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Journal articles on the topic 'Ordinary least square'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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1

Park, Min Jung, and Soon Man Kwon. "Socioeconomic Determinants of Korean Medicine Ambulatory Services: Comparing Panel Fixed Effect Model with Pooled Ordinary Least Square." Health Policy and Management 24, no. 1 (March 31, 2014): 47–55. http://dx.doi.org/10.4332/kjhpa.2014.24.1.47.

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2

Benouaz, T., and O. Arino. "Least square approximation of a nonlinear ordinary differential equation." Computers & Mathematics with Applications 31, no. 8 (April 1996): 69–84. http://dx.doi.org/10.1016/0898-1221(96)00032-6.

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3

Rashid, Intan Maizura Abd. "Determinants of FDI Inflows in Agriculture Sector Using Pooled Ordinary Least Square (OLS), Pooled Generalized Least Square (GLS), Augmented Dickey-Fuller (ADF) and Philips-perron Unit Root Test." International Journal of Psychosocial Rehabilitation 24, no. 5 (April 20, 2020): 2560–67. http://dx.doi.org/10.37200/ijpr/v24i5/pr201955.

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4

Tabass, Manije Sanei, and G. R. Mohtashami Borzadaran. "A comparison of generalised maximum entropy and ordinary least square." International Journal of Information and Decision Sciences 10, no. 4 (2018): 297. http://dx.doi.org/10.1504/ijids.2018.095495.

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Tabass, Manije Sanei, and G. R. Mohtashami Borzadaran. "A comparison of generalised maximum entropy and ordinary least square." International Journal of Information and Decision Sciences 10, no. 4 (2018): 297. http://dx.doi.org/10.1504/ijids.2018.10016400.

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Tong, Hongzhi, Di-Rong Chen, and Fenghong Yang. "Least Square Regression with lp-Coefficient Regularization." Neural Computation 22, no. 12 (December 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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Sadoghi Yazdi, Hadi, Morteza Pakdaman, and Hamed Modaghegh. "Unsupervised kernel least mean square algorithm for solving ordinary differential equations." Neurocomputing 74, no. 12-13 (June 2011): 2062–71. http://dx.doi.org/10.1016/j.neucom.2010.12.026.

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8

Abramov, A. A., and L. F. Yukhno. "The Least Square Method for Systems of Linear Ordinary Differential Equations." Computational Mathematics and Mathematical Physics 59, no. 6 (June 2019): 915–25. http://dx.doi.org/10.1134/s0965542519060022.

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9

Enjarwati, Tria. "IMPACT OF GOVERNMENT FISCAL SPACE AND MANPOWER TO THE GROSS DOMESTIC PRODUCTS OF INDONESIA PERIOD 1990-2015." Journal of Developing Economies 3, no. 1 (July 31, 2018): 20. http://dx.doi.org/10.20473/jde.v3i1.8562.

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To purpose of this study was to examine and analyze the effect of fiscal space and labour absorption to Indonesia Gross Domestic Product (GDP) within period 1990-2015. This study uses the least squares method or Ordinary Least Square (OLS) with time series data. Variables used in this study is the Gross Domestic Product (GDP) as the dependent variable, whereas for independent variables using the fiscal space and labour absorption. The results of regression calculations using the least squares method or Ordinary Least Square (OLS) in this study indicate that the fiscal space variable has a positive significant effect, and labour absorption variable has a positive significant effect to indonesia Gross Domestic Product (GDP).
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10

Yeniay, Özgür, Öznur İşçi, Atilla Göktaş, and M. Niyazi Çankaya. "Time Scale in Least Square Method." Abstract and Applied Analysis 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/354237.

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Study of dynamic equations in time scale is a new area in mathematics. Time scale tries to build a bridge between real numbers and integers. Two derivatives in time scale have been introduced and called as delta and nabla derivative. Delta derivative concept is defined as forward direction, and nabla derivative concept is defined as backward direction. Within the scope of this study, we consider the method of obtaining parameters of regression equation of integer values through time scale. Therefore, we implemented least squares method according to derivative definition of time scale and obtained coefficients related to the model. Here, there exist two coefficients originating from forward and backward jump operators relevant to the same model, which are different from each other. Occurrence of such a situation is equal to total number of values of vertical deviation between regression equations and observation values of forward and backward jump operators divided by two. We also estimated coefficients for the model using ordinary least squares method. As a result, we made an introduction to least squares method on time scale. We think that time scale theory would be a new vision in least square especially when assumptions of linear regression are violated.
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Yang, Y., G. Su, Y. Li, F. Liu, and Z. Lin. "A LEAST SQUARE ALGORITHM FOR GEOMETRIC MATCHING OF REMOTE SENSED IMAGES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 121–25. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-121-2020.

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Abstract. The aim of geometric matching is to extract the geometric transformation parameters between the corresponding images. That is useful for photogrammetric mapping, deformation detection, and flying platform's posture analyses, etc. It is different compare with ordinary feature based image matching succeed by selecting feature points correctly, the proposed method takes all the pixels within the corresponding images to participate the matching procedure for calculating the geometric parameters by least square criterion. The principle of the algorithm, such as the gray corresponding equation, the information quantity inequation and procedure of least square solution are introduced in detail. Particularly, the wavelet analyses for gray signal and calculating the information quantity by signal to noise ratio. Finally, a series of sequential images obtained by a low-altitude helicopter equipped with a video camera was used to test and verify the validity and reliability of the theory and algorithm in this paper. Two typical results are got according to the relative orientation elements model and parallax grid model. The conclusion is got in comparing APM with ordinary feature point method by the information quantity inequation.
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Merlin, Matumona Lubabu, and Yinfei Chen. "Analysis of the factors affecting electricity consumption in DR Congo using fully modified ordinary least square (FMOLS), dynamic ordinary least square (DOLS) and canonical cointegrating regression (CCR) estimation approach." Energy 232 (October 2021): 121025. http://dx.doi.org/10.1016/j.energy.2021.121025.

