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Journal articles on the topic 'Least Square Error'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 July 2025

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1

Yonghe, Deng. "Improved Total Least Square Algorithm." Open Civil Engineering Journal 9, no. 1 (2015): 394–99. http://dx.doi.org/10.2174/1874149501509010394.

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Aim to blemish of total least square algorithm based on error equation of virtual observation,this paper proposed a sort of improved algorithm which doesn’t neglect condition equation of virtual observation,and considers both error equation and condition equation of virtual observation.So,the improved algorithm is better.Finally,this paper has fitted a straight line in three-dimensional space based on the improved algorithm.The result showed that the improved algorithm is viable and valid.

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Armentano, Marı́a G., and Ricardo G. Durán. "Error estimates for moving least square approximations." Applied Numerical Mathematics 37, no. 3 (2001): 397–416. http://dx.doi.org/10.1016/s0168-9274(00)00054-4.

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3

Zuppa, Carlos. "Error estimates for moving least square approximations." Bulletin of the Brazilian Mathematical Society 34, no. 2 (2003): 231–49. http://dx.doi.org/10.1007/s00574-003-0010-7.

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4

Octavia, Ranti Wilda Nur, and Umi Chotijah. "Implementasi Metode Least Square Untuk Prediksi Penjualan Kue Donat dan Bomboloni." Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi 11, no. 1 (2022): 251. http://dx.doi.org/10.35889/jutisi.v11i1.802.

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<p><strong>Abstrak.</strong> Prediksi Jumlah permintaan kue donat dan bomboloni oleh pelanggan (konsumen) Toko <em>Milly Donuts</em> selama ini tidak akurat, dimana jumlah produksi tidak sesuai dengan jumlah permintaan konsumen. Penelitian ini bertujuan mengimplementasikan Metode <em>Least Square</em> untuk melakukan <em>forecast</em> (prediksi) dalam penjualan. Metode <em>Least Square</em> merupakan salah satu teknik dalam menyusun <em>forecast</em> penjualan dengan meminimumkan fungsi kriteria jumlah kuadrat kesala

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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 (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. T

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Li, Long, and Jiacai Huang. "Recursive of Least Square Based Online Calibration Method in Geomagnetic Detection." MATEC Web of Conferences 232 (2018): 04087. http://dx.doi.org/10.1051/matecconf/201823204087.

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With the problem of attitude measurement accuracy is susceptible to various errors of geomagnetic survey, this paper establishes geomagnetic measurement error ellipsoid model by analysis of on the environment and own errors, uses the maximum likelihood algorithm for solving the static error correction coefficient. The experimental results show that, the maximum of attitude angle errors is less than 5° near blind direction, online combination correction can ensure the accuracy of attitude detection system under different shooting conditions.

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Seweh, Emmanuel Amomba, Zou Xiaobo, Feng Tao, Shi Jiachen, Haroon Elrasheid Tahir, and Muhammad Arslan. "Multivariate analysis of three chemometric algorithms on rapid prediction of some important quality parameters of crude shea butter using Fourier transform-near infrared spectroscopy." Journal of Near Infrared Spectroscopy 27, no. 3 (2019): 220–31. http://dx.doi.org/10.1177/0967033519830061.

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A comparative study of three chemometric algorithms combined with NIR spectroscopy with the aim of determining the best performing algorithm for quantitative prediction of iodine value, saponification value, free fatty acids content, and peroxide values of unrefined shea butter. Multivariate calibrations were developed for each parameter using supervised partial least squares, interval partial least squares, and genetic-algorithm partial least square regression methods to establish a linear relationship between standard reference and the Fourier transformed-near infrared predicted. Results sho

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Harshavardhan, P., and A. Raja. "Comparison on BER Performance Using Linear Filtering Least Square Method and Least Square Method for MIMO-OFDM Based Communication Systems." ECS Transactions 107, no. 1 (2022): 14197–204. http://dx.doi.org/10.1149/10701.14197ecst.

