Journal articles: 'Ordinary least square model'

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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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Watagoda, Lasanthi C. R. Pelawa. "A Sub-Model Theorem for Ordinary Least Squares." International Journal of Statistics and Probability 8, no. 1 (November 19, 2018): 40. http://dx.doi.org/10.5539/ijsp.v8n1p40.

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Variable selection or subset selection is an important step in the process of model fitting. There are many ways to select the best subset of variables including Forward selection, Backward elimination, etcetera. Ordinary least squares (OLS) is one of the most commonly used methods of fitting the final model. Final sub-model can perform poorly if the variable selection process failed to choose the right number of variables. This paper gives a new theorem and a mathematical proof to illustrate the reason for the poor performances, when using the least squares method after variable selection.

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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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Miessi Sanches, Fabio A., Daniel Junior Silva, and Sorawoot Srisuma. "ORDINARY LEAST SQUARES ESTIMATION OF A DYNAMIC GAME MODEL." International Economic Review 57, no. 2 (April 28, 2016): 623–34. http://dx.doi.org/10.1111/iere.12170.

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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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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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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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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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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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Weiss, Andrew A. "A Comparison of Ordinary Least Squares and Least Absolute Error Estimation." Econometric Theory 4, no. 3 (December 1988): 517–27. http://dx.doi.org/10.1017/s0266466600013438.

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In a linear-regression model with heteroscedastic errors, we consider two tests: a Hausman test comparing the ordinary least squares (OLS) and least absolute error (LAE) estimators and a test based on the signs of the errors from OLS. It turns out that these are related by the well-known equivalence between Hausman and the generalized method of moments tests. Particular cases, including homoscedasticity and asymmetry in the errors, are discussed.

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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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Shah Zainal Abidin, Irwan, Muhammad Haseeb, Lee Wen Chiat, and Md Rabiul Islam. "Determinants of Malaysia – BRICS trade linkages: gravity model approach." Investment Management and Financial Innovations 13, no. 2 (July 14, 2016): 389–98. http://dx.doi.org/10.21511/imfi.13(2-2).2016.14.

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The main objective of this study is to explore the long-run and short-run relationship between trade and other macroeconomic variables of Malaysian and the BRICS countries. To test relationship between trade and other macroeconomic variables, the empirical investigation will be conducted based on the dynamic ordinary least square (DOLS) and fully modify ordinary least square (FMOLS) model for the period 1980-2015. Results of both DOLS and FMOLS show that out of all the variables included in the model distance between Malaysia and BRICS countries and corruption of both side have negative affect on bilateral trade between them. Whereas, GDP, GDP per capita and trade to GDP ratio are positively contribute in the bilateral trade. However, inflation and exchange rate of Malaysia and BRCIS countries have no effect on the bilateral trade between Malaysia and BRICS countries. The findings suggest that economic strengthening as the basis for increase in trade between Malaysia and BRICS members. Investment appears to be complementary to the trading relations in the Malaysia-BRICS case. The social capital also plays role in supporting the trade

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Aflakhah, Zahrotul, Jajang Jajang, and Agustini Tripena Br. Sb. "KAJIAN METODE ORDINARY LEAST SQUARE DAN ROBUST ESTIMASI M PADA MODEL REGRESI LINIER SEDERHANA YANG MEMUAT OUTLIER." Jurnal Ilmiah Matematika dan Pendidikan Matematika 11, no. 1 (May 18, 2020): 21. http://dx.doi.org/10.20884/1.jmp.2020.12.1.1934.

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This research discusses about the Ordinary Least Squares (OLS) method and robust M-estimation method; compare between the Tukey bisquare and Huber weighting from simple linier regression models that contain outliers. Data are generated through simulation with the percentages of outliers and sample sizes. Each data will be formed into a simple linier regression model, then the percentage of outliers, RSE and MAD values are calculated. The results show that RSE and MAD values produced by a simple linear regression model with the OLS method are influenced by the percentage of outliers. However, the regression model of robust M-estimation with sample size 30, 60, 90, 120, and 150 results an unstable RSE values with the change of the percentage of outlier and the MAD values that are not affected by the percentage of outliers and sample size. The robust M-estimation method with Tukey Bisquare weighting is as good as the Huber weighting. Full Article

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Kline, Theresa J. B., and Joy D. Klammer. "Path Model Analyzed With Ordinary Least Squares Multiple Regression Versus LISREL." Journal of Psychology 135, no. 2 (March 2001): 213–25. http://dx.doi.org/10.1080/00223980109603692.