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Yanuar, Ferra. "The Simulation Study to Test the Performance of Quantile Regression Method With Heteroscedastic Error Variance." CAUCHY 5, no. 1 (November 30, 2017): 36. http://dx.doi.org/10.18860/ca.v5i1.4209.

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<div><p class="Keywords">The purpose of this article was to describe the ability of the quantile regression method in overcoming the violation of classical assumptions. The classical assumptions that are violated in this study are variations of non-homogeneous error or heteroscedasticity. To achieve this goal, the simulated data generated with the design of certain data distribution. This study did a comparison between the models resulting from the use of the ordinary least squares and the quantile regression method to the same simulated data. Consistency of both methods was compared with conducting simulation studies as well. This study proved that the quantile regression method had standard error, confidence interval width and mean square error (MSE) value smaller than the ordinary least squares method. Thus it can be concluded that the quantile regression method is able to solve the problem of heteroscedasticity and produce better model than the ordinary least squares. In addition the ordinary least squares is not able to solve the problem of heteroscedasticity.</p></div>
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14

Tusilowati, Tusilowati, L. Handayani, and Rais Rais. "SIMULASI PENANGANAN PENCILAN PADA ANALISIS REGRESI MENGGUNAKAN METODE LEAST MEDIAN SQUARE (LMS)." JURNAL ILMIAH MATEMATIKA DAN TERAPAN 15, no. 2 (December 6, 2018): 238–47. http://dx.doi.org/10.22487/2540766x.2018.v15.i2.11362.

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The simulation of handling of outliers on regression analysis used the method which was commonly used to predict the parameter in regression analysis, namely Least Median Square (LMS) due to the simple calculation it had. The data with outliers would result in unbiased parameter estimate. Hence, it was necessary to draw up the robust regression to overcome the outliers. The data used were simulation data of the number of data pairs ( X,Y) by 25 and 100 respectively. The result of the simulation was divided into 5 subsets of data cluster of parameter regression prediction by Ordinary Least Square (OLS) and Least Median Square (LMS) methods. The prediction result of the parameter of each method on each subset of data cluster was tested with both method to discover the which better one. Based on the research findings, it was found that The Least Median Square (LMS) method was known better than Ordinary Least Square (OLS) method in predicting the regression parameter on the data which had up to 3% of the percentage of the outlier.
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15

Yang, Hong Ying, Shuang Lei Feng, Bo Wang, Wei Sheng Wang, and Chun Liu. "Hybrid Corrected Approach for Wind Power Forecasting Based on Ordinary Least Square Method." Advanced Materials Research 846-847 (November 2013): 1392–97. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1392.

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This paper shows an application of Ordinary Least Square (OLS) in the wind power forecasting field. The OLS algorithm is applied to obtain the estimated parameter of the hybrid correction model, and then the properly structured correction model was used to correct the forecasting errors form the physical forecasting method and the statistical forecasting method. Satisfactory experimental results are obtained for day-ahead forecast by using actual wind power data.
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16

Prasada, I. M. Y., and Masyhuri. "Food security in Java Island, Indonesia: Panel data of ordinary least square approach." IOP Conference Series: Earth and Environmental Science 346 (October 14, 2019): 012065. http://dx.doi.org/10.1088/1755-1315/346/1/012065.

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17

Xie, Sheng Dong, Ai Qun Hu, and Yi Huang. "Nonlinear Least Square Localization Algorithm Based on Time Difference of Arrival." Applied Mechanics and Materials 411-414 (September 2013): 903–6. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.903.

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TDOA is a predominant localization algorithm. In this paper, we propose a TDOA localization algorithm called NLLS, using the nonlinear least square estimation. We first get the initial location utilizing the LCLS algorithm, which could achieve the global optimal solution to the ordinary constraint linear least square estimation, and based on the initial location, we make use of nonlinear least square estimation to improve the localization precision. Simulation results show that compared with the CTLS algorithm, its performance is superior especially when the measurement noise is larger.
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18

Prime, Sunantha. "Forecasting the changes in daily stock prices in Shanghai Stock Exchange using Neural Network and Ordinary Least Squares Regression." Investment Management and Financial Innovations 17, no. 3 (October 1, 2020): 292–307. http://dx.doi.org/10.21511/imfi.17(3).2020.22.

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The research focuses on finding a superior forecasting technique to predict stock movement and behavior in the Shanghai Stock Exchange. The author’s interest is in stock market activities during high volatility, specifically 13 years from 2002 to 2015. This volatile period, fueled by events such as the dot-com bubble, SARS outbreak, political leadership transitions, and the global financial crisis, is of interest. The study aims to analyze changes in stock prices during an unstable period. The author used advanced computer sciences, Machine Learning through information processing and training, and the traditional statistical approach, the Multiple Linear Regression Model, with the least square method. Both techniques are accurate predictors measured by Absolute Percent Error with a range of 1.50% to 1.65%, using a data file containing 3,283 observations generated to record the daily close prices of individual Chinese companies. The t-test paired difference experiment shows the superiority of Neural Network in the finance sector and potentially not in other sectors. The Multiple Linear Regression Model performs equivalent to the Neural Network in other sectors.
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19

M, Anila, and G. Pradeepini. "Least Square Regression for Prediction Problems in Machine Learning using R." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 960. http://dx.doi.org/10.14419/ijet.v7i3.12.17612.