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In Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems, the Bit Error Rate is poor by using Linear Filtering Least Square method (LFLS) and Least Square (LS) method for massive MIMO-OFDM systems to extend the strength of the signal and to scale back the noise. Therefore the objective of the project is to maintain better Bit Error Rate performance. Materials and methods: Using Linear Filtering Least Square method, different values are used to enhance the Bit Error Rate (BER). The proposed method is compared with the least square method, the results are

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9

Koh, Show-Long Patrick. "WEIGHTED LEAST-SQUARE ESIMATE FOR SOFTWARE ERROR INTENSITY." Journal of the Chinese Institute of Industrial Engineers 25, no. 2 (2008): 162–73. http://dx.doi.org/10.1080/10170660809509081.

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10

Qi Cheng and Yingbo Hua. "Detection of cisoids using least square error function." IEEE Transactions on Signal Processing 45, no. 6 (1997): 1584–90. http://dx.doi.org/10.1109/78.600000.

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11

Li, Ting Jun, Jian Li Han, Zhi Yong Liu, Ya Zhou Zhang, and Jian Cun Ren. "Research on Passive Location Based on the Least Square Estimation Method." Key Engineering Materials 474-476 (April 2011): 1388–93. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.1388.

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Passive location system does not radiate electromagnetic wave, so it can work passively and conceal itself very well. Main factors interfering location precision are: measure errors of time difference, measure errors of three satellites, height errors of targets location on earth, geometry form of satellites cluster and orbit height of satellites cluster; The Least-square estimation method is presented, at the same time in the process of algorithm iteration the target height can be inquired in the geography information system and be corrected continuously, and the localization error is analyze

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12

Usman, U., N. Garba, A.B Zoramawa, and H. Usman. "Assessing the Performance of Ordinary Least Square and Kernel Regression." Continental J. Applied Sciences 15, no. 1 (2020): 14–23. https://doi.org/10.5281/zenodo.3764305.

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The assessment of Ordinary Least Squares (OLS) and kernel regression on their predictive performance was studied. We used simulated data to assess the performance of estimators using small and large sample. However, the mean square error (MSE) and root mean square error (RMSE) was used to find out the most efficient among the estimated models. The results show that, when  the ordinary least square is more efficient than the kernel regression due to having the least MSE and RMSE in both distributions. Whereas for  the ordinary least square and the kernel regression have the same perfo

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Xu, Yong-Li, Di-Rong Chen, and Han-Xiong Li. "Least Square Regularized Regression for Multitask Learning." Abstract and Applied Analysis 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/715275.

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The study of multitask learning algorithms is one of very important issues. This paper proposes a least-square regularized regression algorithm for multi-task learning with hypothesis space being the union of a sequence of Hilbert spaces. The algorithm consists of two steps of selecting the optimal Hilbert space and searching for the optimal function. We assume that the distributions of different tasks are related to a set of transformations under which any Hilbert space in the hypothesis space is norm invariant. We prove that under the above assumption the optimal prediction function of every

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Shu, Si Hui, and Zi Zhi Lin. "Approximate Merging B-Spline Curves via Least Square Approximation." Applied Mechanics and Materials 556-562 (May 2014): 3496–500. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3496.

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An algorithm of B-spline curve approximate merging of two adjacent B-spline curves is presented in this paper. In this algorithm, the approximation error between two curves is computed using norm which is known as best least square approximation. We develop a method based on weighed and constrained least squares approximation, which adds a weight function in object function to reduce error of merging. The knot insertion algorithm is also developed to meet the error tolerance.

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Escobar, Luis A., and Bradley Skarpness. "Mean square error and efficiency of the least squares estimator over interval constraints." Communications in Statistics - Theory and Methods 16, no. 2 (1987): 397–406. http://dx.doi.org/10.1080/03610928708829375.

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Elshambaky, Hossam Talaat. "Enhancing the predictability of least-squares collocation through the integration with least-squares-support vector machine." Journal of Applied Geodesy 13, no. 1 (2019): 1–15. http://dx.doi.org/10.1515/jag-2018-0017.