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Mahaboob, B., B. Venkateswarlu, C. Narayana, J. Ravi sankar, and P. Balasiddamuni. "A Treatise on Ordinary Least Squares Estimation of Parameters of Linear Model." International Journal of Engineering & Technology 7, no. 4.10 (October 2, 2018): 518. http://dx.doi.org/10.14419/ijet.v7i4.10.21216.

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This research article primarily focuses on the estimation of parameters of a linear regression model by the method of ordinary least squares and depicts Gauss-Mark off theorem for linear estimation which is useful to find the BLUE of a linear parametric function of the classical linear regression model. A proof of generalized Gauss-Mark off theorem for linear estimation has been presented in this memoir. Ordinary Least Squares (OLS) regression is one of the major techniques applied to analyse data and forms the basics of many other techniques, e.g. ANOVA and generalized linear models [1]. The use of this method can be extended with the use of dummy variable coding to include grouped explanatory variables [2] and data transformation models [3]. OLS regression is particularly powerful as it relatively easy to check the model assumption such as linearity, constant, variance and the effect of outliers using simple graphical methods [4]. J.T. Kilmer et.al [5] applied OLS method to evolutionary and studies of algometry.

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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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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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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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Yasin, Seyab, Sultan Salem, Hamdi Ayed, Shahid Kamal, Muhammad Suhail, and Yousaf Ali Khan. "Modified Robust Ridge M-Estimators in Two-Parameter Ridge Regression Model." Mathematical Problems in Engineering 2021 (September 22, 2021): 1–24. http://dx.doi.org/10.1155/2021/1845914.

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The methods of two-parameter ridge and ordinary ridge regression are very sensitive to the presence of the joint problem of multicollinearity and outliers in the y-direction. To overcome this problem, modified robust ridge M-estimators are proposed. The new estimators are then compared with the existing ones by means of extensive Monte Carlo simulations. According to mean squared error (MSE) criterion, the new estimators outperform the least square estimator, ridge regression estimator, and two-parameter ridge estimator in many considered scenarios. Two numerical examples are also presented to illustrate the simulation results.

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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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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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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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Srinadi, I. Gusti Ayu Made. "Model Partial Least Square Regression (PLSR) Pengaruh Bidang Pendidikan dan Ekonomi Terhadap Tingkat Kemiskinan di Indonesia." Jurnal Matematika 7, no. 1 (June 30, 2017): 67. http://dx.doi.org/10.24843/jmat.2017.v07.i01.p83.

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Partial Least Square Regression (PLSR) is one of the methods applied in the estimation of multiple linear regression models when the ordinary least square method (OLS) can not be used. OLS generates an invalid model estimate when multicollinearity occurs or when the number of independent variables is greater than the number of data observations. In conditions that OLS can be applied in obtaining model estimation, want to know the performance of PLSR method. This study aims to determine the model of PLSR the influence of literacy rate, the average of school duration, school enrollment rate, Income per capita, and open unemployment rate to the level of poverty seen from the percentage of poor people in Indonesia by 2015. Estimated model with OLS , Only variable of literacy rate are included in the model with the coefficient of determination R2 = 32.52%. PLSR model estimation of cross-validation, leave-one-out method with one selected component has R2 of 33,23%. Both models shows a negative relationship between poverty and literacy rate. The higher literacy rate will reduce the poverty level, indicating that the success of the Indonesian government in the development of education will support the government's success in reducing poverty level.

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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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Budiwinarto, Kim, Cicilia Puji Rahayu, and Juni Trisnowati. "Analisis Prediksi Kemungkinan Pergantian Auditor Pada Perusahaan Properti Dengan Menggunakan Linear Probability Model." Jurnal Derivat: Jurnal Matematika dan Pendidikan Matematika 7, no. 1 (July 20, 2020): 31–38. http://dx.doi.org/10.31316/j.derivat.v7i1.871.

