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Journal articles on the topic 'Eight ordinary least squares (OLS) regression model'

Author: Grafiati

Published: 19 February 2023

Last updated: 31 July 2025

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1

Weya, Ince, Sirojuzilam Sirojuzilam, Muhammad Syafi’i, and Dede Ruslan. "Analysis of the Effect of HDI and Road Length Infrastructure Development on Improving Economic Inequality in Eight Districts of the Region La Pago Tradition." Journal of International Conference Proceedings 6, no. 5 (2023): 137–50. http://dx.doi.org/10.32535/jicp.v6i5.2655.

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The objective of this research is to examine the impact of human development index and road length infrastructure development on the reduction of economic inequality, as shown by general publication, throughout the period from 2013 to 2022. The inclusion of secondary data is essential in order to provide a comprehensive explanation or response to the research inquiry. The present work used a panel data model with a linear regression methodology, namely the ordinary least squares (OLS) method, for data analysis. The findings indicate that there is a substantial positive relationship between the

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Islam, Md Aminul, Tanzina Ahmed Rickty, Pramit Kumar Das, and Md Bashirul Haque. "Modeling and Forecasting Urban Sprawl in Sylhet Sadar Using Remote Sensing Data." Proceedings of Engineering and Technology Innovation 23 (January 1, 2023): 23–35. http://dx.doi.org/10.46604/peti.2023.9617.

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Forecasting urban sprawl is important for land-use and transport planning. The aim of this study is to model and predict the future urban sprawl in Sylhet Sadar using remote sensing data. The ordinary least square (OLS) regression model and the geographic information system (GIS) are used for modeling urban expansion. The model is calibrated for the years 2014 to 2017 using eight explanatory variables extracted from the regression model. The regression coefficients of the variables are found statistically significant at a 99% confidence level. The cellular automata (CA) model is then used to a

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Thein, Ei Ei, Atsushi Niigata, and Kazuo Inaba. "Information disclosure and SME financing: A study of firms in the ASEAN region." Journal of Accounting, Business and Finance Research 17, no. 2 (2023): 64–77. http://dx.doi.org/10.55217/102.v17i2.720.

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This study investigates the impact of information and communication technology (ICT) and audited financial statements on small and medium enterprise (SME) financing, as well as their influence on SMEs’ collateral issues in acquiring bank loans, based on the information asymmetry theory. The study applies the ordinary least squares (OLS) test, the two-stage least squares test, and the probit regression model for the analysis. The sample consists of 12,165 firms in eight ASEAN countries between 2009 and 2018. The data used in the analysis was sourced from the Business Environment and Enterprise

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Zhao, Peizhi, and Yuyan Wang. "How Does Economic Policy Uncertainty Affect Momentum Returns? Evidence from China." International Journal of Financial Studies 10, no. 3 (2022): 59. http://dx.doi.org/10.3390/ijfs10030059.

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Economic policy uncertainty has been identified as a new macroeconomic risk factor that harms the stock market’s profitability. This paper examines the impact of the Chinese EPU levels on one of the most famous financial anomalies—momentum returns. A new EPU index based on mainland China newspapers is used to obtain more accurate EPU–momentum relations. We selected 3958 Chinese listed companies’ stocks from 2011 to 2022 to establish time-series (TSM) and returns signal momentum strategies (RSM). Although the momentum effect in the Chinese stock market is weak, the EPU-based dynamic-threshold R

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Yang, Zhiheng, Chenxi Li, and Yongheng Fang. "Driving Factors of the Industrial Land Transfer Price Based on a Geographically Weighted Regression Model: Evidence from a Rural Land System Reform Pilot in China." Land 9, no. 1 (2020): 7. http://dx.doi.org/10.3390/land9010007.

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More and more studies on land transfer prices have been carried out over time. However, the influencing factors of the industrial land transfer price from the perspective of spatial attributes have rarely been explored. Selecting 25 towns as the basic research unit, based on industrial land transfer data, this paper analyzes the influencing factors of the price distribution of industrial land in Dingzhou City, a rural land system reform pilot in China, by using a geographically weighted regression (GWR) model. Eight evaluation factors were selected from five aspects: economy, population, topog

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Chen, Feng, Shenlong Lou, Qiancong Fan, et al. "Normalized Difference Vegetation Index Continuity of the Landsat 4-5 MSS and TM: Investigations Based on Simulation." Remote Sensing 11, no. 14 (2019): 1681. http://dx.doi.org/10.3390/rs11141681.