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Abstract Least-squares collocation (LSC) is a crucial mathematical tool for solving many geodetic problems. It has the capability to adjust, filter, and predict unknown quantities that affect many geodetic applications. Hence, this study aims to enhance the predictability property of LSC through applying soft computing techniques in the stage of describing the covariance function. Soft computing techniques include the support vector machine (SVM), least-squares-support vector machine (LS-SVM), and artificial neural network (ANN). A real geodetic case study is used to predict a national geoid f

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Yao, Yunhan, and Ke Zhang. "An Improved Self-Born Weighted Least Square Method for Cylindricity Error Evaluation." Applied Sciences 12, no. 23 (2022): 12319. http://dx.doi.org/10.3390/app122312319.

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In order to improve the stability of the evaluation results and the gross error resistance of the algorithm in view of the widespread gross errors in geometric error evaluation, an improved self-born weighted least square method (ISWLS) is proposed in this paper. First, the nonlinear cylindrical axial model is linearized to establish the error equation of the observed values. We use the conditional equations of the independent observations found as valid information to derive the weights of the observations. The weights of the observations are subjected to least-square iteration to calculate t

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Li Xiong and Jian-Kang Zhang. "Least Square Error Detection for Noncoherent Cooperative Relay Systems." IEEE Transactions on Vehicular Technology 61, no. 8 (2012): 3677–92. http://dx.doi.org/10.1109/tvt.2012.2206838.

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Fan, Kai Guo, Jian Guo Yang, and Li Yan Yang. "Tool Errors Compensation in Precision and Ultra-Precision Milling Based on the Least-Square Method." Advanced Materials Research 291-294 (July 2011): 428–31. http://dx.doi.org/10.4028/www.scientific.net/amr.291-294.428.

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The CNC milling machine is extensively used in manufacturing of the die and the box-type parts. However, the tool errors, which caused by the cutting heat and the cutting force, seriously affect the machining accuracy of the machined parts. Furthermore, the tool errors are too complex to be calculated by the empirical formula. To solve this problem, a tool error compensation method is proposed in this paper. The least-square method is employed to structure the error model. A weighting coefficient is proposed to adapt the various working conditions. The macro program is used to realize the real

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Ahmed, Mohd, Devinder Singh, Saeed AlQadhi, and Majed A. Alrefae. "Comparison of meshfree displacement and stress error recovery of finite element solutions using moving least squares interpolation." Advances in Mechanical Engineering 14, no. 3 (2022): 168781322210854. http://dx.doi.org/10.1177/16878132221085435.

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The moving least square (MLS) interpolation based the recovery procedures have been successfully applied to recover the finite element solution errors in the analysis of elastic plates and pipes problems and can be advantageously applied for large deformation and fracture problems. The study presents the displacement and stress error recovery characteristics in the error estimation analysis employing Moving Least Squares interpolation approach. The study considers quartic spline, cubic spline, and exponential weight function with three different order of basis function in Moving Least Squares

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21

Li, Rui. "Research on Treatment of Retaining Wall Foundation with Geosynthetics Based on BP Neural Network." Key Engineering Materials 852 (July 2020): 220–29. http://dx.doi.org/10.4028/www.scientific.net/kem.852.220.

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Through the long-term load creep test of CE131 geonet and SD L25 retaining wall foundation, which are widely used in reinforced earth engineering, a large number of experimental data are obtained. On this basis, the least-squares and BP neural network are used to predict its creep variables. The principle of least squares is to find a curve in the curve family to fit the experimental data. From the sum of the squared errors σ = 0. 001 16, the fitting accuracy is higher. The BP neural network has adaptive learning and memory capabilities, especially the three-layer BP neural network model. The

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22

Kundalia, Kush. "Comparative Analysis of Mean Square Loss and Least Square Loss in Modeling a Noisy Sine Curve." International Journal for Research in Applied Science and Engineering Technology 13, no. 2 (2025): 1518–22. https://doi.org/10.22214/ijraset.2025.67124.

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This paper compares the Mean Square Error (MSE) and Least Square Error (LSE) loss functions when modeling a noisy sine wave. Utilizing a simple linear regression model implemented in Python on Google Colab, a sine curve corrupted by Gaussian noise is generated. Two models are then fit, one optimizing MSE and the other LSE. Their performance is evaluated using Mean Absolute Error (MAE) and R-squared (R²) metrics. The experimental results offer insights into the efficiency and effectiveness of each loss function in capturing underlying trends within noisy data.