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This study aims to analyze the prediction of the possibility of an auditor switching in property companies listed on the Indonesia Stock Exchange in 2017 based on the Public Accountant Firm’s size and management switching using a linear probability model. The data used are secondary data obtained from the financial statements of property companies. Property companies that meet the requirements in the study as a sample of 33 companies. To estimate the linear probability model using the ordinary least square method. The model that has been obtained was tested by t-test and F test. The results of the data analysis can be concluded that the model obtained can be used to predict the possibility of a property company auditor switching based on the predictor variable of Public Accountant Firm’s size and management switching with a predictive reliability level of 84.84%. Keywords: linear probability model, auditor switching, Public Accountant Firm’s size, management switching, ordinary least square method.

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Sebayang, Jimmy Saputra, and Budi Yuniarto. "Perbandingan Model Estimasi Artificial Neural Network Optimasi Genetic Algorithm dan Regresi Linier Berganda." MEDIA STATISTIKA 10, no. 1 (August 14, 2017): 13. http://dx.doi.org/10.14710/medstat.10.1.13-23.

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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 topology, learning parameters and weight initialization. Genetic Algorithm method can be used to solve those problems. A set of simulation data was generated to test the reliability of ANN-GA model compared to Multiple Linear Regression model. Model comparison experiments indicate that ANN-GA model are better than Multiple Linear Regression model for estimating simulation data both on the data training and data testing.Keywords:Neural Network, Genetic Algorithm, Ordinary Least Square

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Suganthi, L., and T. R. Jagadeesan. "A Modified Model for Prediction of India's Future Energy Requirement." Energy & Environment 3, no. 4 (June 1992): 371–86. http://dx.doi.org/10.1177/0958305x9200300403.

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A modified model has been projected in this paper which links environmental quality and technological efficiency with energy and economic factors. A comparison is made between the results obtained from the modified model with a time series and an econometric model using Ordinary Least Square Error (OLSE), square of the correlation coefficient R2 and Durbin Watson statistic. The time series model is built using seven regression equations and the best fit is selected from among them. The econometric model is built with consumption, price and gross national product. In the modified model, two more variables, technological efficiency and carbon dioxide emission are included, which help to determine the impact of these variables on energy consumption. It is found that the modified model gives least squared error and higher correlation coefficient for coal, oil and electricity. Also the Durbin Watson statistic ‘DW’ is found to be higher in two out of the three cases - coal and oil. The requirement of coal, oil and electricity in the year 1995–96 and 2000–01 is determined using the data for the period 70–89.

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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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Ahrens, Achim, Christian B. Hansen, and Mark E. Schaffer. "lassopack: Model selection and prediction with regularized regression in Stata." Stata Journal: Promoting communications on statistics and Stata 20, no. 1 (March 2020): 176–235. http://dx.doi.org/10.1177/1536867x20909697.

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In this article, we introduce lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso, square-root lasso, elastic net, ridge regression, adaptive lasso, and postestimation ordinary least squares. The methods are suitable for the high-dimensional setting, where the number of predictors p may be large and possibly greater than the number of observations, n. We offer three approaches for selecting the penalization (“tuning”) parameters: information criteria (implemented in lasso2), K-fold cross-validation and h-step-ahead rolling cross-validation for cross-section, panel, and time-series data (cvlasso), and theory-driven (“rigorous” or plugin) penalization for the lasso and square-root lasso for cross-section and panel data (rlasso). We discuss the theoretical framework and practical considerations for each approach. We also present Monte Carlo results to compare the performances of the penalization approaches.

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Wu, Jibo. "Comparison of Some Estimators under the Pitman’s Closeness Criterion in Linear Regression Model." Journal of Applied Mathematics 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/654949.

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Batah et al. (2009) combined the unbiased ridge estimator and principal components regression estimator and introduced the modifiedr-kclass estimator. They also showed that the modifiedr-kclass estimator is superior to the ordinary least squares estimator and principal components regression estimator in the mean squared error matrix. In this paper, firstly, we will give a new method to obtain the modifiedr-kclass estimator; secondly, we will discuss its properties in some detail, comparing the modifiedr-kclass estimator to the ordinary least squares estimator and principal components regression estimator under the Pitman closeness criterion. A numerical example and a simulation study are given to illustrate our findings.