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Landsat 4-5, built at the same time and with the same design, carrying the Multispectral Scanner System (MSS) and the Thematic Mapper (TM) simultaneously, jointly provided observation service for about 30 years (1982–2013). Considering the importance of data continuity for time series analyses, investigations on the continuity of the Landsat 4-5 MSS and TM are required. In this paper, characterization differences between the Landsat 4-5 MSS and TM were initially discussed using the synthesized reflectance records generated from a collection of Hyperion hyperspectral profiles which were well ca

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Akande, Joseph Olorunfemi. "THE IMPACT OF ESG PRACTICES ON THE RISK PORTFOLIO OF LISTED OIL AND GAS FIRMS IN NIGERIA USING A MULTILAYERED CRITERION." Gusau Journal of Accounting and Finance 5, no. 2 (2025): 143–55. https://doi.org/10.57233/gujaf.v5i2.09.

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This study investigates the impact of Environmental, Social, and Governance (ESG) factors on the risk-adjusted returns of Nigerian oil and gas firms listed on the Nigerian Exchange Group (NGX) over a 11-year period (2012–2022). The study was anchored on signalling theory. Utilizing a correlational research design, data was collected from eight firms meeting inclusion criteria, focusing on ESG scores as independent variables, with Firm Size as a control variable, and risk-adjusted returns as the dependent variable. Diagnostic tests ensured adherence to Best Linear Unbiased Estimator (BLUE) assu

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Wali Ullah, G. M., Mohammad Nasrath Faisal, and Sadaqa Tuz Zuhra. "Factors Determining Profitability of the Insurance Industry of Bangladesh." International Finance and Banking 3, no. 2 (2016): 138. http://dx.doi.org/10.5296/ifb.v3i2.9954.

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Insurance is a form of risk management, used to hedge against the risk of a contingent loss. It involves the transfer of the risk of potential loss from one entity to another, in exchange for a risk premium. Insurance sector plays an important role in service based economy of both developed and developing markets. The purpose of this research is to analyze the determinants that serve as significant predictors of non-life insurance firms’ profitability in Bangladesh. It analyzes panel data of eight different insurance companies—selected using convenience sampling method from the years 2004-2014

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Basri, Mohd Faizal, Fitri Shuhaida Shoib, and Surianor Kamaralzaman. "Determinants of Capital Structure: Evidence From Malaysian Food and Beverage Firms." Research in World Economy 10, no. 5 (2019): 45. http://dx.doi.org/10.5430/rwe.v10n5p45.

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This paper investigates the firm-specific elements, which are profitability, growth, tangible assets and liquidity in determining the capital structure of Food and Beverage (F&B) firms in Malaysia. The research employed panel data regression model based on ordinary least square (OLS) method. The sample of research consists of eight firms listed in the food producer segment in Bursa Malaysia for the period between 2013 and 2018, with a total observation of 48 firms-years. Debt to equity was chosen as dependent variable. On the other hand, profitability, asset growth, tangibility of assets,

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CRUZ, Lucas Xavier Pereira, Sérgio Ricardo Miranda NAZARÉ, Danielle Montenegro Salamone NUNES, and Mariana Porto FERNANDES. "INCOME DIVERSIFICATION AND ITS EFFECTS ON PROFITABILITY AND RISK: A STUDY OF BRAZILIAN BANKS." Boletim de Conjuntura (BOCA) 21, no. 62 (2025): 133–53. https://doi.org/10.5281/zenodo.14908433.

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Banks generally have two main types of income: interest-related income, or simply interest income, and non-interest-related income, or non-interest income. This study presents an analysis of the impact that income diversification has on profitability (measured by ROE and ROA), insolvency risk (measured by ZScore), and risk-adjusted return (measured by the ratio of ROE and ROA to their respective standard deviations) of banks. The analysis considers diversification between income groups (interest and non-interest) and diversification within each group, where different types of interest and non-

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Komara Rifai, Nur Azizah, and Mufdhil Afta Zhahirulhaq. "FORECASTING INFLATION IN INDONESIA USING THE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE METHOD." Parameter: Journal of Statistics 4, no. 1 (2024): 37–45. http://dx.doi.org/10.22487/27765660.2024.v4.i1.17130.