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El Marghichi, Mouncef, Soufiane Dangoury, Younes zahrou, et al. "Improving accuracy in state of health estimation for lithium batteries using gradient-based optimization: Case study in electric vehicle applications." PLOS ONE 18, no. 11 (2023): e0293753. http://dx.doi.org/10.1371/journal.pone.0293753.

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Significant improvements in battery performance, cost reduction, and energy density have been made since the advancements of lithium-ion batteries. These advancements have accelerated the development of electric vehicles (EVs). The safety and effectiveness of EVs depend on accurate measurement and prediction of the state of health (SOH) of lithium-ion batteries; however, this process is uncertain. In this study, our primary goal is to enhance the accuracy of SOH estimation by reducing uncertainties in state of charge (SOC) estimation and measurements. To achieve this, we propose a novel method

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Araveeporn, Autcha. "Comparing Parameter Estimation of Random Coefficient Autoregressive Model by Frequentist Method." Mathematics 8, no. 1 (2020): 62. http://dx.doi.org/10.3390/math8010062.

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This paper compares the frequentist method that consisted of the least-squares method and the maximum likelihood method for estimating an unknown parameter on the Random Coefficient Autoregressive (RCA) model. The frequentist methods depend on the likelihood function that draws a conclusion from observed data by emphasizing the frequency or proportion of the data namely least squares and maximum likelihood methods. The method of least squares is often used to estimate the parameter of the frequentist method. The minimum of the sum of squared residuals is found by setting the gradient to zero.

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Onyegbadue, Ikenna, Cosmas Ogbuka, and Theophilus Madueme. "Robust least square approach for optimal development of quadratic fuel quantity function for steam power stations." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 732–40. https://doi.org/10.11591/ijeecs.v25.i2.pp732-740.

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Ordinary least square (OLS) and robust least square (RLS) consisting of least absolute residual and Bi-square approaches were deployed to obtain the fuel consumption characteristic curve and the coefficients of the quadratic fuel consumption function for thermal stations in Nigeria. Results were compared based on convergence property, root mean square error, R-square value, adjusted R-square value, and width interval of coefficients. Valve Point loading effects of Egbin and Sapele power stations were used to develop the quadratic fuel consumption characteristic curve and function. The average

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Duan, Ming De, Hao Liang Feng, Kang Hua Liu, and Jun Yong Lu. "Modeling of Fixed Joints Stiffness Based on Modified Least-Square." Applied Mechanics and Materials 536-537 (April 2014): 1365–68. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.1365.

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According to experimental data, the model of fixed Joints stiffness in machine tools was built by least square of relative error. The new regression equations were obtained by regression analysis. Compared to the original equations with Gaussian least-square, the relative error of new regression equations is within 3.5%, which reduces by 12.5% and the mean absolute percentage error (MAPE) decreases by 18.0%, 12.4%and 19.0% respectively.

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SUN, HONGWEI, and PING LIU. "REGULARIZED LEAST SQUARE ALGORITHM WITH TWO KERNELS." International Journal of Wavelets, Multiresolution and Information Processing 10, no. 05 (2012): 1250043. http://dx.doi.org/10.1142/s0219691312500439.

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A new multi-kernel regression learning algorithm is studied in this paper. In our setting, the hypothesis space is generated by two Mercer kernels, thus it has stronger approximation ability than the single kernel case. We provide the mathematical foundation for this regularized learning algorithm. We obtain satisfying capacity-dependent error bounds and learning rates by the covering number method.

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Xing, Chuan, and Hai Zhang. "Optimal Weighted Least Square Data Fusion Method for Redundant IMU Based on Calculation of Measurement Error." Applied Mechanics and Materials 241-244 (December 2012): 149–55. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.149.

