Log in

Relevant bibliographies by topics / Weight Least Square Regression / Journal articles

To see the other types of publications on this topic, follow the link: Weight Least Square Regression.

Journal articles on the topic 'Weight Least Square Regression'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Weight Least Square Regression.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

11

Takemura, Kazuhisa. "Fuzzy Least Squares Regression Analysis for Social Judgment Study." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 5 (2005): 461–66. http://dx.doi.org/10.20965/jaciii.2005.p0461.

Full text

Abstract:

Social judgment data was analyzed using fuzzy least squares regression analysis based on the extension principle. The proposed analysis is new fuzzy least squares regression analysis in which input data, output data, and coefficients are represented by L-R fuzzy numbers. To evaluate data fitness, we propose a fuzzy version of a squared multiple correlation (<I>R</I>2) and conducted an experiment to determine the effect of partial attribute information on the overall evaluation of desirability for a marital partner and personality of a person using fuzzy rating to measure vagueness

APA, Harvard, Vancouver, ISO, and other styles

12

R, Aditya Setyawan, Mustika Hadijati, and Ni Wayan Switrayni. "Analisis Masalah Heteroskedastisitas Menggunakan Generalized Least Square dalam Analisis Regresi." EIGEN MATHEMATICS JOURNAL 1, no. 2 (2019): 61. http://dx.doi.org/10.29303/emj.v1i2.43.

Full text

Abstract:

Regression analysis is one statistical method that allows users to analyze the influence of one or more independent variables (X) on a dependent variable (Y).The most commonly used method for estimating linear regression parameters is Ordinary Least Square (OLS). But in reality, there is often a problem with heteroscedasticity, namely the variance of the error is not constant or variable for all values of the independent variable X. This results in the OLS method being less effective. To overcome this, a parameter estimation method can be used by adding weight to each parameter, namely the Gen

APA, Harvard, Vancouver, ISO, and other styles

13

Islamiyati, Anna, Anisa Anisa, Muhammad Zakir, et al. "THE USE OF PENALIZED WEIGHTED LEAST SQUARE TO OVERCOME CORRELATIONS BETWEEN TWO RESPONSES." BAREKENG: Jurnal Ilmu Matematika dan Terapan 16, no. 4 (2022): 1497–504. http://dx.doi.org/10.30598/barekengvol16iss4pp1497-1504.

Full text

Abstract:

The non-parametric regression model can consider two correlated responses. However, for these conditions, we cannot use the usual estimation process because there are violations of assumptions. To solve this problem, we use a penalized weighted least square involving knots, smoothing parameters, and weighting in the estimation criteria simultaneously. The estimation process involves a weighted criteria matrix in the estimation criteria. Estimation results show that the estimated two-response non-parametric regression function with penalized spline is a linear estimation class in y response obs

APA, Harvard, Vancouver, ISO, and other styles

14

Wang, Haiqi, Liuke Li, Lei Che, et al. "Geospatial Least Squares Support Vector Regression Fused with Spatial Weight Matrix." ISPRS International Journal of Geo-Information 10, no. 11 (2021): 714. http://dx.doi.org/10.3390/ijgi10110714.

Full text

Abstract:

Due to the increasingly complex objects and massive information involved in spatial statistics analysis, least squares support vector regression (LS-SVR) with a good stability and high calculation speed is widely applied in regression problems of geospatial objects. According to Tobler’s First Law of Geography, near things are more related than distant things. However, very few studies have focused on the spatial dependence between geospatial objects via SVR. To comprehensively consider the spatial and attribute characteristics of geospatial objects, a geospatial LS-SVR model for geospatial da

APA, Harvard, Vancouver, ISO, and other styles

15

Peng, Jiangtao. "Sparse matrix transform based weight updating in partial least squares regression." Journal of Mathematical Chemistry 52, no. 8 (2014): 2197–209. http://dx.doi.org/10.1007/s10910-014-0380-7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Lee, Dae-Hyun, Seung-Hyun Lee, Byoung-Kwan Cho, et al. "Estimation of carcass weight of Hanwoo (Korean native cattle) as a function of body measurements using statistical models and a neural network." Asian-Australasian Journal of Animal Sciences 33, no. 10 (2020): 1633–41. http://dx.doi.org/10.5713/ajas.19.0748.