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Indonesia faces significant economic challenges, particularly inflation, which affects the economic, social, and cultural sectors. High inflation can exacerbate poverty, alter consumption patterns, and contribute to social injustice, whereas low inflation can enhance national income and stimulate economic activities. Given its fluctuating nature, inflation in Indonesia requires accurate forecasting to inform policy-making and economic decisions. This study aims to forecast inflation in Indonesia for the next eight months using the Autoregressive Integrated Moving Average (ARIMA) method. Monthl

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Attah, Omokova M., and Samuel Olayemi Olanrewaju. "Efficient Combined Estimator for Parameter Estimation of Linear Regression Model with Multicollinearity." Asian Journal of Probability and Statistics 27, no. 5 (2025): 1–11. https://doi.org/10.9734/ajpas/2025/v27i5750.

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The classical linear regression model relies on several key assumptions, including homoscedasticity, normality of errors, independence of observations and the absence of multicollinearity among explanatory variables (Gujarati, 2021), These assumptions are rarely fulfilled in real life situations. Multicollinearity occurs when the assumption of independent explanatory variables is violated (Alreshidi et al., 2025), There are many sources of multicollinearity, some of which are the data collection methods, the constraints placed on the model or having an overdetermined model (Paul, 2006), When m

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Purnamasari, Fitry, Lestari Rahayu Waluyati, and Masyhuri Masyhuri. "The Effect of Good Agriculture Practices (GAP) on Soybean Productivity with Cobb-Douglas Production Function Analysis in Kulon Progo Regency." Agro Ekonomi 28, no. 2 (2017): 220. http://dx.doi.org/10.22146/jae.26823.

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This study aims to determine the level of adoption of Good Agriculture Practices (GAP) and the influence of GAP and other factors of production on soybean productivity. The number of respondents in this research is 50 farmers taken randomly. This research used proportional parameter test and multiple linear regression analysis with Ordinary Least Square (OLS) method. This research has been declared valid, reliability, data have been the normal distribution, free from multicollinearity and heteroscedasticity problem. The result of the analysis shows that (1) the adoption rate of GAP of soybean

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Chowdhury, Muntaqim, Imroz Mahmud, and Md Mehrabul Hoque. "Factors Influencing Agency Costs in the Publicly Listed IT Firms: Evidence from Bangladesh." Business and Finance Journal 8, no. 1 (2023): 1–15. http://dx.doi.org/10.33086/bfj.v8i1.3460.

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The primary goal of this research is to identify the factors that influence agency costs in publicly listed IT firms in Bangladesh. The research is based on secondary data obtained from nine IT firms listed on the Dhaka Stock Exchange (DSE) between 2018 and 2021, providing thirty-five firm-year observations. The effects of eight independent factors: board size, firm size, female directors, independent directors, managerial ownership, foreign ownership, institutional ownership, and leverage are examined in this study. For measuring the agency costs, the Asset Utilization Ratio (AUR) and Expense

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Hossain, Md Solaiman. "Impact of Disclosure Practices on Investment Decision." American Journal of Trade and Policy 9, no. 3 (2022): 103–10. http://dx.doi.org/10.18034/ajtp.v9i3.627.

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This study aims to investigate the impact of different parts of the disclosure index on concerned components of investors’ decisions. For this consideration, this study has constructed an unweighted disclosure index under eight headings: corporate profile items, corporate governance items, employees and social responsibility items, risk management items, indicators of financial performance, income statement items, balance sheet items, and accounting policy items. The individual disclosure of each part of the disclosure index has been calculated. This study also identified some concerning compo

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Adegun, Simeon Olorunwa, Bankole Olu Akinuli, and Deborah Adesoye Ogunlade. "The Effects of Direct Assessment, Pay-As-You-Earn (PAYE) on Internally Generated Revenue in Ondo State." British Journal of Multidisciplinary and Advanced Studies 6, no. 2 (2025): 64–86. https://doi.org/10.37745/bjmas.2022.04275.

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This study analyzes the effect of personal income tax on internally generated revenue in Ondo State. The specific objectives are to examine the individual contributions of the components of personal income tax on internally generated revenue in Ondo State. This study used both primary sources and secondary sources of data. In doing this, two models were utilized. The secondary data focused on secondary data from eight years record from Ondo State Board of Internal Revenue (OSBIR), while the primary data employed survey through the aid of questionnaires distributed across the three senatorial d

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Sutadipraja, Marista Winanti, Sri Setia Ningsih, and Mardiana Mardiana. "Pajak Kini, Pajak Tangguhan, Aset Pajak Tangguhan, Liabilitas Pajak Tangguhan Terhadap Manajemen Laba." Journal of Applied Accounting and Taxation 5, no. 2 (2020): 158–1665. http://dx.doi.org/10.30871/jaat.v5i2.1306.