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A dodecahedron non-orthogonal redundant IMU configuration was selected as model. To improve fusion accuracy, we proposed an effective calculation method for measurement errors based on the correlation between measurement errors and fusion errors. The method considered the difference between traditional data fusion vector’s projection and measurement results, and then made a conversion from projection error to measurement error. Combined with optimal weighted least square method, measurement error was used to generate an optimal weighted matrix, and this made data fusion errors minimum. Simulat

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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 (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

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Prasetyowati, Sri Arttini Dwi, Munaf Ismail, and Badieah Badieah. "Implementation of Least Mean Square Adaptive Algorithm on Covid-19 Prediction." JUITA: Jurnal Informatika 10, no. 1 (2022): 139. http://dx.doi.org/10.30595/juita.v10i1.11963.

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This study used Corona Virus Disease-19 (Covid-19) data in Indonesia from June to August 2021, consisting of data on people who were infected or positive Covid-19, recovered from Covid-19, and passed away from Covid-19. The data were processed using the adaptive LMS algorithm directly without pre-processing cause calculation errors, because covid-19 data was not balanced. Z-score and min-max normalization were chosen as pre-processing methods. After that, the prediction process can be carried out using the LMS adaptive method. The analysis was done by observing the error prediction that occurr

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Kang, SeYoung, TaeHyun Kim, and WonZoo Chung. "Hybrid RSS/AOA Localization using Approximated Weighted Least Square in Wireless Sensor Networks." Sensors 20, no. 4 (2020): 1159. http://dx.doi.org/10.3390/s20041159.

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We present a target localization method using an approximated error covariance matrix based weighted least squares (WLS) solution, which integrates received signal strength (RSS) and angle of arrival (AOA) data for wireless sensor networks. We approximated linear WLS errors via second-order Taylor approximation, and further approximated the error covariance matrix using a least-squares solution and the variance in measurement noise over the sensor nodes. The algorithm does not require any prior knowledge of the true target position or noise variance. Simulations validated the superior performa

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Tong, Hongzhi, Di-Rong Chen, and Fenghong Yang. "Least Square Regression with lp-Coefficient Regularization." Neural Computation 22, no. 12 (2010): 3221–35. http://dx.doi.org/10.1162/neco_a_00044.

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The selection of the penalty functional is critical for the performance of a regularized learning algorithm, and thus it deserves special attention. In this article, we present a least square regression algorithm based on lp-coefficient regularization. Comparing with the classical regularized least square regression, the new algorithm is different in the regularization term. Our primary focus is on the error analysis of the algorithm. An explicit learning rate is derived under some ordinary assumptions.

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Wang, Yu, Jie Zou, Yuelin Xu, et al. "Optical Fiber Vibration Sensor Using Least Mean Square Error Algorithm." Sensors 20, no. 7 (2020): 2000. http://dx.doi.org/10.3390/s20072000.

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In order to enhance the signal-to-noise ratio (SNR) of a distributed optical fiber vibration sensor based on coherent optical time domain reflectometry (COTDR), a high extinction ratio cascade structure of an acousto-optic modulator and semiconductor optical amplifier is applied. The prior time-frequency analysis and least mean square error algorithm are adopted in the COTDR system for amplitude demodulation and phase demodulation, in order to improve the SNR by noise elimination. The experimental results show that the adaptive filter based on the least mean square error algorithm could realiz

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Armentano, María G. "Error Estimates in Sobolev Spaces for Moving Least Square Approximations." SIAM Journal on Numerical Analysis 39, no. 1 (2001): 38–51. http://dx.doi.org/10.1137/s0036142999361608.

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Dam, H. H., S. Nordebo, K. L. Teo, and A. Cantoni. "FIR filter design over discrete coefficients and least square error." IEE Proceedings - Vision, Image, and Signal Processing 147, no. 6 (2000): 543. http://dx.doi.org/10.1049/ip-vis:20000598.

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Tao, Yanfang, Peipei Yuan, and Biqin Song. "Error analysis of regularized least-square regression with Fredholm kernel." Neurocomputing 249 (August 2017): 237–44. http://dx.doi.org/10.1016/j.neucom.2017.03.076.

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Yiu-Wing Leung. "Least-square-error estimate of individual contribution in group project." IEEE Transactions on Education 41, no. 4 (1998): 282–85. http://dx.doi.org/10.1109/13.728262.