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Abdullahi, Ibrahim, and Abubakar Yahaya. "Analysis of quantile regression as alternative to ordinary least squares." International Journal of Advanced Statistics and Probability 3, no. 2 (June 15, 2015): 138. http://dx.doi.org/10.14419/ijasp.v3i2.4686.

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<p>In this article, an alternative to ordinary least squares (OLS) regression based on analytical solution in the Statgraphics software is considered, and this alternative is no other than quantile regression (QR) model. We also present goodness of fit statistic as well as approximate distributions of the associated test statistics for the parameters. Furthermore, we suggest a goodness of fit statistic called the least absolute deviation (LAD) coefficient of determination. The procedure is well presented, illustrated and validated by a numerical example based on publicly available dataset on fuel consumption in miles per gallon in highway driving.</p>

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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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Efendi, Riswan, Adhe N. Imandari, Yusnita Rahmadhani, Suhartono, Noor A. Samsudin, Nureize Arbai, and Mustafa M. Deris. "Fuzzy Autoregressive Time Series Model Based on Symmetry Triangular Fuzzy Numbers." New Mathematics and Natural Computation 17, no. 02 (April 7, 2021): 387–401. http://dx.doi.org/10.1142/s1793005721500204.

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The symmetry triangular fuzzy number has been developed to build fuzzy autoregressive models by using various approaches such as low-high data, integer number, measurement error, and standard deviation data. However, most of these approaches are not simulated and compared between ordinary least square and fuzzy optimization in parameter estimation. In this paper, we are interested in implementation of measurement error and standard deviation data in construction symmetry triangular fuzzy numbers. Additionally, both types of triangular fuzzy numbers are deployed to build a fuzzy autoregressive model, especially the second order. The simulation result showed that the fuzzy autoregressive model produced the smaller mean square error and average width if compared with the ordinary autoregressive model. In the implementation, the high accuracy was also achieved by the fuzzy autoregressive model in consumer goods stock prediction. From the simulation and implementation, the proposed fuzzy autoregressive model is a competent approach for upper and lower forecasts.

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Sari, Yolanda, and Nurlia Fusfita. "PERAMALAN PENERIMAAN BEA CUKAI INDONESIA." EKONOMIS : Journal of Economics and Business 2, no. 1 (March 23, 2018): 137. http://dx.doi.org/10.33087/ekonomis.v2i1.38.

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The revenue of customs and excise is very important in APBN. By making accurate estimation, target of revenue can be better determined. In addition, the revenue of customs and excise is also influenced by many external factors that are difficult to predict therefore a rational approach is needed to estimate revenue. This research uses Double Exponential Smoothing, Ordinary Least Square (OLS) model and Moving Average in predicting customs and excise revenue. Data used in this research is secondary data in time coherent pattern. The data includes import duty, export duty and excise obtained from the Directorate General of Customs and excise (DJBC) in the form of annual and quarterly data. This data starts from 2002 to 2016 with out of sample from 2017 to 2019. Some of these models are compared to each other to obtain the best model, and from the best model is also obtained estimating results in 3 years ahead. This study shows that the Double Exponential Smoothing model is better for predicting import duties compared to OLS and Moving Average models, which are models that have the smallest Sum Square Error (SSE) value. While the export and excise duty is best estimated by using OLS model which is shown with coefficient of determination value (R2) regression model of export duty is 0.8, while the excise regression model has coefficient of determination of 0.9.Keywords: Customs Estimation, Double Exponential Smoothing, Ordinary Least Square, Moving Average

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Wallace, Michael P., Erica EM Moodie, and David A. Stephens. "Model validation and selection for personalized medicine using dynamic-weighted ordinary least squares." Statistical Methods in Medical Research 26, no. 4 (May 10, 2017): 1641–53. http://dx.doi.org/10.1177/0962280217708665.