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The most commonly used prediction technique is Ordinary Least Squares Regression (OLS Regression). It has been applied in many fields like statistics, finance, medicine, psychology and economics. Many people, specially Data Scientists using this technique know that it has not gone with enough training to apply it and should be checked why & when it can or can’t be applied.It’s not easy task to find or explain about why least square regression [1] is faced much criticism when trained and tried to apply it. In this paper, we mention firstly about fundamentals of linear regression and OLS regression along with that popularity of LS method, we present our analysis of difficulties & pitfalls that arise while OLS method is applied, finally some techniques for overcoming these problems.
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20

Adewoye, Kunle Bayo, Ayinla Bayo Rafiu, Titilope Funmilayo Aminu, and Isaac Oluyemi Onikola. "INVESTIGATING THE IMPACT OF MULTICOLLINEARITY ON LINEAR REGRESSION ESTIMATES." MALAYSIAN JOURNAL OF COMPUTING 6, no. 1 (March 9, 2021): 698. http://dx.doi.org/10.24191/mjoc.v6i1.10540.

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Multicollinearity is a case of multiple regression in which the predictor variables are themselves highly correlated. The aim of the study was to investigate the impact of multicollinearity on linear regression estimates. The study was guided by the following specific objectives, (i) to examined the asymptotic properties of estimators and (ii) to compared lasso, ridge, elastic net with ordinary least squares. The study employed Monte-carlo simulation to generate set of highly collinear and induced multicollinearity variables with sample sizes of 25, 50, 100, 150, 200, 250, 1000 as a source of data in this research work and the data was analyzed with lasso, ridge, elastic net and ordinary least squares using statistical package. The study findings revealed that absolute bias of ordinary least squares was consistent at all sample sizes as revealed by past researched on multicollinearity as well while lasso type estimators were fluctuate alternately. Also revealed that, mean square error of ridge regression was outperformed other estimators with minimum variance at small sample size and ordinary least squares was the best at large sample size. The study recommended that ols was asymptotically consistent at a specified sample sizes on this research work and ridge regression was efficient at small and moderate sample size.
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Peprah, M. S., and I. O. Mensah. "Performance evaluation of the Ordinary Least Square (OLS) and Total Least Square (TLS) in adjusting field data: an empirical study on a DGPS data." South African Journal of Geomatics 6, no. 1 (May 8, 2017): 73. http://dx.doi.org/10.4314/sajg.v6i1.5.

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22

Mugableh, Mohamed Ibrahim. "Does Monetary Policy Affect Economic Growth in Jordan? Evidence from Ordinary Least Square Models." International Business Research 12, no. 1 (December 6, 2018): 27. http://dx.doi.org/10.5539/ibr.v12n1p27.

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The main objective of this paper is to analyze equilibrium and dynamic causality relationships between monetary policy tools and economic growth in Jordan for the period (1990-2017). For this purpose, it considers the autoregressive distributed lag (ARDL) and vector error correction (VEC) models estimations. The results of ARDL approach show that monetary policy variables (i.e., real interest rate and money supply) have positive impact on economic growth in long-run and short-run except inflation rate. In addition, the results of VECM indicate bidirectional causal relationships between economic growth and monetary policy variables in long-run and short-run.
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Samantaray, Barsa, Kunal Kumar Das, and Jibendu Sekhar Roy. "Beamforming in Smart Antenna using Some Variants of Least Mean Square Algorithm." Circulation in Computer Science MCSP2017, no. 01 (September 24, 2017): 23–26. http://dx.doi.org/10.22632/ccs-2017-mcsp034.

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Beamforming and side lobe level reduction of smart antenna are important tasks in mobile network. Adaptive signal processing algorithms are used for beam forming in smart antenna. In this paper, variable step-size sign least mean square (VS-SLMS) algorithm is used for beam forming of smart antenna with linear antenna array. The results are compared with the results obtained using sign least mean square (SLMS) algorithm. Variable step-size algorithm shows good results for beam forming compared to ordinary constant step-size algorithm.
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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 (December 31, 2019): 61. http://dx.doi.org/10.29303/emj.v1i2.43.

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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 Generalized Least Square (GLS) method. This study aims to examine the use of the GLS method in overcoming heteroscedasticity in regression analysis and examine the comparison of estimation results using the OLS method with the GLS method in the case of heteroscedasticity.The results show that the GLS method was able to maintain the nature of the estimator that is not biased and consistent and able to overcome the problem of heteroscedasticity, so that the GLS method is more effective than the OLS method.
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LI, R., F. LI, and J. W. HUANG. "THE PREDICTIVE PERFORMANCE EVALUATION AND NUMERICAL EXAMPLE STUDY FOR THE PRINCIPAL COMPONENT TWO-PARAMETERS ESTIMATOR." Latin American Applied Research - An international journal 48, no. 3 (October 8, 2019): 181–86. http://dx.doi.org/10.52292/j.laar.2018.223.

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In this paper, detailed comparisons are given between those estimators that can be derived from the principal component two-parameter estimator such as the ordinary least squares estimator, the principal components regression estimator, the ridge regression estimator, the Liu estimator, the r-k estimator and the r-d estimator by the prediction mean square error criterion. In addition, conditions for the superiority of the principal component two-parameter estimator over the others are obtained. Furthermore, a numerical example study is conducted to compare these estimators under the prediction mean squared error criterion.
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Prananda, Dio, Idris Idris, and Dewi Zaini Putri. "DAMPAK KESEHATAN TERHADAP PERTUMBUHAN EKONOMI DI INDONESIA." Jurnal Ecogen 1, no. 3 (February 7, 2019): 578. http://dx.doi.org/10.24036/jmpe.v1i3.5028.