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Yilmaz, Orhan, and M. Turhan Taner. "Discrete plane‐wave decomposition by least‐mean‐square‐error method." GEOPHYSICS 59, no. 6 (1994): 973–82. http://dx.doi.org/10.1190/1.1443657.

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The recording of a point source wavefield can be decomposed into a set of plane‐wave components, each corresponding to different angles of propagation. Such plane‐wave seismograms have a far simpler structure than the spherical waves of the point source records, which makes them desirable in many steps of seismic data processing such as predictive deconvolution, migration, inversion, etc. The implementation of the plane‐wave decomposition requires the computation of the Radon transform in the discrete data domain. A straightforward application of the integral solutions to geophysical problems

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SÖKÜT AÇAR, Tuğba. "Kibria-Lukman Estimator for General Linear Regression Model with AR(2) Errors: A Comparative Study with Monte Carlo Simulation." Journal of New Theory, no. 41 (December 31, 2022): 1–17. http://dx.doi.org/10.53570/jnt.1139885.

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The sensitivity of the least-squares estimation in a regression model is impacted by multicollinearity and autocorrelation problems. To deal with the multicollinearity, Ridge, Liu, and Ridge-type biased estimators have been presented in the statistical literature. The recently proposed Kibria-Lukman estimator is one of the Ridge-type estimators. The literature has compared the Kibria-Lukman estimator with the others using the mean square error criterion for the linear regression model. It was achieved in a study conducted on the Kibria-Lukman estimator's performance under the first-order autor

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Zamzami, Balqis Dwian Fitri, Aisyah Tiara Pratiwi, Della Septiani, et al. "Algoritma Alternating Least Squares Untuk Mesin Rekomendasi Film." PROSIDING SEMINAR NASIONAL SAINS DATA 4, no. 1 (2024): 341–50. https://doi.org/10.33005/senada.v4i1.210.

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Abstract: The entertainment world is inseparable from the rapidly growing movie industry and is accompanied by huge data growth. The rapid growth of data has brought about a new era of information. These data are utilized to build innovative, efficient and more effective systems. This research implements a movie recommendation system using the Alternating Least Squares (ALS) algorithm from Apache Spark MLlib with the MovieLens 25M dataset. A collaborative filtering approach with matrix factorization is used to model user preferences and movie characteristics. The evaluation is done by calculat

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Mao, Qin, and Youming Li. "Robust Localization Based on Constrained Total Least Squares in Wireless Sensor Networks." Wireless Communications and Mobile Computing 2022 (March 14, 2022): 1–7. http://dx.doi.org/10.1155/2022/4101571.

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Source localization based on signal strength measurements has become very popular due to its wide applications. This paper focuses on differential received signal strength-based localization with model uncertainties in case of unknown transmit power. The error caused by the measurement noise and the reference power offset and the first-order approximate error generated in the process of DRSS receiving power linearization are merged into the constrained total least square regression matrix equation after linearization. The location optimization problem is formed by minimizing the influence of t

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LI, LUOQING. "REGULARIZED LEAST SQUARE REGRESSION WITH SPHERICAL POLYNOMIAL KERNELS." International Journal of Wavelets, Multiresolution and Information Processing 07, no. 06 (2009): 781–801. http://dx.doi.org/10.1142/s0219691309003240.

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This article considers regularized least square regression on the sphere. It develops a theoretical analysis of the generalization performances of regularized least square regression algorithm with spherical polynomial kernels. The explicit bounds are derived for the excess risk error. The learning rates depend on the eigenvalues of spherical polynomial integral operators and on the dimension of spherical polynomial spaces.

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Hwan Seo, Myung. "ESTIMATION OF NONLINEAR ERROR CORRECTION MODELS." Econometric Theory 27, no. 2 (2010): 201–34. http://dx.doi.org/10.1017/s026646661000023x.