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Model assessment is a standard component of statistical analysis, but it has received relatively little attention within the dynamic treatment regime literature. In this paper, we focus on the dynamic-weighted ordinary least squares approach to optimal dynamic treatment regime estimation, introducing how its double-robustness property may be leveraged for model assessment, and how quasilikelihood may be used for model selection. These ideas are demonstrated through simulation studies, as well as through application to data from the sequenced treatment alternatives to relieve depression study.

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Zhang, Xinyu, Aman Ullah, and Shangwei Zhao. "On the dominance of Mallows model averaging estimator over ordinary least squares estimator." Economics Letters 142 (May 2016): 69–73. http://dx.doi.org/10.1016/j.econlet.2016.02.027.

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Iswati, Helmi, Rahmat Syahni, and Maiyastri . "PERBANDINGAN PENDUGA ORDINARY LEAST SQUARES (OLS) DAN GENERALIZED LEAST SQUARES (GLS) PADA MODEL REGRESI LINIER DENGAN REGRESOR BERSIFAT STOKASTIK DAN GALAT MODEL BERAUTOKORELASI." Jurnal Matematika UNAND 3, no. 4 (December 1, 2014): 168. http://dx.doi.org/10.25077/jmu.3.4.168-176.2014.

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Pendugaan parameter model regresi linier pada analisis regresi linier, biasanyadilakukan dengan metode penduga OLS. Penduga OLS harus memenuhi asumsi-asumsistatistik yang disebut dengan asumsi klasik. Jika asumsi tidak dipenuhi, maka akanmenghasilkan kesimpulan yang tidak valid sehingga penduga OLS tidak bisa digunakanlagi dalam melakukan pendugaan parameter. Oleh karena itu diperlukan metode pendugaan lain untuk memperoleh hasil yang valid yaitu penduga GLS. Pelanggaran asumsidiantaranya terdapat autokorelasi pada galat model dan regresor bersifat stokastik.Adanya autokorelasi dengan regresor bersifat stokastik dilihat melalui simulasi MonteCarlo dengan ukuran sampel, koefisien autokorelasi dan ulangan yang bervariasi. Selainitu, pendugaan parameter juga dievaluasi melalui beberapa kriteria yaitu nilai AbsolutBias, Varian dan MSE. Hasil simulasi menunjukkan bahwa semakin bertambahnya ukuran sampel mengakibatkan kriteria penduga parameter semakin kecil. Sementara itu,ulangan yang tinggi yang dilakukan pada simulasi ini tidak mempengaruhi kriteria penduga parameter. Pada pendugaan parameter model untuk semua penduga, penduga GLSlebih efisien dan stabil dibanding dengan penduga OLS kecuali untuk koefisen autokorelasi −0.5 ≤ ρ ≤ −0.25 dan ρ = 0.5 pada βb1 , dan ρ = −0.25 pada βb2.

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Handoko, Rudi. "MODEL PROYEKSI EKSPOR DAN IMPOR - VOLUME DAN HARGA." Kajian Ekonomi dan Keuangan 14, no. 3 (November 9, 2015): 61–81. http://dx.doi.org/10.31685/kek.v14i3.62.

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Kinerja ekspor dan impor Indonesia selama periode 2000 - 2009 cenderung mengalami peningkatan walaupun sempat mengalami penurunan saat terjadi krisis ekonomi global 2008/2009. Variabel ekonomi yang mempengaruhi ekspor dan impor diidentifikasi seperti permintaan dunia, volume perdagangan dunia, harga ekspor, dan nilai tukar. Model proyeksi difokuskan kepada pertumbuhan (growth) volume dan harga baik ekspor maupun impor. Model ekonometrik yang dikembangkan menggunakan metode ordinary least square (OLS) dengan meregresikan variabel-variabel yang mempengaruhi volume dan harga—ekspor dan impor.

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Shina, Arya Fendha Ibnu. "ESTIMASI PARAMETER PADA SISTEM MODEL PERSAMAAN SIMULTAN DATA PANEL DINAMIS DENGAN METODE 2 SLS GMM-AB." MEDIA STATISTIKA 11, no. 2 (December 30, 2018): 79–91. http://dx.doi.org/10.14710/medstat.11.2.79-91.

