for a statistical assistance in the improvement of their model in order to predict the glucose level more accurately. The main goal of this project was to improve the ability of the non-invasive blood glucose monitor to predict the glucose values more precisely. The goal was achieved by finding and implementing the best regression model. The methods inc

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Deilami, Kaveh. "Modelling the urban heat island intensities of alternative urban growth management policies in Brisbane." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/107656/1/Kaveh_Deilami_Thesis.pdf.

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When urban areas experience higher temperature than their surrounding rural areas, this phenomenon is called the urban heat island (UHI) effect. UHI contributes to global warming. Urban planning policy plays a significant role in controlling the UHI. This study examines the UHI effects of urban planning policy scenarios for Brisbane, including: a) business as usual; b) transit oriented development; c) infill development; d) motorway oriented development; and e) sprawl development. The findings show Infill development will be effective but will generate pockets of extreme UHI. Sprawl developmen

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Rosopa, Patrick. "A COMPARISON OF ORDINARY LEAST SQUARES, WEIGHTED LEAST SQUARES, AND OTHER PROCEDURES WHEN TESTING FOR THE EQUALITY OF REGRESSION." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2311.

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When testing for the equality of regression slopes based on ordinary least squares (OLS) estimation, extant research has shown that the standard F performs poorly when the critical assumption of homoscedasticity is violated, resulting in increased Type I error rates and reduced statistical power (Box, 1954; DeShon & Alexander, 1996; Wilcox, 1997). Overton (2001) recommended weighted least squares estimation, demonstrating that it outperformed OLS and performed comparably to various statistical approximations. However, Overton's method was limited to two groups. In this study, a generalization

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Zhang, Desheng. "The Effect of Psychometric Parallelism among Predictors on the Efficiency of Equal Weights and Least Squares Weights in Multiple Regression." Thesis, University of North Texas, 1996. https://digital.library.unt.edu/ark:/67531/metadc278996/.

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There are several conditions for applying equal weights as an alternative to least squares weights. Psychometric parallelism, one of the conditions, has been suggested as a necessary and sufficient condition for equal-weights aggregation. The purpose of this study is to investigate the effect of psychometric parallelism among predictors on the efficiency of equal weights and least squares weights. Target correlation matrices with 10,000 cases were simulated so that the matrices had varying degrees of psychometric parallelism. Five hundred samples with six ratios of observation to predictor = 5

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Can, Mutan Oya. "Comparison Of Regression Techniques Via Monte Carlo Simulation." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/3/12605175/index.pdf.

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The ordinary least squares (OLS) is one of the most widely used methods for modelling the functional relationship between variables. However, this estimation procedure counts on some assumptions and the violation of these assumptions may lead to nonrobust estimates. In this study, the simple linear regression model is investigated for conditions in which the distribution of the error terms is Generalised Logistic. Some robust and nonparametric methods such as modified maximum likelihood (MML), least absolute deviations (LAD), Winsorized least squares, least trimmed squares (LTS), Theil and wei

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Tano, Kent. "Multivariate modelling and monitoring of mineral processes using partial least square regression." Licentiate thesis, Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, 1996. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-16872.

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Li, Ying. "A Comparison Study of Principle Component Regression, Partial Least Square Regression and Ridge Regression with Application to FTIR Data." Thesis, Uppsala University, Department of Statistics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-127983.

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<p>Least squares estimator may fail when the number of explanatory vari-able is relatively large in comparison to the sample or if the variablesare almost collinear. In such a situation, principle component regres-sion, partial least squares regression and ridge regression are oftenproposed methods and widely used in many practical data analysis,especially in chemometrics. They provide biased coecient estima-tors with the relatively smaller variation than the variance of the leastsquares estimator. In this paper, a brief literature review of PCR,PLS and RR is made from a theoretical perspectiv

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Zheng, Shimin, and A. K. Gupta. "A New Approach to Statistical Efficiency of Weighted Least Squares Fitting Algorithms for Reparameterization of Nonlinear Regression Models." Digital Commons @ East Tennessee State University, 2012. https://dc.etsu.edu/etsu-works/36.