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Asymptotic theory for the estimation of nonlinear vector error correction models that exhibit regime-specific short-run dynamics is developed. In particular, regimes are determined by the error correction term, and the transition between regimes is allowed to be discontinuous, as in, e.g., threshold cointegration. Several nonregular problems are resolved. First of all, consistency—square rootnconsistency for the cointegrating vectorβ—is established for the least squares estimation of this general class of models. Second, the convergence rates are obtained for the least squares of threshold coi

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Hua, Taoyi, Ying Gao, Yuelin You, and Changwen Jiang. "Parameter Identification of DOC Model Based on Variable Forgetting Factor Least Squares." E3S Web of Conferences 360 (2022): 01038. http://dx.doi.org/10.1051/e3sconf/202236001038.

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In diesel engine after-treatment control technology, the accurate real-time control of Diesel Oxidation Catalyst (DOC) outlet temperature is an important topic. To find a high-precision parameter identification algorithm for the DOC system, this paper establishes zero-dimensional (0D) and one-dimensional (1D) mathematical models of DOC, introduces Variable Forgetting Factor Least Squares(VFFRLS) and Nonlinear Least Squares parameter identification for comparison and analysis. The results show that the 0D determination coefficient R-square of Nonlinear Least Squares parameter identification res

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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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Yang, Bin, Min Chen, Tong Su, and Jianjun Zhou. "Robust Estimation for Semi-Functional Linear Model with Autoregressive Errors." Mathematics 11, no. 2 (2023): 277. http://dx.doi.org/10.3390/math11020277.

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It is well-known that the traditional functional regression model is mainly based on the least square or likelihood method. These methods usually rely on some strong assumptions, such as error independence and normality, that are not always satisfied. For example, the response variable may contain outliers, and the error term is serially correlated. Violation of assumptions can result in unfavorable influences on model estimation. Therefore, a robust estimation procedure of a semi-functional linear model with autoregressive error is developed to solve this problem. We compare the efficiency of

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Amani, J., A. Saboor Bagherzadeh, and T. Rabczuk. "Error Estimate and Adaptive Refinement in Mixed Discrete Least Squares Meshless Method." Mathematical Problems in Engineering 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/721240.

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The node moving and multistage node enrichment adaptive refinement procedures are extended in mixed discrete least squares meshless (MDLSM) method for efficient analysis of elasticity problems. In the formulation of MDLSM method, mixed formulation is accepted to avoid second-order differentiation of shape functions and to obtain displacements and stresses simultaneously. In the refinement procedures, a robust error estimator based on the value of the least square residuals functional of the governing differential equations and its boundaries at nodal points is used which is inherently availabl

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

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One of the main radio technologies that could be used for indoor localization is Ultra-wideband, (UWB). It is a short-range RF technology for wireless communication that can be leveraged to detect the location of people, devices, and assets with significant precision. But, it has a major limitation which is the need for a non-line-of-sight (NLOS) identification and mitigation approach to precise location a target in a hard indoor environment. The NLOS approach will complicate the positioning approach. The goals of this work are; i- for saving cost and time of installation of anchor nodes, the

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Hall, Peter, and James Stephen Marron. "Extent to which least-squares cross-validation minimises integrated square error in nonparametric density estimation." Probability Theory and Related Fields 74, no. 4 (1987): 567–81. http://dx.doi.org/10.1007/bf00363516.

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Bengnga, Amiruddin, and Rezqiwati Ishak. "Prediksi Jumlah Mahasiswa Ujian Skripsi Dengan Metode Least Square." Jurnal SITECH : Sistem Informasi dan Teknologi 4, no. 1 (2021): 43–50. http://dx.doi.org/10.24176/sitech.v4i1.6224.

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Pencapaian target jumlah mahasiswa yang ujian skripsi di setiap semester pada setiap Program Studi yang sudah ditentukan di awal semester tentunya ada yang mencapai target dan tidak mencapai target. Jika tidak mencapai target maka hal ini akan menjadi penilaian kinerja Program Studi menurun, agar hal ini tidak terjadi, maka salah satu soluisnya adalah melakukan teknik prediksi dengan menggunakan data yang relevan di periode semester sebelumnya. Metode prediksi yang digunakan adalah metode Least Square karena metode ini cocok digunakan untuk memprediksi data dalam bentuk time series. Data yang

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