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The purpose of this study was to examine whether there is an effect of current tax expense, deferred tax, deferred tax assets, and deferred tax liability on earnings management actions in consumer goods companies listed on the Indonesia Stock Exchange (IDX). This study's sample consisted of 27 consumer goods industries listed on the Indonesia Stock Exchange in 2013-2017 using the purposive sampling method. Hypothesis testing in this study using the t-test. Earnings management is proxied by discretionary accruals using the Modified Jones Model. The type of data used is secondary data. Data anal

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Yasmeen, Safia, and G.Manoj Someswar. "Evaluation of Calibration Techniques to Build Software Cost Estimation Models." COMPUSOFT: An International Journal of Advanced Computer Technology 05, no. 08 (2016): 2223–26. https://doi.org/10.5281/zenodo.14800007.

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This research paper describes three calibration techniques, namely ordinary least squares regression, Bayesian analysis, and constrained regression technique, which are applied to calibrating the cost drivers of the model. Ordinary least squares (OLS) regression is the most popular technique used to build software cost estimation models. In COCOMO, the OLS is used for many purposes, such as analyzing the correlation between cost drivers and the effort and generating coefficients and their variances during the Bayesian analysis. 

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Ogunbona, Babafemi D., Folorunsho O. Balogun, and Kayode S. Famuagun. "Solving Multicolinearity Problem in a Linear Regression: A Comparative Study of Ordinary Least Squares and Partial Least Squares Regression." Journal of Institutional Research, Big Data Analytics and Innovation 1, no. 1 (2024): 66–75. https://doi.org/10.5281/zenodo.15556948.

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Ordinary Least Squares (OLS) estimator usually yields inefficient estimates when multicollinearity is present in a Linear Regression Model. The inefficiency of OLS can be mitigated by Partial Least Squares Regression (PLSR). However, this method requires selecting latent variables in order to yield efficient estimates of regression parameters. This paper proposes using weighted standard errors and ranking standard errors of regression coefficients for latent variable extraction, alongside model selection methods such as Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC),

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Mahaboob, B., B. Venkateswarlu, C. Narayana, J. Ravi sankar, and P. Balasiddamuni. "A Monograph on Nonlinear Regression Models." International Journal of Engineering & Technology 7, no. 4.10 (2018): 543. http://dx.doi.org/10.14419/ijet.v7i4.10.21277.

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This research article uses Matrix Calculus techniques to study least squares application of nonlinear regression model, sampling distributions of nonlinear least squares estimators of regression parametric vector and error variance and testing of general nonlinear hypothesis on parameters of nonlinear regression model. Arthipova Irina et.al [1], in this paper, discussed some examples of different nonlinear models and the application of OLS (Ordinary Least Squares). MA Tabati et.al (2), proposed a robust alternative technique to OLS nonlinear regression method which provide accurate parameter e

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Weiss, Andrew A. "A Comparison of Ordinary Least Squares and Least Absolute Error Estimation." Econometric Theory 4, no. 3 (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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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 (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

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Račkauskas, Alfredas, and Danas Zuokas. "Properties of the coefficient estimators for the linear regression model with heteroskedastic error term." Lietuvos matematikos rinkinys 46 (September 21, 2023): 267–72. http://dx.doi.org/10.15388/lmr.2006.30725.

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In this paper we present estimated generalized least squares (EGLS) estimator for the coefficient vector β in the linear regression model y = βX + ε, where disturbance term can be heteroskedastic. For the heteroskedasticity of the changed segment type, using Monte-Carlo method, we investigate empirical properties of the proposed and ordinary least squares (OLS) estimators. The results show that the empirical covariance of the EGLS estimators is smaller than that of OLS estimators.

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Abdulrasheed, BelloBadawaire* S. Y. Jackson M. G. Bukar. "A COMPARATIVE STUDY OF SOME ESTIMATORS IN ECONOMETRIC MODEL WITH MULTICOLLINEARITY." Global Journal of Engineering Science and Research Management 5, no. 2 (2018): 18–25. https://doi.org/10.5281/zenodo.1186509.