Full text

Abstract:

Objective: The objective of this study was to develop a model for estimating the carcass weight of Hanwoo cattle as a function of body measurements using three different modeling approaches: i) multiple regression analysis, ii) partial least square regression analysis, and iii) a neural network.Methods: Data from a total of 134 Hanwoo cattle were obtained from the National Institute of Animal Science in South Korea. Among the 372 variables in the raw data, 20 variables related to carcass weight and body measurements were extracted to use in multiple regression, partial least square regression,

APA, Harvard, Vancouver, ISO, and other styles

17

Hou, Yan-Yan, Jian Li, Xiu-Bo Chen, and Chong-Qiang Ye. "A partial least squares regression model based on variational quantum algorithm." Laser Physics Letters 19, no. 9 (2022): 095204. http://dx.doi.org/10.1088/1612-202x/ac81b6.

Full text

Abstract:

Abstract Partial least squares regression (PLSR) is an essential multivariate correlation analysis method in machine learning field. In this paper, we propose a variational quantum algorithm for partial least regression (VQPLSR). By exploring the relationship between standard basis states and optimization, we design a cost function that can train regression parameters and weight vectors simultaneously. The VQPLS requires only one copy of variables as input, which reduces the complexity of quantum circuit implementation. Compared with PLSR, the VQPLSR achieves an exponential speed-up in the ind

APA, Harvard, Vancouver, ISO, and other styles

18

Sheng, Wenjuan, Haiqi Dang, and G. D. Peng. "Hysteresis and temperature drift compensation for FBG demodulation by utilizing adaptive weight least square support vector regression." Optics Express 29, no. 24 (2021): 40547. http://dx.doi.org/10.1364/oe.442776.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Lestari, Trianingsih Eni, and Rike Desy Tri Yuansa Yuansa. "Response Surface Regression with LTS and MM-Estimator to Overcome Outliers on Red Roselle Flowers." Jurnal Varian 4, no. 2 (2021): 91–98. http://dx.doi.org/10.30812/varian.v4i2.882.

Full text

Abstract:

The surface response method is similar to the regression analysis method which uses procedures or ways of estimating the response function regression model based on the Ordinary Least Square (OLS) method. Unfortunately, using the quadratic method has no drawbacks because it is easily sensitive to assumption deviations due to outlier cases. One of the solutions to the outlier problem is using robust regression. The method of parameters in the regression is very diverse, but the methods used in this study are the Least Trimmed Square (LTS) and MM-estimator methods because both methods have a hig

APA, Harvard, Vancouver, ISO, and other styles

20

Zainab S. Noori and Makki A. Mohammed Salih. "Linear Formulas for Estimating the Reliability of Generalized Inverse Weibull Distributi." Mustansiriyah Journal of Pure and Applied Sciences 1, no. 1 (2022): 140–54. http://dx.doi.org/10.47831/mjpas.v1i1.15.

Full text

Abstract:

Estimation methods that depend on linear formulas to estimate the reliability of the three parameters generalized inverse Weibull distribution (GIW) were used in this research, including each of Least Square method (LS), Weight Least Square method (WLS), White method (W), Modified White method (MW) and Linear Regression method (REG). The parameters and reliability of the distribution were estimated in four experiments using simulation to generating the required samples and the results were compared using the mean square error criterion. The results showed: In general the Modified White estimat

APA, Harvard, Vancouver, ISO, and other styles

21

Zhang, Xinyu, and Chu-An Liu. "INFERENCE AFTER MODEL AVERAGING IN LINEAR REGRESSION MODELS." Econometric Theory 35, no. 4 (2018): 816–41. http://dx.doi.org/10.1017/s0266466618000269.

Full text

Abstract:

This article considers the problem of inference for nested least squares averaging estimators. We study the asymptotic behavior of the Mallows model averaging estimator (MMA; Hansen, 2007) and the jackknife model averaging estimator (JMA; Hansen and Racine, 2012) under the standard asymptotics with fixed parameters setup. We find that both MMA and JMA estimators asymptotically assign zero weight to the under-fitted models, and MMA and JMA weights of just-fitted and over-fitted models are asymptotically random. Building on the asymptotic behavior of model weights, we derive the asymptotic distr

APA, Harvard, Vancouver, ISO, and other styles

22

Lestari, Windi, Widiarti, Bernadhita Herindri Samodera Utami, Mustofa Usman, and Vitri Aprilla Handayani. "Robust Panel Data Regression Analysis using the Least Trimmed Squares (LTS) Estimator on Poverty Line Data in Lampung Province." Integra: Journal of Integrated Mathematics and Computer Science 1, no. 2 (2024): 35–42. https://doi.org/10.26554/integrajimcs.20241210.