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Single equation models ignore interdependencies or two-way relationships between response variables. The simultaneous equation model accommodates this two-way relationship form. Two Stage Least Square Generalized Methods of Moment Arellano and Bond (2 SLS GMM-AB) is used to estimate the parameters in the simultaneous system model of dynamic panel data if each structural equation is exactly identified or over identified. In the simultaneous equation system model with dynamic panel data, each structural equation and reduced form is a dynamic panel data regression equation. Estimation of structural equations and reduced form using Ordinary Least Square (OLS) resulted biased and inconsistent estimators. Arellano and Bond GMM method (GMM AB) estimator produces unbiased, consistent, and efficient estimators.The purpose of this paper is to explain the steps of 2 SLS GMM-AB method to estimate parameter in simultaneous equation model with dynamic panel data. Keywords:2 SLS GMM-AB, Arellano and Bond estimator, Dynamic Panel Data, Simultaneous Equations

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Huang, Zhiyong, Ziyan Luo, and Naihua Xiu. "High-Dimensional Least-Squares with Perfect Positive Correlation." Asia-Pacific Journal of Operational Research 36, no. 04 (August 2019): 1950016. http://dx.doi.org/10.1142/s0217595919500167.

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The least-squares is a common and important method in linear regression. However, it often leads to overfitting phenomenon as dealing with high-dimensional problems, and various regularization schemes regarding prior information for specific problems are studied to make up such a deficiency. In the sense of Kendall’s [Formula: see text] from the community of nonparametric analysis, we establish a new model wherein the ordinary least-squares is equipped with perfect positive correlation constraint, sought to maintain the concordance of the rankings of the observations and the systematic components. By sorting the observations into an ascending order, we reduce the perfect positive correlation constraint into a linear inequality system. The resulting linearly constrained least-squares problem together with its dual problem is shown to be solvable. In particular, we introduce a mild assumption on the observations and the measurement matrix which rules out the zero vector from the optimal solution set. This indicates that our proposed model is statistically meaningful. To handle large-scale instances, we propose an efficient alternating direction method of multipliers (ADMM) to solve the proposed model from the dual perspective. The effectiveness of our model compared to ordinary least-squares is evaluated in terms of rank correlation coefficient between outputs and the systematic components, and the efficiency of our dual algorithm is demonstrated with the comparison to three efficient solvers via CVX in terms of computation time, solution accuracy and rank correlation coefficient.

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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 6, 2018): 52. http://dx.doi.org/10.20956/jmsk.v15i2.5569.

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Perancangan 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.

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Lukman, Adewale F., Kayode Ayinde, Sek Siok Kun, and Emmanuel T. Adewuyi. "A Modified New Two-Parameter Estimator in a Linear Regression Model." Modelling and Simulation in Engineering 2019 (May 26, 2019): 1–10. http://dx.doi.org/10.1155/2019/6342702.

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The literature has shown that ordinary least squares estimator (OLSE) is not best when the explanatory variables are related, that is, when multicollinearity is present. This estimator becomes unstable and gives a misleading conclusion. In this study, a modified new two-parameter estimator based on prior information for the vector of parameters is proposed to circumvent the problem of multicollinearity. This new estimator includes the special cases of the ordinary least squares estimator (OLSE), the ridge estimator (RRE), the Liu estimator (LE), the modified ridge estimator (MRE), and the modified Liu estimator (MLE). Furthermore, the superiority of the new estimator over OLSE, RRE, LE, MRE, MLE, and the two-parameter estimator proposed by Ozkale and Kaciranlar (2007) was obtained by using the mean squared error matrix criterion. In conclusion, a numerical example and a simulation study were conducted to illustrate the theoretical results.

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Prastuti, Mike, and Iis Dewi Ratih. "KAJIAN SIMULASI ESTIMASI PARAMETER MODEL GSTAR-GLS UNTUK DATA BERPOLA MUSIMAN." MEDIA BINA ILMIAH 13, no. 12 (July 23, 2019): 1769. http://dx.doi.org/10.33758/mbi.v13i12.261.