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We study nonlinear least-squares problem that can be transformed to linear problem by change of variables. We derive a general formula for the statistically optimal weights and prove that the resulting linear regression gives an optimal estimate (which satisfies an analogue of the Rao–Cramer lower bound) in the limit of small noise.

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Books on the topic "Weight Least Square Regression"

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Toutenburg, Helge. MSE-comparisons between restricted least squares, mixed, and weighted mixed estimators with special emphasize [i.e. emphasis] to nested restrictions. Akademie der Wissenschaften der DDR, Karl-Weierstrass-Institut für Mathematik, 1988.

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O. Görgülü and A. Akilli. Egg production curve fitting using least square support vector machines and nonlinear regression analysis. Verlag Eugen Ulmer, 2018. http://dx.doi.org/10.1399/eps.2018.235.

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Hall, Peter. Principal component analysis for functional data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.8.

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This article discusses the methodology and theory of principal component analysis (PCA) for functional data. It first provides an overview of PCA in the context of finite-dimensional data and infinite-dimensional data, focusing on functional linear regression, before considering the applications of PCA for functional data analysis, principally in cases of dimension reduction. It then describes adaptive methods for prediction and weighted least squares in functional linear regression. It also examines the role of principal components in the assessment of density for functional data, showing how

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Cardot, Hervé, and Pascal Sarda. Functional Linear Regression. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.2.

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This article presents a selected bibliography on functional linear regression (FLR) and highlights the key contributions from both applied and theoretical points of view. It first defines FLR in the case of a scalar response and shows how its modelization can also be extended to the case of a functional response. It then considers two kinds of estimation procedures for this slope parameter: projection-based estimators in which regularization is performed through dimension reduction, such as functional principal component regression, and penalized least squares estimators that take into account

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Halperin, Sandra, and Oliver Heath. 16. Patterns of Association. Oxford University Press, 2017. http://dx.doi.org/10.1093/hepl/9780198702740.003.0016.

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This chapter discusses the principles of bivariate analysis as a tool for helping researchers get to know their data and identify patterns of association between two variables. Bivariate analysis offers a way of establishing whether or not there is a relationship between two variables, a dependent variable and an independent variable. With bivariate analysis, theoretical expectations can be compared against evidence from the real world to see if the theory is supported by what is observed. The chapter examines the pattern of association between dependent and independent variables, with particu

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Book chapters on the topic "Weight Least Square Regression"

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Wen, Wen, Zhifeng Hao, Zhuangfeng Shao, Xiaowei Yang, and Ming Chen. "A Heuristic Weight-Setting Algorithm for Robust Weighted Least Squares Support Vector Regression." In Neural Information Processing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11893028_86.

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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. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0187-6_24.

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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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Dukalang, Hendra H., Joko Purwadi, Sukma Adi Perdana, and Setia Ningsih. "Recovery Rate of Pulmonary Tuberculosis Patients Using 2-Parameter Gamma Regression Model with Weighted Least Square Approach." In Advances in Social Science, Education and Humanities Research. Atlantis Press SARL, 2025. https://doi.org/10.2991/978-2-38476-410-5_6.

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Guo, Yang, Nan-nan Wang, and Gen-shen Kai. "A Weighted Least Square Support Vector Regression Method with MPP-GGP Based Sequential Sampling for Efficient Reliability Analysis." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8599-9_20.

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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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Mašíček, L. "Consistency of the Least Weighted Squares Regression Estimator." In Theory and Applications of Recent Robust Methods. Birkhäuser Basel, 2004. http://dx.doi.org/10.1007/978-3-0348-7958-3_17.

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Beran, Rudolf. "Multivariate regression through affinely weighted penalized least squares." In Institute of Mathematical Statistics Collections. Institute of Mathematical Statistics, 2013. http://dx.doi.org/10.1214/12-imscoll904.

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Ruby-Figueroa, René. "Partial Least Square Regression (PLSR)." In Encyclopedia of Membranes. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-40872-4_2000-1.

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Wickramasingha, Ishan M., Biniyam K. Mezgebo, and Sherif S. Sherif. "Weighted Tensor Least Angle Regression for Solving Sparse Weighted Multilinear Least Squares Problems." In New Approaches for Multidimensional Signal Processing. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0109-4_3.