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This study aims to determine and analyze the impact of life expentacy, fertilitiy rates, morbidity rates, and investment on economic growth in Indonesia. This type of research is associative descriptive research, where the data used was secondary data from 1985 to 2015 obtained from related institutions, which are analyzed using the Ordinary Least Square (OLS) method. The findings of this study indicate that life expectancy, fertility rates, morbidity rates, and investment have a significant effect on economic growth in Indonesia. Keywords: life expectancy, fertility rates, morbidity rates, investment, and Ordinary Least Square (OLS)
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27

Ivanov, Dmitriy V., Ilya L. Sandler, and Natalya V. Chertykovtseva. "Estimation of Parameters of Hyperbolic Functions with Additive Noise." Advances in Science and Technology 105 (April 2021): 302–8. http://dx.doi.org/10.4028/www.scientific.net/ast.105.302.

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Hyperbolic functions are widely used to write solutions to ordinary differential equations and partial differential equations. These functions are nonlinear in parameters, which makes it difficult to estimate the parameters of these functions. In the paper, two-step algorithms for estimating the parameters of hyperbolic sine and cosine (sinh and cosh) in the presence of measurement errors are proposed. At the first step, the hyperbolic function is transformed into a linear difference equation (autoregression) of the second order. Estimation in the presence of noise of observation of autoregression parameters using ordinary least square (OLS) gives biased estimates. Modifications of the two-stage estimation algorithm based on the use of the method of total least squares (TLS) and the method of extended instrumental variables (EIV), hyperbolic sine and cosine in the presence of errors in measurements are proposed. Numerical experiments have shown that the accuracy of the parameter estimation using the proposed modifications is higher than the accuracy of the estimate obtained using the ordinary least squares method (OLS).
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Shen, Gao Zhan, Tao Zhang, Ying Xu, and Yan Xing Wei. "Research and Experiment on Nonlinear Correction Algorithm of Metal Tube Rotameter." Advanced Materials Research 301-303 (July 2011): 1123–27. http://dx.doi.org/10.4028/www.scientific.net/amr.301-303.1123.

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The calibration curve of some metal tube rotameter has the characteristic of mutation. If the nonlinear least square method is still used as the method of linear correction, it will increase the measurement error and reduce the measurement accuracy. This paper presents a Division Ordinary Least Squares Method, which can reduce errors and improve accuracy. By the algorithm comparative experiment it can be proved that the method can improve the measurement accuracy.
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Sadollah, Ali, Hadi Eskandar, Do Guen Yoo, and Joong Hoon Kim. "Approximate solving of nonlinear ordinary differential equations using least square weight function and metaheuristic algorithms." Engineering Applications of Artificial Intelligence 40 (April 2015): 117–32. http://dx.doi.org/10.1016/j.engappai.2015.01.014.

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30

You, Qiang, JinXin Xu, Gang Wang, and Zhonghua Zhang. "Uncertainty evaluation for ordinary least-square fitting with arbitrary order polynomial in joule balance method." Measurement Science and Technology 27, no. 1 (December 10, 2015): 015010. http://dx.doi.org/10.1088/0957-0233/27/1/015010.

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31

Leng, Ling, Tianyi Zhang, Lawrence Kleinman, and Wei Zhu. "Ordinary least square regression, orthogonal regression, geometric mean regression and their applications in aerosol science." Journal of Physics: Conference Series 78 (July 1, 2007): 012084. http://dx.doi.org/10.1088/1742-6596/78/1/012084.

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32

De-Graft Acquah, Henry. "Comparing ols and rank-based estimation techniques for production analysis: An application to Ghanaian maize farms." Applied Studies in Agribusiness and Commerce 10, no. 4-5 (December 31, 2016): 125–30. http://dx.doi.org/10.19041/apstract/2016/4-5/16.

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This paper introduces the rank-based estimation method to modelling the Cobb-Douglas production function as an alternative to the least squares approach. The intent is to demonstrate how a nonparametric regression based on a rank-based estimator can be used to estimate a Cobb-Douglas production function using data on maize production from Ghana. The nonparametric results are compared to common parametric specification using the ordinary least squares regression. Results of the study indicate that the estimated coefficients of the CobbDouglas Model using the Least squares method and the rank-based regression analysis are similar. Findings indicated that in both estimation techniques, land and Equipment had a significant and positive influence on output whilst agrochemicals had a significantly negative effect on output. Additionally, seeds which also had a negative influence on output was found to be significant in the robust rank-based estimation, but insignificant in the ordinary least square estimation. Both the least squares and rank-based regression suggest that the farmers were operating at an increasing returns to scale. In effect this paper demonstrate the usefulness of the rank-based estimation in production analysis. JEL CODE: Q18, D24, Q12, C1 and C67
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Marliana, Reny Rian. "PARTIAL LEAST SQUARE-STRUCTURAL EQUATION MODELING PADA HUBUNGAN ANTARA TINGKAT KEPUASAN MAHASISWA DAN KUALITAS GOOGLE CLASSROOM BERDASARKAN METODE WEBQUAL 4.0." Jurnal Matematika, Statistika dan Komputasi 16, no. 2 (December 19, 2019): 174. http://dx.doi.org/10.20956/jmsk.v16i2.7851.