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Secara umum, metode untuk mengestimasi parameter dalam model GSTAR adalah Ordinary Least Squares (OLS). Estimasi parameter dengan menggunakan OLS untuk model GSTAR dengan residual yang berkorelasi akan menghasilkan estimator yang tidak efisien. Metode yang sesuai untuk mengestimasi parameter dengan residual yang berkorelasi adalah Generalized Least Square (GLS). Tujuan dari makalah ini adalah untuk mengusulkan metode GLS untuk mengestimasi parameter dalam model GSTAR Musiman, yang dikenal sebagai GSTAR-GLS, dan membandingkan hasilnya dengan metode OLS atau GSTAR-OLS. Selain itu, tujuan dari makalah ini adalah untuk mengekaji lebih lanjut dalam penentuan bobot spasial yang sesuai pada model GSTAR. Hasil studi simulasi menunjukkan bahwa penentuan bobot spasial pada model GSTAR dapat dilakukan secara optimal dengan menggunakan normalisasi hasil inferensi statistik terhadap parsial korelasi silang antar lokasi pada lag waktu yang bersesuaian. Selain itu, GSTAR-GLS menghasilkan estimator yang lebih efisien daripada GSTAR-OLS, dimana standard error yang dihasilkan lebih kecil

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Halim, Putri Riswani, Fauziah Agustina Sa'ban, and Farah Muthia Syifa. "RELEVANSI MANAJERIAL PENDIDIKAN “INDONESIA MENGAJAR” DENGAN KUALITAS PENDIDIKAN DAERAH 3T: STUDI KASUS ROTE NDAO." Edunomic Jurnal Pendidikan Ekonomi 6, no. 2 (September 22, 2018): 72. http://dx.doi.org/10.33603/ejpe.v6i2.1470.

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Rote Ndao sebagai sebuah kabupaten paling selatan di Indonesia memiliki berbagai keterbatasan dalam mewujudkan pendidikan yang berkualitas. Indonesia Mengajar hadir sebagai gerakan untuk memajukan pendidikan di Rote Ndao. Penelitian ini bertujuan untuk melihat relevansi manajerial pendidikan yang dilakukan Indonesia Mengajar dengan kualitas pendidikan di Rote Ndao. Analisis penelitian ini menggunakan metode campuran atau mix-method anatara kuantitatif dan kualitatif. Metode kuantitatif menggunakan pendekatan ekonometrika dengan model regresi Ordinary Least Square (OLS). Model Ordinary Least Square (OLS) didukung dengan metode kualitatif dengan pendekatan wawancara mendalam atau In-Depth Interview. Hasil penelitian menunjukkan bahwa terdapat relevansi positif antara Indonesia Mengajar dengan kualitas pendidikan daerah 3T khususnya Rote Ndao. Indonesia Mengajar memiliki manajerial pendidikan yang baik dan dapat meningkatkan kualitas pendidikan di Rote Ndao sehingga program serupa dapat diterapkan di daerah 3T lainnya agar ketimpangan pendidikan di Indonesia dapat diatasi.Kata kunci: Rote Ndao, Pendidikan, Indonesia Mengajar, Metode Campuran

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Abdelhadi, Yaser. "Linear modeling and regression for exponential engineering functions by a generalized ordinary least squares method." International Journal of Engineering & Technology 3, no. 2 (April 20, 2014): 174. http://dx.doi.org/10.14419/ijet.v3i2.2023.

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Linear transformations are performed for selected exponential engineering functions. The Optimum values of parameters of the linear model equation that fits the set of experimental or simulated data points are determined by the linear least squares method. The classical and matrix forms of ordinary least squares are illustrated. Keywords: Exponential Functions; Linear Modeling; Ordinary Least Squares; Parametric Estimation; Regression Steps.

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Ahmed Issa, Mohamed Khalifa. "New Estimator for AR (1) Model with Missing Observations." Journal of University of Shanghai for Science and Technology 23, no. 09 (September 6, 2021): 147–59. http://dx.doi.org/10.51201/jusst/21/09521.