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Conference papers on the topic "Weight Least Square Regression"

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Duffo, Loïc, Ivar Eskerud Smith, and Jørn Kjølaas. "A 2-Parameters Weight Definition in Least-Squares Regression Towards Data Fitting with Uncertainty." In 2024 10th International Conference on Optimization and Applications (ICOA). IEEE, 2024. http://dx.doi.org/10.1109/icoa62581.2024.10754381.

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Yang, Xiaorong, and Ke-Ang Fu. "Copy number detection using self-weighted least square regression." In 2011 IEEE International Conference on Systems Biology (ISB). IEEE, 2011. http://dx.doi.org/10.1109/isb.2011.6033119.

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Chamidah, Nur, Budi Lestari, Anies Y. Wulandari, and Lailatul Muniroh. "Z-score standard growth chart design of toddler weight using least square spline semiparametric regression." In INTERNATIONAL CONFERENCE ON MATHEMATICS, COMPUTATIONAL SCIENCES AND STATISTICS 2020. AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0042285.

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Wen, Wen, Zhi-feng Hao, Zhuang-feng Shao, and Xiao-wei Yang. "A Kernel-Based Weight-Setting Method in Robust Weighted Least Squares Support Vector Regression." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258944.

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Yeo, Wan Sieng. "Yellowness index prediction using locally weighted kernel partial least square regression model." In MATERIALS V INTERNATIONAL YOUTH APPLIED RESEARCH FORUM “OIL CAPITAL”: Conference Series “OIL CAPITAL”. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0165220.

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Soemartojo, Saskya Mary, Rima Dini Ghaisani, Titin Siswantining, Mariam Rahmania Shahab, and Moch Muchid Ariyanto. "Parameter estimation of geographically weighted regression (GWR) model using weighted least square and its application." In Proceedings of the 17th International Conference on Ion Sources. Author(s), 2018. http://dx.doi.org/10.1063/1.5054485.

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Malik, B., and M. Benaissa. "Determination of glucose concentration from near-infrared spectra using locally weighted partial least square regression." In 2012 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2012. http://dx.doi.org/10.1109/embc.2012.6347402.

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Xiaoya Yu, Huanbo Wang, Yujun Zhang, et al. "Component Recognition of 3D Fluorescence Spectra based on Wavelet Analysis and Weighted Least Square Regression." In IET International Conference on Information Science and Control Engineering 2012 (ICISCE 2012). Institution of Engineering and Technology, 2012. http://dx.doi.org/10.1049/cp.2012.2301.

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Kuzairi, N. Chamidah, and I. N. Budiantara. "Theoretical Study of Fourier Series Estimator in Semiparametric Regression for Longitudinal Data Based on Weighted Least Square Optimization." In 1st International Multidisciplinary Conference on Education, Technology, and Engineering (IMCETE 2019). Atlantis Press, 2020. http://dx.doi.org/10.2991/assehr.k.200303.064.

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Erazo, Maria P., William Singer, Nick Lord, Maria L. Rosso, and Bo Zhang. "Development of a Near-infrared Spectroscopy Calibration Model to Predict Methionine Content in Whole Soybeans." In 2022 AOCS Annual Meeting & Expo. American Oil Chemists' Society (AOCS), 2022. http://dx.doi.org/10.21748/zusl9413.

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Soybean is an important source of high-quality protein for humans and livestock. However, the content of methionine, a sulfur-containing essential amino acid, is deficient and inadequate for consumer needs. Amino acid evaluation can be performed by conventional wet chemical techniques like high-performance liquid chromatography (HPLC). To ease out the need for complex laboratory infrastructure and highly skilled experts often required by these procedures, HPLC datasets can be paired with data obtained from Near-infrared (NIR) reflectance spectroscopy. Through this approach, spectral data can b

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Reports on the topic "Weight Least Square Regression"

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Carroll, Raymond J., Marie Davidian, and David Ruppert. Estimating Weights in Heteroscedastic Regression Models by Applying Least Squares to Squared or Absolute Residuals. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada168487.