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AbstractThis paper studied a relationship between the quality of google classroom and student satisfaction. The quality of google classroom measured based on the Webqual 4.0 which is consists of four variables i.e. information quality, service interaction quality, user interface quality and usability. The relationship modeling between these latent variables and the student satisfaction was done by using the Partial Least Square-Structural Equation Modeling (PLS-SEM). Estimation parameters of the model used PLS-SEM algorithm and Ordinary Least Square (OLS) method. Data was collected using a questionnaire to 89 students of google classroom’s Probability and Statistics Course, Odd Semester 2017-2018 at STMIK Sumedang. The result showed the information quality, the service interaction quality and the user interface quality does not have significantly influence of the student satisfaction. Each of the total effects are 0.149; 0.011 and -0.155. While the usability has a significant effect to the student satisfaction positively with total effect 0.707. Keywords : partial least squares, pls-sem, webqual 4.0 AbstrakHubungan antara kualitas google classroom dan tingkat kepuasan mahasiswa dipelajari pada paper ini. Kualitas google classroom diukur berdasarkan metode Webqual 4.0 yang terdiri atas empat variabel laten yaitu kualitas informasi, kualitas interaksi layanan, kualitas antar muka pengguna dan kegunaa. Pemodelan hubungan antara keempat laten variabel tersebut dengan tingkat kepuasan mahasiswa dilakukan dengan menggunakan Partial Least Squares- Structural Equation Modeling (PLS-SEM). Estimasi parameter model dilakukan dengan menggunakan algoritma PLS-SEM yang didasarkan pada metode Ordinary Least Square (OLS). Data penelitian diperoleh melalui penyebaran 89 kuesioner terhadap mahasiswa yang terdaftar pada google classroom mata kuliah Probabilitas dan Statistika pada Semester Gasal 2017-2018 di STMIK Sumedang. Hasil analisis menunjukkan bahwa kualitas informasi, kualitas antar muka penggunan dan kualitas interaksi layanan tidak berpengaruh secara signifikan terhadap tingkat kepuasan mahasiswa dengan total pengaruh berturut-turut 0,149; 0,011 dan -0,155. Sementara variabel kegunaan berpengaruh secara signifikan terhadap tingkat kepuasan mahasiswa dengan total pengaruh sebesar 0,707. Keywords : partial least squares, pls-sem, webqual 4.0
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Green, Edwin J., and William E. Strawderman. "Stein-rule estimation of coefficients for 18 eastern hardwood cubic volume equations." Canadian Journal of Forest Research 16, no. 2 (April 1, 1986): 249–55. http://dx.doi.org/10.1139/x86-044.

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A Stein-rule estimator, which shrinks least squares estimates of regression parameters toward their weighted average, was employed to estimate the coefficient in the constant form factor volume equation for 18 species simultaneously. The Stein-rule procedure was applied to ordinary least squares estimates and weighted least squares estimates. Simulation tests on independent validation data sets revealed that the Stein-rule estimates were biased, but predicted better than the corresponding least squares estimates. The Stein-rule procedures also yielded lower estimated mean square errors for the volume equation coefficient than the corresponding least squares procedure. Different methods of withdrawing sample data from the total sample available for each species revealed that the superiority of Stein-rule procedures over least squares decreased as the sample size increased and that the Stein-rule procedures were robust to unequal sample sizes, at least on the scale studied here.
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Hosen, Md Arif, Sujan Chandra Paul, and Md Harun Or Rosid. "Impact of democracy on literacy rate." International Journal of Research in Business and Social Science (2147- 4478) 9, no. 7 (December 12, 2020): 204–11. http://dx.doi.org/10.20525/ijrbs.v9i7.968.

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This study investigates the impact of democracy indices on the literacy rate. Panel Data of 134 Countries from 2007-2018 were collected from the website the World Bank and Gapminder. This study uses Ordinary Least Square (OLS), Pooled Ordinary Least Square (POLS), Driscoll-Kraay (DK), Second Stage Least Square (2SLS), Generalized Methods of Moments (GMM) methods. This research has found that political participation index and political culture index has a significant positive relationship with literacy rate in all the method. The functioning of the government index has a significant positive relationship and electoral process and the pluralism index has a significant negative relationship with literacy rate in all the methods except the GMM method. The civil liberties index has a significant negative relationship with literacy rate in POLS and in the other models, there is no significant relationship between the civil liberties index and literacy rate.
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Challa, Ratna Kumari, Siva Prasad Chintha, B. Reddaiah, and Kanusu Srinivasa Rao. "A Novel Fast Searching Algorithm Based on Least Square Regression." Revue d'Intelligence Artificielle 35, no. 1 (February 28, 2021): 93–98. http://dx.doi.org/10.18280/ria.350111.

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Currently, the machine learning group is well-understood and commonly used for predictive modelling and feature generation through linear methodologies such as reversals, principal analysis and canonical correlation analyses. All these approaches are typically intended to capture fascinating subspaces in the original space of high dimensions. These methods have all a closed-form approach because of its simple linear structures, which makes estimation and theoretical analysis for small datasets very straightforward. However, it is very common for a data set to have millions or trillions of samples and features in modern machine learning problems. We deal with the problem of fast estimation from large volumes of data for ordinary squares. The search operation is a very important operation and it is useful in many applications. Some applications when the data set size is large, the linear search takes the time which is proportional to the size of the data set. Binary search and interpolation search performs good for the search of elements in the data set in O(logn) and ⋅O(log(⋅logn)) respectively in the worst case. Now, in this paper, an effort is made to develop a novel fast searching algorithm based on the least square regression curve fitting method. The algorithm is implemented and its execution results are analyzed and compared with binary search and interpolation search performance. The proposed model is compared with the traditional methods and the proposed fast searching algorithm exhibits better performance than the traditional models.
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Salmerón Gómez, Román, Ainara Rodríguez Sánchez, Catalina García García, and José García Pérez. "The VIF and MSE in Raise Regression." Mathematics 8, no. 4 (April 16, 2020): 605. http://dx.doi.org/10.3390/math8040605.