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Mostly, economic data are afflicted with the problems of multicollinearity. This leads to inaccurate parameter estimates in Ordinary Least Squares. Therefore, this paper examined the efficiency of three methods of parameter estimation in regression model (Ordinary Least Squares(OLS), Ridge Regression and Least Absolute Shrinkage and Selection Operator (LASSO)) under multicollinearity. Monte-Carlo experiment of 1000 trials was carried out for four sample sizes (20, 50, 100 and 150), each with three levels of collinearity( Low, Mild and Severe). The findings from this paper showed that when the

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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 (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.

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Modu, Babagana, and Abdullahi Mohammed Inuwa. "Time Series Regression Modeling with AR(1) Errors." European Journal of Statistics 3 (August 24, 2023): 13. http://dx.doi.org/10.28924/ada/stat.3.13.

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When ordinary regression analysis is performed using time-series variables, it is common for the errors (residuals) to have a time-series structure. This violates the usual assumption of independent errors in ordinary least squares (OLS) regressions. Consequently, the estimates of the coefficients and their standard errors are incorrect if the time-series structure of the errors is ignored. In this study, an investigation of a regression model with time-series variables, particularly a simple case, was conducted using the conventional method. The ‘AirPassengers Dataset’ was downloaded from the

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Maekawa, Koichi. "Edgeworth Expansion for the OLS Estimator in a Time Series Regression Model." Econometric Theory 1, no. 2 (1985): 223–39. http://dx.doi.org/10.1017/s0266466600011154.

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In this paper we consider the situation in which ordinary least squares (OLS) is used to estimate an ARMA (1,1) model with one exogenous variable. Applying Edgeworth expansion techniques, we examine the misspecification errors and the approximate distributions of the OLS estimator. Extensive numerical studies were performed and selected results are shown graphically. In addition, a technical device is developed to calculate the Edgeworth coefficients.

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Pati, Kafi Dano. "Using Robust Ridge Regression Diagnostic Method to Handle Multicollinearity Caused High Leverage Points." Academic Journal of Nawroz University 10, no. 1 (2021): 326. http://dx.doi.org/10.25007/ajnu.v10n1a578.

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Statistics practitioners have been depending on the ordinary least squares (OLS) method in the linear regression model for generation because of its optimal properties and simplicity of calculation. However, the OLS estimators can be strongly affected by the existence of multicollinearity which is a near linear dependency between two or more independent variables in the regression model. Even though in the presence of multicollinearity the OLS estimate still remained unbiased, they will be inaccurate prediction about the dependent variable with the inflated standard errors of the estimated par

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Bastiaan, Richy Marcelino, Deiby Tineke Salaki, and Djoni Hatidja. "Comparing the Performance of Prediction Model of Ridge and Elastic Net in Correlated Dataset." Operations Research: International Conference Series 3, no. 1 (2022): 8–13. http://dx.doi.org/10.47194/orics.v3i1.127.

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Multicollinearity refers to a condition where high correlation between independent variables in linear regression model occurs. In this case, using ordinary least squares (OLS) leads to unstable model. Some penalized regression approaches such as ridge and elastic-net regression can be applied to overcome the problem. Penalized regression estimates model by adding a constrain on the size of parameter regression. In this study, simulation dataset is generated, comprised of 100 observation and 95 independent variables with high correlation. This empirical study shows that elastic-net method outp

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Khan, Sajid Ali, Sayyad Khurshid, Shabnam Arshad, and Owais Mushtaq. "Bias Estimation of Linear Regression Model with Autoregressive Scheme using Simulation Study." Journal of Mathematical Analysis and Modeling 2, no. 1 (2021): 26–39. http://dx.doi.org/10.48185/jmam.v2i1.131.

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In regression modeling, first-order auto correlated errors are often a problem, when the data also suffers from independent variables. Generalized Least Squares (GLS) estimation is no longer the best alternative to Ordinary Least Squares (OLS). The Monte Carlo simulation illustrates that regression estimation using data transformed according to the GLS method provides estimates of the regression coefficients which are superior to OLS estimates. In GLS, we observe that in sample size $200$ and $\sigma$=3 with correlation level $0.90$ the bias of GLS $\beta_0$ is $-0.1737$, which is less than al

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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 (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 datase

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Oladapo, Olasunkanmi James, Janet Iyabo Idowu, Abiola Timothy Owolabi, and Kayode Ayinde. "A New Biased Two-Parameter Estimator in Linear Regression Model." EQUATIONS 3 (October 3, 2023): 73–92. http://dx.doi.org/10.37394/232021.2023.3.10.