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สุกมลสันต์, สุพัฒน์. การวิเคราะห์วิถีสัมพันธ์ของตัวประกอบที่มีผลต่อสัมฤทธิผล ในการเรียนภาษาอังกฤษของนิสิตชั้นปีที่ 2 : รายงานการวิจัย. จุฬาลงกรณ์มหาวิทยาลัย, 1991. https://doi.org/10.58837/chula.res.1991.32.

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วัตถุประสงค์สำคัญในการศึกษาครั้งนี้เพื่อวิเคราะห์วิถีสัมพันธ์ของตัวประกอบจำนวนหนึ่งที่คาดว่าจะมีผลต่อสัมฤทธิผลในการเรียนภาษาอังกฤษของนิสิตชั้นปีที่ 2 และเพื่อสร้างรูปแบบความสัมพันธ์ของตัวประกอบดังกล่าวแล้ว พลวิจัยครั้งนี้ได้แก่ตัวอย่างนิสิตชั้นปีที่ 2 จาก 5 คณะของจุฬาลงกรณ์มหาวิทยาลัย ที่เรียนรายวิชา EAP II ในภาคปลาย ปีการศึกษา 2532 จำนวน 460 คน จากประชากรทั้งหมด 1,006 คนหรือร้อยละ 45.72 ซึ่งได้จากการสุ่มหลายระดับชั้น เครื่องมือในการวิจัยได้แก่แบบทดสอบสัมฤทธิผลที่ใช้จริงในการเรียนรายวิชา EAP II แบบสอบถามเกี่ยวกับข้อมูลพื้นฐาน แรงจูงใจ และเจตคติของนิสิต แบบสำรวจนิสัยและเจตคติในการเรียน และแบบสอ

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Over, Thomas, Riki Saito, Andrea Veilleux, et al. Estimation of Peak Discharge Quantiles for Selected Annual Exceedance Probabilities in Northeastern Illinois. Illinois Center for Transportation, 2016. http://dx.doi.org/10.36501/0197-9191/16-014.

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This report provides two sets of equations for estimating peak discharge quantiles at annual exceedance probabilities (AEPs) of 0.50, 0.20, 0.10, 0.04, 0.02, 0.01, 0.005, and 0.002 (recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively) for watersheds in Illinois based on annual maximum peak discharge data from 117 watersheds in and near northeastern Illinois. One set of equations was developed through a temporal analysis with a two-step least squares-quantile regression technique that measures the average effect of changes in the urbanization of the watersheds used i

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สิริภัทราวรรณ, อุบลรัตน์, та สุวัสสา พงษ์อำไพ. การประเมินอายุการเก็บแบบรวดเร็วของผลิตภัณฑ์อาหารแปรรูปโดยใช้ NIR specytoscopy และ Chemometrics : รายงานการวิจัย. จุฬาลงกรณ์มหาวิทยาลัย, 2014. https://doi.org/10.58837/chula.res.2014.58.

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งานวิจัยนี้พัฒนาการประเมินคุณภาพและอายุการเก็บของผลิตภัณฑ์อาหารแปรรูปพร้อมบริโภค ด้วยวิธี แบบรวดเร็วโดยใช้ NIR spectroscopy ร่วมกับ chemometrics ทำโดยเตรียมผลิตภัณฑ์ไส้กรอกหมูบรรจุใน ถุงพลาสติกภายใต้ภาวะสุญญากาศและเก็บรักษาที่อุณหภูมิ 4 °C ติดตามการเปลี่ยนแปลงคุณภาพทางเคมี (ค่า pH) ทางกายภาพ (ค่าแรงตัดขาด และ ค่าสี) ทางจุลินทรีย์ (จำนวนแบคทีเรียทั้ง หมด และ แบคทีเรียแลกติก) และทาง ประสาทสัมผัส (odor, color, appearance และ overall acceptability) ของผลิตภัณฑ์ในระหว่างการเก็บรักษา รวมทั้งวิเคราะห์โดยใช้ near infrared spectroscopy จากผลการทดลองพบว่า ผลิตภัณฑ์มีค่า pH ค่าแรงตัดขาด ค่าสี (L* (ความเข

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