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The raise regression has been proposed as an alternative to ordinary least squares estimation when a model presents collinearity. In order to analyze whether the problem has been mitigated, it is necessary to develop measures to detect collinearity after the application of the raise regression. This paper extends the concept of the variance inflation factor to be applied in a raise regression. The relevance of this extension is that it can be applied to determine the raising factor which allows an optimal application of this technique. The mean square error is also calculated since the raise regression provides a biased estimator. The results are illustrated by two empirical examples where the application of the raise estimator is compared to the application of the ridge and Lasso estimators that are commonly applied to estimate models with multicollinearity as an alternative to ordinary least squares.
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DEVITA, HANY, I. KOMANG GDE SUKARSA, and I. PUTU EKA N. KENCANA. "KINERJA JACKKNIFE RIDGE REGRESSION DALAM MENGATASI MULTIKOLINEARITAS." E-Jurnal Matematika 3, no. 4 (November 28, 2014): 146. http://dx.doi.org/10.24843/mtk.2014.v03.i04.p077.

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Ordinary least square is a parameter estimations for minimizing residual sum of squares. If the multicollinearity was found in the data, unbias estimator with minimum variance could not be reached. Multicollinearity is a linear correlation between independent variabels in model. Jackknife Ridge Regression(JRR) as an extension of Generalized Ridge Regression (GRR) for solving multicollinearity. Generalized Ridge Regression is used to overcome the bias of estimators caused of presents multicollinearity by adding different bias parameter for each independent variabel in least square equation after transforming the data into an orthoghonal form. Beside that, JRR can reduce the bias of the ridge estimator. The result showed that JRR model out performs GRR model.
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Lin, Yanli, Guannan Chu, Caiyuan Lin, and Yongda Yan. "An optimized constitutive model for reproducing flow stress–strain relationships of anisotropic materials." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 233, no. 4 (May 22, 2018): 1357–68. http://dx.doi.org/10.1177/0954406218771100.

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Due to the strong anisotropic property of the advanced metal materials used in automobile, aviation, and aerospace, experimental flow stress–strain relations including different stress states are necessary to provide the information of anisotropic hardening and plastic flow for constructing a constitutive model. Therefore, reasonably reproducing the experimental stress–strain relations is the most fundamental work to substitute adequate flow stress–strain curves into the constitutive equation at the same time. However, accurate and stable regression results are difficult to obtain through the current regression models such as power exponent, second-order function model, fourth-order function model, and so forth. In this paper, an optimized model named as a least square quadratic regression model (ordinary least square model) was proposed based on the most useful second-order function model. The significant difference is that all experimental points are used to reproduce the experimental stress–strain relations in ordinary least square model in place of only three experimental points adopted in second-order function model, which results in good regression accuracy. Through comparison, it is found that the regression results by power function are poor with regard to some experimental results, and the results reproduced by second-order function model or fourth-order function model are very sensitive to the experimental points selected to do the regression. The sum of squares for error (SSE) increases sharply when the selected points are unreasonable. In addition, for second-order function and fourth-order function models, only limited experimental points are adopted to do the regression, the best regression accuracy cannot be obtained even if the selected points are reasonable. In contrast, SSE of the regression curve by ordinary least square model reduces to less than 50% of the best regressed result by second-order function model, the yielding behavior and variable strain increment ratio of the anisotropic materials can be reflected more accurately. This is very important for accurately describing the plastic flow behaviors of anisotropic materials.
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Hossain, Altaf, Arnab Kumar Podder, and Mohammed Asaduzzaman. "How Does Remittance Impact on Economic Development in South Asia? A Dynamic Ordinary Least-Square Approach." Indian Economic Journal 67, no. 1-2 (June 2019): 68–81. http://dx.doi.org/10.1177/0019466220941427.

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This paper aims to examine the impacts of remittances on the economic development of four South Asian countries: Bangladesh, India, Pakistan and Sri Lanka. The study considers the World Bank’s balanced panel data (1977–2017). The dynamic ordinary least-square approach finds mixed results, which explain the realisation of the pluralistic approach. Evidence shows that remittances directly increase gross domestic product, household consumption and expenditure and decreases government consumption expenditure, inflation and population growth. However, the flow of remittances is not promoting private bank credit; rather it increases import-dominated trade. Besides, remittances are yet to be significant at gross capital formation. Therefore, we recommend that policymakers should find a way to bring remittance through formal channels for capital formation and investment. Fortunately, remittance inflows indirectly cause population control and sustainability. Decision-makers should also formulate policies that will modify remittance-induced current production and consumption patterns to promote the process of sustainable development further.
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Ashenagar, Samaneh, and Farshid Ziaee. "Estimation of reactivity ratios of styrene/butyl acrylate copolymer by ordinary and generalized least square methods." Iranian Polymer Journal 22, no. 7 (April 24, 2013): 511–18. http://dx.doi.org/10.1007/s13726-013-0147-1.

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Et.al, Abdul Hadi Bhatti. "Least Square Methods Based on Control Points of Said Ball Curves for Solving Ordinary Differential Equations." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 11, 2021): 2597–607. http://dx.doi.org/10.17762/turcomat.v12i3.1261.

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This paper presents the use of Said Ball curve’s control points to approximate the solutions of linear ordinary differential equations (ODEs). Least squares methods (LSM) is proposed to find the control points of Said Ball curves by minimizing the error of residual function.The residual error is measured by taking the sum of squares of the Said Ball curve’s control points of the residual function. Then the approximate solution of ODEs is obtained by minimizing residual error.Two numerical examples are given in term of error and compared with the exact solution to demonstrate the efficiency of the proposed method.
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Angriany, A. Muthiah Nur, Georgina Maria Tinungki, and Raupong Raupong. "Estimasi Komponen Variansi pada Rancangan Faktorial Acak Lengkap Menggunakan Metode Generalized Least Squares." Jurnal Matematika Statistika dan Komputasi 15, no. 2 (December 20, 2018): 54. http://dx.doi.org/10.20956/jmsk.v15i2.5714.