Full text

Abstract:

Robust regression is an alternative method in regression analysis designed to produce stable parameter estimates, even when the data contain outliers or deviate from classical assumptions. One of its estimation techniques, the Least Trimmed Square (LTS),works by minimizing the smallest squared residuals, thereby assigning smaller weights to extreme data points. This method serves as a solution when classical approaches, such as Ordinary Least Squares (OLS), fail to meet the assumptions, especially in socio-economic data that are often complex and prone to outliers. This study employs robust re

APA, Harvard, Vancouver, ISO, and other styles

23

Sheng, Baohuai, and Daohong Xiang. "The performance of semi-supervised Laplacian regularized regression with the least square loss." International Journal of Wavelets, Multiresolution and Information Processing 15, no. 02 (2017): 1750016. http://dx.doi.org/10.1142/s0219691317500163.

Full text

Abstract:

The capacity convergence rate for a kind of kernel regularized semi-supervised Laplacian learning algorithm is bounded with the convex analysis approach. The algorithm is a graph-based regression whose structure shares the feature of both the kernel regularized regression and the kernel regularized Laplacian ranking. It is shown that the kernel reproducing the hypothesis space has contributions to the clustering ability of the algorithm. If the scale parameters in the Gaussian weights are chosen properly, then the learning rate can be controlled by the unlabeled samples and the algorithm conve

APA, Harvard, Vancouver, ISO, and other styles

24

Rodrigues, Adriano, Lucas Monteiro Chaves, Fabyano Fonseca Silva, Idalmo Pereira Garcia, Darlene Ana Souza Duarte, and Henrique Torres Ventura. "Isotonic regression analysis of Guzerá cattle growth curves." Revista Ceres 65, no. 1 (2018): 24–27. http://dx.doi.org/10.1590/0034-737x201865010004.

Full text

Abstract:

ABSTRACT The objective of this study was to apply data transformation via isotonic regression in growth curves studies of Guzerá cattle whose data presented disturbances characterized by decreased body weight in certain age groups. Weight-age data were collected on newly weaned Guzerá males according to the methodology of weight gain tests (WGT) defined by the Brazilian Association of Zebu Breeders (ABCZ). The Logistic, Von Bertalanffy and Gompertz models were fitted to weight-age data using the generalized least squares method for non-linear regression models through the Gauss-Newton algorith

APA, Harvard, Vancouver, ISO, and other styles

25

Cominotte, Alexandre, Arthur Fernandes, João Dórea, et al. "Use of Biometric Images to Predict Body Weight and Hot Carcass Weight of Nellore Cattle." Animals 13, no. 10 (2023): 1679. http://dx.doi.org/10.3390/ani13101679.

Full text

Abstract:

The objective of this study was to evaluate different methods of predicting body weight (BW) and hot carcass weight (HCW) from biometric measurements obtained through three-dimensional images of Nellore cattle. We collected BW and HCW of 1350 male Nellore cattle (bulls and steers) from four different experiments. Three-dimensional images of each animal were obtained using the Kinect® model 1473 sensor (Microsoft Corporation, Redmond, WA, USA). Models were compared based on root mean square error estimation and concordance correlation coefficient. The predictive quality of the approaches used m

APA, Harvard, Vancouver, ISO, and other styles

26

Yılmaz, Furkan, Lütfi Bayyurt, Samet Hasan Abacı, and Yalçın Tahtalı. "Çoklu Doğrusal Bağlantı Durumunda En Küçük Kareler ve Bazı Yanlı Tahmin Edicilerin Karşılaştırılması." Turkish Journal of Agriculture - Food Science and Technology 8, no. 3 (2020): 793. http://dx.doi.org/10.24925/turjaf.v8i3.793-799.3405.