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In this paper, new form of the parameters of AR(1) with constant term with missing observations has been derived by using Ordinary Least Squares (OLS) method, Also, the properties of OLS estimator are discussed, moreover, an extension of Youssef [18]has been suggested for AR(1) with constant with missing observations. A comparative study between (OLS), Yule-Walker (YW) and modification of the ordinary least squares (MOLS) is considered in the case of stationary and near unit root time series, using Monte Carlo simulation.

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Danna Solihin, Faizal Reza, Adisthy Shabrina Nurqamarani,. "IS ARBITRAGE PRICING THEORY IS A FAIRY TALE ?: THE EVIDENCE FROM INDONESIA WITH ORDINARY LEAST SQUARE ESTIMATION." Research Journal of Accounting and Business Management 2, no. 1 (June 26, 2018): 18. http://dx.doi.org/10.31293/rjabm.v2i1.3475.

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Time series analysis in financial sector has grown in popularity since the last few decades, theories about capital markets are increasingly studied since Capital Asset Pricing Model (CAPM) and Arbitrage Pricing Theory (APT) are often compared and paired with one another. The development of APT is widespread by involving many macroeconomic variables. Unfortunately there is no consensus until now. This paper aims to examine the relevance of APT in the case of Indonesia during the period 2009-2017 with standard multiple regression equations. From three independent variables, only exchange rates have a significant influence on the composite stock price (IHSG) in Indonesia, while inflation and GDP have no significant effect on stock prices. 99.8% variation in stock price formation can be explained by independent variables where there is a positive relationship between exchange rate and stock price indicates a mechanism of stock price formation through strong domestic demand before fourth quarter 2016 and trade effect (export-import) after fourth quarter 2016 .

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Mensi, Sami. "Measurement of competitiveness degree in Tunisian deposit banks: An application of the Panzar and Rosse model." Panoeconomicus 57, no. 2 (2010): 189–207. http://dx.doi.org/10.2298/pan1002189m.

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This paper explores the use of the Panzar-Rosse statistic as a basis for empirical assessment of competitive conditions among Tunisian deposit banks. The elaborated model has been tested with an interest revenues equation and a total revenues equation. Proceeding by means of an Ordinary Least Square analysis, the H-statistics is respectively estimated at 0.87 and 0.91. The computations undertaken using bank fixed effects and bank random effects General Least Square methods yield similar results. With reference to the reviewed literature, we are inclined to believe that Tunisian banks implement neither a joint monopoly nor a collusive competition context, and that they evolve within an oligopolistic competition context in a contestable market. Thus, it confirms the presence of a competitive environment.

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Haupt, Harry, Friedrich Lösel, and Mark Stemmler. "Quantile Regression Analysis and Other Alternatives to Ordinary Least Squares Regression." Methodology 10, no. 3 (January 1, 2014): 81–91. http://dx.doi.org/10.1027/1614-2241/a000077.

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Data analyses by classical ordinary least squares (OLS) regression techniques often employ unrealistic assumptions, fail to recognize the source and nature of heterogeneity, and are vulnerable to extreme observations. Therefore, this article compares robust and non-robust M-estimator regressions in a statistical demonstration study. Data from the Erlangen-Nuremberg Development and Prevention Project are used to model risk factors for physical punishment by fathers of 485 elementary school children. The Corporal Punishment Scale of the Alabama Parenting Questionnaire was the dependent variable. Fathers’ aggressiveness, dysfunctional parent-child relations, various other parenting characteristics, and socio-demographic variables served as predictors. Robustness diagnostics suggested the use of trimming procedures and outlier diagnostics suggested the use of robust estimators as an alternative to OLS. However, a quantile regression analysis provided more detailed insights beyond the measures of central tendency and detected sources of considerable heterogeneity in the risk structure of father’s corporal punishment. Advantages of this method are discussed with regard to methodological and content issues.

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Funmilayo Gatta, Nusirat, and Banjoko Alabi Waheed. "Investigating the Effects of Multicollinearity on the Model Parameters of Ordinary Least Squares Estimator." International Journal of Advances in Scientific Research and Engineering 5, no. 5 (2019): 116–21. http://dx.doi.org/10.31695/ijasre.2019.33217.

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Journal articles on the topic 'Ordinary least square model'

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