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AbstractsExperiment design is a test or a row of test by using both statistical description and inference statistical. The aim of this test is to change an input to become an output as a respond of the experiment. In the experiment design, variance of factor A, B , AB error of variance are called as variant component. The aim of this study is to estimate variance component on complete random factorial design for fixed model and mixed model by using Generalized Least Squares (GLS)method, where GLS method as a development of Ordinary Least Square method. It used to be applied on data of complete random factorial design, namely like the influence to density pelleting food which is caused by increasing adhesive material and longtime in storage. The results show that there is no influence of increasing adhesive material to the density of pelleting food. In addition, there exist of diversity of longtime of storage and there exists a diversity interaction between adding adhesive material and long of time of storage to the density of pelleting food Keywords: Generalized Least Squares, variance component, complete random factorial design AbstrakPerancangan percobaan adalah suatu uji atau sederet uji baik itu menggunakan statistika deskripsi maupun statistika inferensi, yang bertujuan untuk mengubah peubah input menjadi suatu output yang merupakan respon dari percobaan tersebut. Dalam perancangan percobaan, variansi dari faktor A, variansi dari faktor B, variansi interaksi faktor AB, dan variansi galat disebut dengan komponen varian. Penelitian ini bertujuan untuk mengestimasi komponen variansi pada rancangan faktorial acak lengkap model tetap dan model campuran menggunakan metode Generalized Least Squares (GLS), dimana metode GLS adalah pengembangan dari metode Ordinary Least Square yang biasa digunakan untuk mengatasi asumsi homogenitas yang biasa dilanggar dalam perancangan percobaan. Metode tersebut diterapkan pada data rancangan faktorial acak lengkap yaitu pengaruh berat jenis pakan pellet dengan kombinasi perlakuan penambahan bahan perekat dan lama penyimpanan. Hasil menunjukkan bahwa tidak terdapat pengaruh penambahan bahan perekat terhadap berat jenis pakan pellet. Selain itu, terdapat keragaman faktor lama penyimpan dan terdapat keragaman interaksi antara faktor penambahan perekat dan lama penyimpanan terhadap berat jenis pakan pellet. Kata kunci: Generalized Least Squares, komponen variansi, rancangan faktorial acak lengkap
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Subardini, Subardini. "Analisis Kontribusi Sektor Pariwisata terhadap Produk Domestik Regional Bruto Provinsi Jawa Timur." Jurnal Ilmiah Administrasi Bisnis dan Inovasi 1, no. 2 (March 28, 2018): 102. http://dx.doi.org/10.25139/jai.v1i2.815.

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Produk Domstik Regional Bruto ( PDRB ) menceeminkan pertumbuhan eknomi di suatu wilayah yang disebabkan berbagai sektor Salah satunya sektor Pariwisata, khususnya di Provinsi Jawa Timur. Penelitian ini ingin mengetahui kontribusi sektor pariwisata yang dilihat dari varibel investasi di bidang hotel, jumlah wisatawan asing dan lama mereka tinggal. Dari hasil analisis data time series selama 10 tahun dengan methode Ordinary Least Square ( OLS ) menunjukan bahwa, sektor Pariwisata berdasarkan ketiga variabel penelitian tersebut mempunyai kontribusi positif dan signifikan terhadap Produk Domestik Regional Bruto Provinsi Jawa Timur secara bersama-sama (simultan), akan tetapi secara partial yang kontribusinya signifikan variabel investasi hotel dan jumlah wisatawan asing ; sedangkan lama tinggal wisatawan kontribusinya tidak signifikanKata Kunci: PDRB Jawa Timur, Sektor Pariwisata, Ordinary Least Square
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45

Lu, Yuan, and Xiang Hong Cheng. "Non-Linear Least Squares Large Misalignment Estimation in Transfer Alignment." Advanced Materials Research 989-994 (July 2014): 1962–68. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1962.

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Large misalignment is unavoidable for subsystems which could be deployed randomly on the carriers such as shipborne aircrafts, AUV. Ordinary linear filtering algorithms don’t converge fast and accurately in non-linear conditions. It's critical for the accuracy of the transfer alignment. In this paper, a new misalignment and gyroscope bias online estimation method based on angular velocity processing is presented. Sensor measurements of M-SINS and S-SINS will be recorded for a certain period. Misalignment and the gyroscope bias will be calculated from these measurements directly with non-linear least square algorithm. Trust region method with pre-conditioning, subspace and conjugate gradient are applied for faster converge and better accuracy. Simulation results demonstrate the effectiveness of the algorithm.
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Işik, Sefa, and Fatih Cemil ÖZBUĞDAY. "The impact of agricultural input costs on food prices in Turkey: A case study." Agricultural Economics (Zemědělská ekonomika) 67, No. 3 (March 19, 2021): 101–10. http://dx.doi.org/10.17221/260/2020-agricecon.