Full text

Abstract:

The aim of this study is to compare the least squares (LS) method that lost its function in the case of multicollinearity in regression methods with Ridge Regression (RR) and Principal Components Regression (PCR) which are bias estimators. For this aim, the effect of some body measurements on body weight (BW), body length (BL), height at withers (HW), height at rump (HR), chest depth (CD), chest girth (CG) and chest width (CW) obtained from 59 Saanen kids at weaning period raised at Research Farm of Tokat Gaziosmanpaşa University. Determination coefficient (R2) and mean square error (MSE) valu

APA, Harvard, Vancouver, ISO, and other styles

27

Li, Xiaoli, Bin Hu, and Ruxu Du. "Predicting the Parts Weight in Plastic Injection Molding Using Least Squares Support Vector Regression." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 38, no. 6 (2008): 827–33. http://dx.doi.org/10.1109/tsmcc.2008.2001707.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Adiele, DF, and UO Elem. "Biostatistical analysis of birth weight and head circumference of babies a case study of Nigeria." Global Journal of Mathematical Sciences 12, no. 1 (2015): 5–12. http://dx.doi.org/10.4314/gjmas.v12i1.12.

Full text

Abstract:

This paper examined the relationship between birth weight and head circumference of babies. The ordinary least square method of linear regression analysis and Chi-square test were utilized to achieve its objectives. The hypothesis that birth weight is independent of head circumference; birth weight is independent of sex; and head circumference is independent of sex was rejected at 5 percent level of significance. The result showed that birth weight is dependent on head circumference and sex; and that head circumference depends on sex. Birth weight and head circumference of male babies are high

APA, Harvard, Vancouver, ISO, and other styles

29

Andika Putra, Muhammad Rafael, Nurjannah Nurjannah, and Mila Kurniawaty. "Estimation of Gompertz Mortality Parameter Models on Indonesian Population Mortality Table 2023." CAUCHY: Jurnal Matematika Murni dan Aplikasi 10, no. 2 (2025): 545–55. https://doi.org/10.18860/cauchy.v10i2.33319.

Full text

Abstract:

The research article discuss Gompertz Mortality Law parameter estimation using several methods to get the best models. The data based from Indonesian population mortality table or called Tabel Mortalitas Penduduk Indonesia (TMPI) 2023. Parameter estimation using several methods, includes Nonlinear Least Square (NLLS) with the Gauss-Newton algorithm, Weighted Least Squares (WLS), and Poisson Regression. Model validation is done by calculating root mean square error (RMSE) to determine the most accurate method. The analysis includes calculation of values in the mortality table, transformation of

APA, Harvard, Vancouver, ISO, and other styles

30

Palla, Ilir. "The Comparison of Some Methods in Analysis of Linear Regression Using R Software." European Journal of Engineering and Formal Sciences 3, no. 3 (2019): 22. http://dx.doi.org/10.26417/ejef.v3i3.p22-31.

Full text

Abstract:

This article contains the OLS method, WLS method and bootstrap methods to estimate coefficients of linear regression and their standard deviation. If regression holds random errors with constant variance and if those errors are independent normally distributed we can use least squares method, which is accurate for drawing inferences with these assumptions. If the errors are heteroscedastic, meaning that their variance depends from explanatory variable, or have different weights, we can’t use least squares method because this method cannot be safe for accurate results. If we know weights for ea

APA, Harvard, Vancouver, ISO, and other styles

31

Bhavika, Dineshkumar Shah. "An application of Multivariate Models on GSRTC Data." RESEARCH REVIEW International Journal of Multidisciplinary 03, no. 07 (2018): 232–38. https://doi.org/10.5281/zenodo.1312353.

Full text

Abstract:

In this study a systematic methodology is developed to find out the causes & effects of the variability which helps to study the parameters of GSRTC so, various multiple regression analysis are developed. We have tried to find out the main parameters affects the margin of GSRTC. These variables provide the negative correlation with the rate of changes in the margins of the selected 16 depots of GSRTC, which shows that these variables also have negative linear relationships. By compressing these variables margin can be increase. Here, we have tried to show how space and time matters to the

APA, Harvard, Vancouver, ISO, and other styles

32

Amiri-Simkooei, A., and S. Jazaeri. "Weighted total least squares formulated by standard least squares theory." Journal of Geodetic Science 2, no. 2 (2012): 113–24. http://dx.doi.org/10.2478/v10156-011-0036-5.