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The most frequently used estimation technique in the linear regression model is the ordinary least squares (OLS) estimator. The presence of multicollinearity makes the technique inefficient and gives misleading results. This study proposed a new biased two-parameter estimator to deal with the multicollinearity problem. Theory and simulation results show that this estimator outperforms existing estimators considered under some conditions, according to the mean squares error (MSE) criterion. Finally, the real-life dataset illustrates the paper's findings, which agree with the theoretical and sim

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Vable, Anusha M., Mathew V. Kiang, M. Maria Glymour, Joseph Rigdon, Emmanuel F. Drabo, and Sanjay Basu. "Performance of Matching Methods as Compared With Unmatched Ordinary Least Squares Regression Under Constant Effects." American Journal of Epidemiology 188, no. 7 (2019): 1345–54. http://dx.doi.org/10.1093/aje/kwz093.

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AbstractMatching methods are assumed to reduce the likelihood of a biased inference compared with ordinary least squares (OLS) regression. Using simulations, we compared inferences from propensity score matching, coarsened exact matching, and unmatched covariate-adjusted OLS regression to identify which methods, in which scenarios, produced unbiased inferences at the expected type I error rate of 5%. We simulated multiple data sets and systematically varied common support, discontinuities in the exposure and/or outcome, exposure prevalence, and analytical model misspecification. Matching infer

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J.Ravi, S.Dickson, J.Mohan, and R.Akila. "Performance of Robust Regression Estimators." Journal of Statistics and Mathematical Engineering 4, no. 3 (2018): 22–30. https://doi.org/10.5281/zenodo.1686254.

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<em>Ordinary least-squares (OLS) estimates for a linear model are very sensitive to unusual values in the design space or outliers among unpredicted values. Even one single value may have a large effect on the parameter estimates. This paper aims to focus, review and describe some available and popular, robustregressiontechniques, including compare them in terms of efficiency. Recent developed robust regression techniques also discussed.</em>

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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 (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-ba

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Oliveira, Leise Kelli de, Gracielle Gonçalves Ferreira de Araújo, Bruno Vieira Bertoncini, Carlos David Pedrosa, and Francisco Gildemir Ferreira da Silva. "Modelling Freight Trip Generation Based on Deliveries for Brazilian Municipalities." Sustainability 14, no. 16 (2022): 10300. http://dx.doi.org/10.3390/su141610300.

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Freight trip generation modelling is important for forecasting freight movements, and understanding freight movements is crucial to enabling sustainable freight transportation planning. The existing literature focuses on model development, and most of the previous models are estimated by ordinary least squares regression. However, few studies have carefully considered the OLS assumptions. The objective of this paper is to estimate freight trip generation models using deliveries to commercial establishments in Brazilian municipalities. A procedure is described to estimate models by ordinary lea

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Lukman, Adewale F., Kayode Ayinde, B. M. Golam Kibria, and Segun L. Jegede. "Two-Parameter Modified Ridge-Type M-Estimator for Linear Regression Model." Scientific World Journal 2020 (May 15, 2020): 1–24. http://dx.doi.org/10.1155/2020/3192852.

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The general linear regression model has been one of the most frequently used models over the years, with the ordinary least squares estimator (OLS) used to estimate its parameter. The problems of the OLS estimator for linear regression analysis include that of multicollinearity and outliers, which lead to unfavourable results. This study proposed a two-parameter ridge-type modified M-estimator (RTMME) based on the M-estimator to deal with the combined problem resulting from multicollinearity and outliers. Through theoretical proofs, Monte Carlo simulation, and a numerical example, the proposed

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Sadek, Amjed Mohammed, and Lekaa Ali Mohammed. "Evaluation of the Performance of Kernel Non-parametric Regression and Ordinary Least Squares Regression." JOIV : International Journal on Informatics Visualization 8, no. 3 (2024): 1352. http://dx.doi.org/10.62527/joiv.8.3.2430.