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Food price inflation has been a significant subject of debate in Turkey since food prices soared in 2018. The study examines the linkage between agricultural input prices and food prices in Turkey by using quantitative method approaches with the monthly data spanning from 2015-M01 to 2020-M01. A co-integration analysis is performed using the autoregressive-distributed lag (ARDL) bounds test approach and Maki co-integration test with structural breaks. Additionally, the fully modified ordinary least square (FMOLS), dynamic ordinary least squares (DOLS), and canonical co-integrating regression (CCR) are applied to verify the results of the ARDL approach. The analysis demonstrates a significant, long-running relationship between agricultural input prices and food prices in Turkey. The long-run agricultural input price elasticities are found to be in the range of 1.30–1.36.
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Feng, Xia, and Brad Humphreys. "Assessing the Economic Impact of Sports Facilities on Residential Property Values." Journal of Sports Economics 19, no. 2 (February 9, 2016): 188–210. http://dx.doi.org/10.1177/1527002515622318.

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We estimate the effect of proximity to two sports facilities in Columbus, OH, on residential property values. Results from a spatial hedonic model indicate that the presence of sports facilities in Columbus have a significant, positive, and distance-decaying effect on surrounding residential housing values, supporting the idea that professional sports facilities generate intangible benefits in the local economy. Ordinary least square (OLS) overestimates the hedonic model parameters compared with maximum likelihood and spatial two-stage least squares, suggesting that spatial autocorrelation plays an important role in this setting.
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Nisak, Siti Choirun. "Seemingly Unrelated Regression Approach for GSTARIMA Model to Forecast Rain Fall Data in Malang Southern Region Districts." CAUCHY 4, no. 2 (June 3, 2016): 57. http://dx.doi.org/10.18860/ca.v4i2.3488.

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Time series forecasting models can be used to predict phenomena that occur in nature. Generalized Space Time Autoregressive (GSTAR) is one of time series model used to forecast the data consisting the elements of time and space. This model is limited to the stationary and non-seasonal data. Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA) is GSTAR development model that accommodates the non-stationary and seasonal data. Ordinary Least Squares (OLS) is method used to estimate parameter of GSTARIMA model. Estimation parameter of GSTARIMA model using OLS will not produce efficiently estimator if there is an error correlation between spaces. Ordinary Least Square (OLS) assumes the variance-covariance matrix has a constant error 𝜀𝑖𝑗~𝑁𝐼𝐷(𝟎,𝝈𝟐) but in fact, the observatory spaces are correlated so that variance-covariance matrix of the error is not constant. Therefore, Seemingly Unrelated Regression (SUR) approach is used to accommodate the weakness of the OLS. SUR assumption is 𝜀𝑖𝑗~𝑁𝐼𝐷(𝟎,𝚺) for estimating parameters GSTARIMA model. The method to estimate parameter of SUR is Generalized Least Square (GLS). Applications GSTARIMA-SUR models for rainfall data in the region Malang obtained GSTARIMA models ((1)(1,12,36),(0),(1))-SUR with determination coefficient generated with the average of 57.726%.
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Trisilia, Meilinda. "Analisis Standar Belanja untuk Penyusunan RKA-APBD Kegiatan Penyediaan Bahan Bacaan (Studi Pada SKPD di Pemerintah Kabupaten Lumajang Tahun 2015)." Jurnal Manajemen dan Bisnis Indonesia 4, no. 1 (October 1, 2016): 147–58. http://dx.doi.org/10.31843/jmbi.v4i1.108.

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Tuntutan transparansi dan akuntabilitas atas pengelolaan keuangan daerah semakin meningkat, karenanya untuk memenuhi tuntutan tersebut diperlukan pengelolaan keuangan daerah yang ekonomis, efisien, dan efektif. Penerapan ASB dimaksudkan untuk meningkatkan efisiensi, efektivitas dan ekonomi RKA-APBD. Penyusunan ASB ini bertujuan untuk membentuk model yang digunakan untuk menilai kewajaran beban kerja dan biaya dalam melaksanakan kegiatan penyediaan bahan bacaan di Kabupaten Lumajang. Penelitian ini merupakan penelitian deskriptif dan dalam analisis data menggunakan metode Ordinary Least Square (OLS). Memodelkan ASB dengan analisis regresi dapat menghasilkan model yang wajar, apabila kegiatan-kegiatan yang anggaran belanjanya tidak wajar tidak diikutsertakan (adanya outlier), sehingga model regresi memiliki ketepatan tinggi dalam memprediksi total belanja setiap kegiatan. Keywords : ASB, RKA-APBD, Ordinary Least Square (OLS), model regresi.
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Susanto, Joko, and Muhammad Arsya Wildan pratama. "DETERMINAN TINGKAT PENGANGGURAN TERBUKA DI D.I. YOGYAKARTA." Develop 5, no. 1 (April 1, 2021): 1. http://dx.doi.org/10.25139/dev.v5i1.3689.

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Studi ini bertujuan untuk mengetahui determinan pengangguran terbuka di Daerah Istimewa Yogyakarta. Penelitian ini menggunakan data sekunder yang diterbitkan oleh Badan Pusat Statistik tahun 2010-2018. Data tersebut mencakup tingkat pengangguran terbuka, pertumbuhan ekonomi, upah minimum kabupaten/kota, dan modal manusia. Penelitian ini menggunakan regresi panel berdasarkan Fully Modified Ordinary Least Square (FMOLS) dan Dynamic Ordinary Least Square (DOLS). Hasil penelitian menunjukkan bahwa pertumbuhan ekonomi berdampak negatif, sedangkan upah minimum kabupaten/kota berdampak positif terhadap pengangguran terbuka. Peningkatan pertumbuhan ekonomi mendorong penurunan pengangguran terbuka, sedangkan kenaikan upah minimum kabupaten diikuti oleh kenaikan tingkat pengangguran terbuka. Sementara itu, modal manusia tidak berpengaruh pada tingkat pengangguran terbuka akibat rendahnya rata-rata lama sekolah. Untuk itu, pemerintah perlu meningkatkan sumber daya manusia melalui wajib belajar 12 tahun.
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