Full text

Abstract:

Weighted total least squares formulated by standard least squares theoryThis contribution presents a simple, attractive, and flexible formulation for the weighted total least squares (WTLS) problem. It is simple because it is based on the well-known standard least squares theory; it is attractive because it allows one to directly use the existing body of knowledge of the least squares theory; and it is flexible because it can be used to a broad field of applications in the error-invariable (EIV) models. Two empirical examples using real and simulated data are presented. The first example, a li

APA, Harvard, Vancouver, ISO, and other styles

33

Ansumal, Aman, and Bharat Raj Subba. "Studies on Length-Weight Relationship of a Hill-Stream Loach, Schistura cupicola (McClelland)." Journal of Natural History Museum 24 (October 9, 2009): 126–29. http://dx.doi.org/10.3126/jnhm.v24i1.2288.

Full text

Abstract:

The study of length-weight relationship of a hill-stream loach, Schistura rupicola McClelland collected from Lampengwa Khola, Nepal was done using the least square formula W=aLTb. The values of 'a' and 'b' calculated from the measurement of total length and total body weight were -2.1431 and 3.0508 respectively. The computed value of correlation coefficient (r) was 0.9970. Key words: Correlation coefficient; Regression coefficient; Allometric growth; Body weight. Journal of Natural History MuseumVol. 24, 2009Page : 126-129

APA, Harvard, Vancouver, ISO, and other styles

34

Sebayang, Jimmy Saputra, and Budi Yuniarto. "Perbandingan Model Estimasi Artificial Neural Network Optimasi Genetic Algorithm dan Regresi Linier Berganda." MEDIA STATISTIKA 10, no. 1 (2017): 13. http://dx.doi.org/10.14710/medstat.10.1.13-23.

Full text

Abstract:

Multiple Linear Regression is a statistical approach most commonly used in performing predictive data modeling. One of the methods that can be used in estimating the parameters of the model on Multiple Linear Regression is Ordinary Least Square. It has classical assumptions requirements and often the assumptions are not satisfied. Another method that can be used as an alternative data modeling is Artificial Neural Network. It is a free-distribution estimator because there's no assumptions that have to be satisfied. However, modeling data using ANN has some problems such as selection of network

APA, Harvard, Vancouver, ISO, and other styles

35

DelSole, Timothy, Liwei Jia, and Michael K. Tippett. "Scale-Selective Ridge Regression for Multimodel Forecasting." Journal of Climate 26, no. 20 (2013): 7957–65. http://dx.doi.org/10.1175/jcli-d-13-00030.1.

Full text

Abstract:

Abstract This paper proposes a new approach to linearly combining multimodel forecasts, called scale-selective ridge regression, which ensures that the weighting coefficients satisfy certain smoothness constraints. The smoothness constraint reflects the “prior assumption” that seasonally predictable patterns tend to be large scale. In the absence of a smoothness constraint, regression methods typically produce noisy weights and hence noisy predictions. Constraining the weights to be smooth ensures that the multimodel combination is no less smooth than the individual model forecasts. The propos

APA, Harvard, Vancouver, ISO, and other styles

36

ANKARALI, Handan, Özge YILMAZ, Münevver KIZILAY, İlknur ARSLANOĞLU, and Duygu AYDIN. "The Use of Nonparametric Quantile Regression and Least Median of Squares Regression for Construction of Growth Curves of Weight." Turkiye Klinikleri Journal of Medical Sciences 33, no. 3 (2013): 692–701. http://dx.doi.org/10.5336/medsci.2012-30442.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Xu, Shuqiong, Zhi Liu, and Yun Zhang. "Least squares support vector regression and interval type-2 fuzzy density weight for scene denoising." Soft Computing 20, no. 4 (2015): 1459–70. http://dx.doi.org/10.1007/s00500-015-1598-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Sholiha, Anisatus, Kuzairi Kuzairi, and M. Fariz Fadillah Madianto. "Estimator Deret Fourier Dalam Regresi Nonparametrik dengan Pembobot Untuk Perencanaan Penjualan Camilan Khas Madura." Zeta - Math Journal 4, no. 1 (2018): 18–23. http://dx.doi.org/10.31102/zeta.2018.4.1.18-23.