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Researchers need to understand the differences between parametric and nonparametric regression models and how they work with available information about the relationship between response and explanatory variables and the distribution of random errors. This paper proposes a new nonparametric regression function for the kernel and employs it with the Nadaraya-Watson kernel estimator method and the Gaussian kernel function. The proposed kernel function (AMS) is then compared to the Gaussian kernel and the traditional parametric method, the ordinary least squares method (OLS). The objective of thi

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Samosir, Ravika Dewi, Deiby Tineke Salaki, and Yohanes Langi. "Comparison of Partial Least Squares Regression and Principal Component Regression for Overcoming Multicollinearity in Human Development Index Model." Operations Research: International Conference Series 3, no. 1 (2022): 1–7. http://dx.doi.org/10.47194/orics.v3i1.126.

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One of the assumptions in ordinary least squares (OLS) in estimating regression parameter is lack of multicollinearity. If the multicollinearity exists, Partial Least Square (PLS) and Principal Component Regression (PCR) can be used as alternative approaches to solve the problem. This research intends to compare those methods in modeling factors that influence the Human Development Index (HDI) of North Sumatra Province in 2019 obtained from the Central Bureau of Statistics. The result indicates that the PLS outperforms the PCR in term of the coefficient of determination and squared error

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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 (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

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Imrhan, Sheik N. "A Method of Developing more Realistic Predictive Models." Proceedings of the Human Factors Society Annual Meeting 30, no. 9 (1986): 945–49. http://dx.doi.org/10.1177/154193128603000922.

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This study demonstrates a better method of regression analysis than Ordinary Least Squares (OLS) method under certain conditions. Ridge Regression, as it is called is useful in situations where there are strong intercorrelations among regressor variables – a condition called multicollinearity. When OLS regression is used to model the relationship between the response variable and the regressor variables the model may exhibit some undesirable properties. Using ergonomics data exhibiting multicollinearity the use of the Ridge technique is demonstrated. An OLS model for the same data is also pres

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Kerkri, Abdelmounaim, Jelloul Allal, and Zoubir Zarrouk. "The L-Curve Criterion as a Model Selection Tool in PLS Regression." Journal of Probability and Statistics 2019 (October 30, 2019): 1–7. http://dx.doi.org/10.1155/2019/3129769.

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Partial least squares (PLS) regression is an alternative to the ordinary least squares (OLS) regression, used in the presence of multicollinearity. As with any other modelling method, PLS regression requires a reliable model selection tool. Cross validation (CV) is the most commonly used tool with many advantages in both preciseness and accuracy, but it also has some drawbacks; therefore, we will use L-curve criterion as an alternative, given that it takes into consideration the shrinking nature of PLS. A theoretical justification for the use of L-curve criterion is presented as well as an app

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Bogoya, Jose D., Johan M. Bogoya, and Alfonso J. Peñuela. "Value-added in higher education: ordinary least squares and quantile regression for a Colombian case." Ingeniería e Investigación 37, no. 3 (2017): 30–36. http://dx.doi.org/10.15446/ing.investig.v37n3.61729.

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Colombia applies two mandatory National State tests every year. The first, known as Saber 11, is applied to students who finish the high school cycle, whereas the second, called Saber Pro, is applied to students who finish the higher education cycle. The result obtained by a student on the Saber 11 exam along with his/her gender, and socioeconomic stratum are our independent variables while the Saber Pro outcome is our dependent variable.We compare the results of two statistical models for the Saber Pro exam. The first model, multi-lineal regression or ordinary least squares (OLS), produces an

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Shariff, N. S. M., and H. M. B. Duzan. "An Application of Proposed Ridge Regression Methods to Real Data Problem." International Journal of Engineering & Technology 7, no. 4.30 (2018): 106. http://dx.doi.org/10.14419/ijet.v7i4.30.22061.

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The Ordinary Least Squares (OLS) is a common method to investigate the linear relationship among variable of interest. The presence of multicollinearity will produce unreliable result in the parameter estimates if OLS is applied to estimate the model. Due to such reason, this study aims to use the proposed ridge estimator as linear combinations of the coefficient of least squares regression of explanatory variables to the real application. The numerical example of stock market price and macroeconomic variables in Malaysia is employed using both methods with the aim of investigating the relatio

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ALLEN, D. E., R. J. POWELL, and A. K. SINGH. "QUANTILE REGRESSION AS A TOOL FOR PORTFOLIO INVESTMENT DECISIONS DURING TIMES OF FINANCIAL DISTRESS." Annals of Financial Economics 06, no. 01 (2011): 1150003. http://dx.doi.org/10.1142/s2010495211500035.