Full text

Abstract:

The purpose of regression analysis is determining the relationship between response variables to predictor variables. To estimate the regression curve there are three approaches, parametric regression, nonparametric regression, and semiparametric regression. In this study, the estimator form of nonparametric regression curve is analyzed by using the Fourier series approach with sine and cosine bases, sine bases, and cosine bases. Based on Weighted Least Square (WLS) optimization, the estimator result can be applied to model the sale planning of Madura typical snacks. Nonparametric regression e

APA, Harvard, Vancouver, ISO, and other styles

39

Burm, Jin Pil. "The influence of assay error weight on gentamicin pharmacokinetics using the bayesian and nonlinear least square regression analysis in appendicitis patients." Archives of Pharmacal Research 28, no. 5 (2005): 598–603. http://dx.doi.org/10.1007/bf02977765.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Cui, Licheng, Huawei Zhai, and Hongfei Lin. "A Novel Orthogonal Extreme Learning Machine for Regression and Classification Problems." Symmetry 11, no. 10 (2019): 1284. http://dx.doi.org/10.3390/sym11101284.

Full text

Abstract:

An extreme learning machine (ELM) is an innovative algorithm for the single hidden layer feed-forward neural networks and, essentially, only exists to find the optimal output weight so as to minimize output error based on the least squares regression from the hidden layer to the output layer. With a focus on the output weight, we introduce the orthogonal constraint into the output weight matrix, and propose a novel orthogonal extreme learning machine (NOELM) based on the idea of optimization column by column whose main characteristic is that the optimization of complex output weight matrix is

APA, Harvard, Vancouver, ISO, and other styles

41

Akkol, Suna. "The prediction of live weight of hair goats through penalized regression methods: LASSO and adaptive LASSO." Archives Animal Breeding 61, no. 4 (2018): 451–58. http://dx.doi.org/10.5194/aab-61-451-2018.

Full text

Abstract:

Abstract. The least absolute selection and shrinkage operator (LASSO) and adaptive LASSO methods have become a popular model in the last decade, especially for data with a multicollinearity problem. This study was conducted to estimate the live weight (LW) of Hair goats from biometric measurements and to select variables in order to reduce the model complexity by using penalized regression methods: LASSO and adaptive LASSO for γ=0.5 and γ=1. The data were obtained from 132 adult goats in Honaz district of Denizli province. Age, gender, forehead width, ear length, head length, chest width, rump

APA, Harvard, Vancouver, ISO, and other styles

42

Hattori, Yusuke, Miki Naganuma, and Makoto Otsuka. "Partial Least Squares Regression-Based Robust Forward Control of the Tableting Process." Pharmaceutics 12, no. 1 (2020): 85. http://dx.doi.org/10.3390/pharmaceutics12010085.

Full text

Abstract:

In this study, we established a robust feed-forward control model for the tableting process by partial least squares regression using the near-infrared (NIR) spectra and physical attributes of the granules to be compressed. The NIR spectra of granules are rich in information about chemical attributes, such as the compositions of any ingredients and moisture content. Polymorphism and pseudo-polymorphism can also be quantitatively evaluated by NIR spectra. We used the particle size distribution, flowability, and loose and tapped density as the physical attributes of the granules. The tableting p

APA, Harvard, Vancouver, ISO, and other styles

43

Yan, Ma, Xiong, Siesler, Qi, and Zhang. "Quantitative Analysis of Organic Liquid Three-Component Systems: Near-Infrared Transmission versus Raman Spectroscopy, Partial Least Squares versus Classical Least Squares Regression Evaluation and Volume versus Weight Percent Concentration Units." Molecules 24, no. 19 (2019): 3564. http://dx.doi.org/10.3390/molecules24193564.

Full text

Abstract:

The band shapes and band positions of near-infrared (NIR) and Raman spectra change depending on the concentrations of specific chemical functionalities in a multicomponent system. To elucidate these effects in more detail and clarify their impact on the analytical measurement techniques and evaluation procedures, NIR transmission spectra and Raman spectra of two organic liquid three-component systems with variable compositions were analyzed by two different multivariate calibration procedures, partial least squares (PLS) and classical least-squares (CLS) regression. Furthermore, the effect of

APA, Harvard, Vancouver, ISO, and other styles

44

Wen, Wen, Zhifeng Hao, and Xiaowei Yang. "A heuristic weight-setting strategy and iteratively updating algorithm for weighted least-squares support vector regression." Neurocomputing 71, no. 16-18 (2008): 3096–103. http://dx.doi.org/10.1016/j.neucom.2008.04.022.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

CAN, Mehmet Fatih, and Cemil KARA. "Evaluation of the effect of morphological traits on fish growth by comparison using ridge and ordinary least squares regression." Journal of Biometry Studies 4, no. 1 (2024): 31–41. http://dx.doi.org/10.61326/jofbs.v4i1.03.

Full text

Abstract:

Growth in fish is characterized by length and weight and studies that encompassing relationships among the fish length and weigh with some morphometric traits provide crucial information in the field of fish biology. High correlations (or multicollinearity) among the morphometric traits in fish morphology studies is a well-known phenomenon. If the relationship between growth and morphometry is modeled using the ordinary least squares estimator (OLS), the parameter estimates are likely to be too large in absolute value and possibly have the wrong sign due to the problem of multicollinearity. Th

APA, Harvard, Vancouver, ISO, and other styles

46

Yan, Chao Yong, and Yao Jun Yu. "The Quality Prediction in Small-Batch Producing Process Based on Weighted Least Squares Support Vector Regression." Advanced Materials Research 542-543 (June 2012): 411–15. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.411.

Full text

Abstract:

A novel quality prediction method with mobile time window is proposed for small-batch producing process based on weighted least squares support vector regression (LS-SVR). The design steps and learning algorithm are also addressed. In the method, weighted LS-SVR is taken as the intelligent kernel, with which the small-batch learning is solved well and the nearer sample is set a larger weight, while the farther is set the smaller weight in the history data. A typical machining process of cutting bearing outer race is carried out and the real measured data are used to contrast experiment. The ex

APA, Harvard, Vancouver, ISO, and other styles

47

Burm, Jin Pil. "The influence of weight with assay error on gentamicin pharmacokinetics using the Bayesian and nonlinear least square regression analysis in appendicitis patients." Biopharmaceutics & Drug Disposition 26, no. 5 (2005): 189–94. http://dx.doi.org/10.1002/bdd.450.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Gruszczyński, Stanisław. "An Evaluation of Some Machine Learning Algorithms as Tools for Predicting Soil Characteristics Based on Their Spectral Response in the Vis‑NIR Range." Geomatics and Environmental Engineering 15, no. 1 (2021): 63–95. http://dx.doi.org/10.7494/geom.2021.15.1.63.

Full text

Abstract:

Using the Land Use and Coverage Frame Survey (LUCAS) database of European soil surface layer properties, statistical and machine learning predictive models for several key soil characteristics (clay content, pH in CaCl2, concentration of organic carbon, calcium carbonates and nitrogen and exchange cations capacity) were compared on the basis of processing their spectral responses in the visible (Vis) and near‑infrared (NIR) parts. Standard methods of relationship modeling were used: stepwise regression, partial least squares regression and linear regression with input data obtained from princi

APA, Harvard, Vancouver, ISO, and other styles

49

Sim, Siong Fong, Min Xuan Laura Chai, and Amelia Laccy Jeffrey Kimura. "Prediction of Lard in Palm Olein Oil Using Simple Linear Regression (SLR), Multiple Linear Regression (MLR), and Partial Least Squares Regression (PLSR) Based on Fourier-Transform Infrared (FTIR)." Journal of Chemistry 2018 (November 8, 2018): 1–8. http://dx.doi.org/10.1155/2018/7182801.

Full text

Abstract:

Fourier-transform infrared (FTIR) offers the advantages of rapid analysis with minimal sample preparation. FTIR in combination with multivariate approach, particularly partial least squares regression (PLSR), has been widely used for adulterant analysis. Limited study has been done to compare PLSR with other regression strategies. In this paper, we apply simple linear regression (SLR), multiple linear regression (MLR), and PLSR for prediction of lard in palm olein oil. Pure palm olein oil was adulterated with lard at different concentrations and subjected to analysis with FTIR. The marker band

APA, Harvard, Vancouver, ISO, and other styles

50

Yan, Min, Bing Sen Zhang, and Dong Mei Wang. "Research on Application of Polynomial Regression Analysis for Computer Color Matching in Textile Dyeing." Applied Mechanics and Materials 602-605 (August 2014): 719–22. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.719.

Full text

Abstract:

The traditional color matching method for textile dyeing by experiences cannot meet the needs of modern production for color matching in textile dyeing. In order to solve this problem, a mathematical model for the three stimulus values CMY and the dye weight concentrations has been established by collecting and analyzing the actual data from a factory. We analyze this model by polynomial least square approximation and polynomial regression, and then we solve the dye concentrations with the Newton iteration method. Verification results show that the error between the experimental values and the

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!