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The worldwide impact of the Global Financial Crisis (GFC) on stock markets, investors and fund managers has lead to a renewed interest in appropriate tools for robust risk management. Quantile regression is a powerful technique and deserves the interest of financial decision makers given its remarkable capabilities for capturing and explaining the behavior of financial return series across a distribution more effectively than ordinary least squares regression methods which are the standard tool. In this paper, we present quantile regression estimation as an attractive additional investment too

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Andiappan, M., FJ Hughes, S. Dunne, W. Gao, and ANA Donaldson. "Adjusting the Oral Health Related Quality of Life Measure (Using Ohip-14) for Floor and Ceiling Effects." Journal of Oral Health and Community Dentistry 9, no. 3 (2015): 99–104. http://dx.doi.org/10.5005/johcd-9-3-99.

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ABSTRACT Introduction The influence of floor (lowest) and ceiling (highest) effects on the outcome measure is of serious concern particularly when the outcome is measured using Likert scales. Conventional regression methods yield biased results and hence tobit regression is to be used to adjust for these effects. This paper is an attempt to use tobit regression in finding the predictors of oral health related quality of life after adjusting for floor and ceiling effects. Methods A sample of 360 participants were asked to self asses their oral health related quality of life (OHRQoL) using Oral

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Matthew, Pascalis Kadaro, and Abubakar Yahaya. "Performance analysis on least absolute shrinkage selection operator, elastic net and correlation adjusted elastic net regression methods." International Journal of Advanced Statistics and Probability 3, no. 1 (2015): 93. http://dx.doi.org/10.14419/ijasp.v3i1.4364.

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<p>Some few decades ago, penalized regression techniques for linear regression have been developed specifically to reduce the flaws inherent in the prediction accuracy of the classical ordinary least squares (OLS) regression technique. In this paper, we used a diabetes data set obtained from previous literature to compare three of these well-known techniques, namely: Least Absolute Shrinkage Selection Operator (LASSO), Elastic Net and Correlation Adjusted Elastic Net (CAEN). After thorough analysis, it was observed that CAEN generated a less complex model.</p>

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Abidoye, A. O., I. M. Ajayi, F. L. Adewale, and J. O. Ogunjobi. "Unbiased Modified Two-Parameter Estimator for the Linear Regression Model." Journal of Scientific Research 14, no. 3 (2022): 785–95. http://dx.doi.org/10.3329/jsr.v14i3.58234.

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This study centers on estimating parameters in a linear regression model in the presence of multicollinearity. Multicollinearity poses a threat to the efficiency of the Ordinary Least Squares (OLS) estimator. Some alternative estimators have been developed as remedial measures to the earlier mentioned problem. This study introduces a new unbiased modified two-parameter estimator based on prior information. Its properties are also considered; the new estimator was compared with other estimators’ Mean Square Error (MSE). A numerical example and Monte Carlo simulation were used to illustrate the

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Washington, Simon, and Jean Wolf. "Hierarchical Tree-Based Versus Ordinary Least Squares Linear Regression Models: Theory and Example Applied to Trip Generation." Transportation Research Record: Journal of the Transportation Research Board 1581, no. 1 (1997): 82–88. http://dx.doi.org/10.3141/1581-11.

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Given the continual need for transportation professionals to forecast trends and the increasing availability of sophisticated and improved modeling methods in new and improved software packages, new methods should be explored to determine whether they can replace or supplement more classical statistical methods. One commonly used classical statistical technique for relating a continuous dependent variable with one or more independent variables (continuous or discrete) is ordinary least squares (OLS) regression. This method is routinely applied in transportation to forecast such things as energ

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Tomz, Michael, Joshua A. Tucker, and Jason Wittenberg. "An Easy and Accurate Regression Model for Multiparty Electoral Data." Political Analysis 10, no. 1 (2002): 66–83. http://dx.doi.org/10.1093/pan/10.1.66.

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Katz and King have previously proposed a statistical model for multiparty election data. They argue that ordinary least-squares (OLS) regression is inappropriate when the dependent variable measures the share of the vote going to each party, and they recommend a superior technique. Regrettably, the Katz-King model requires a high level of statistical expertise and is computationally demanding for more than three political parties. We offer a sophisticated yet convenient alternative that involves seemingly unrelated regression (SUR). SUR is nearly as easy to use as OLS yet performs as well as t

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