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Journal articles on the topic 'Regression analysis model'

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Published: 5 June 2025

Last updated: 24 June 2025

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1

Murugan, N. Senthil Vel, Dr V. Vallinayagam Dr. V.Vallinayagam, and Dr K. Senthamarai Kannan. "Multiple Regression Model and Similarity Analysis – A Comparison Study." Indian Journal of Applied Research 4, no. 8 (2011): 430–32. http://dx.doi.org/10.15373/2249555x/august2014/109.

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2

Zvára, Karel. "Analysis of variance as regression model with a reparametrization restriction." Applications of Mathematics 37, no. 6 (1992): 453–58. http://dx.doi.org/10.21136/am.1992.104523.

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3

Ng, Meei Pyng, and Gary K. Grunwald. "Nonlinear Regression Analysis of the Joint-Regression Model." Biometrics 53, no. 4 (1997): 1366. http://dx.doi.org/10.2307/2533503.

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4

Amit, Singh1 and Khushbu babbar2. "A MUTATION TESTING ANALYSIS AND REGRESSION TESTING." International Journal on Foundations of Computer Science & Technology (IJFCST) 5, no. 3 (2023): 7. https://doi.org/10.5281/zenodo.8344960.

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Software testing is a testing which conducted a test to provide information to client about the quality of the product under test. Software testing can also provide an objective, independent view of the software to allow the business to appreciate and understand the risks of software implementation. In this paper we focused on two main software testing –mutation testing and mutation testing. Mutation testing is a procedural testing method, i.e. we use the structure of the code to guide the test program, A mutation is a little change in a program. Such changes are applied to model low level defects that obtain in the process of coding systems. Ideally mutations should model low-level defect creation. Mutation testing is a process of testing in which code is modified then mutated code is tested against test suites. The mutations used in source code are planned to include in common programming errors. A good unit test typically detects the program mutations and fails automatically. Mutation testing is used on many different platforms, including Java, C++, C# and Ruby. Regression testing is a type of software testing that seeks to uncover new software bugs, or regressions, in existing functional and non-functional areas of a system after changes such as enhancements, patches or configuration changes, have been made to them. When defects are found during testing, the defect got fixed and that part of the software started working as needed. But there may be a case that the defects that fixed have introduced or uncovered a different defect in the software. The way to detect these unexpected bugs and to fix them used regression testing. The main focus of regression testing is to verify that changes in the software or program have not made any adverse side effects and that the software still meets its need. Regression tests are done when there are any changes made on software, because of modified functions.

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Yousif, Omaima A., Adil N. Abed, and Hamid A. Awad. "Modal Split Model Using Multiple Linear Regression Analysis." Anbar Journal for Engineering Sciences 12, no. 2 (2021): 222–28. http://dx.doi.org/10.37649/aengs.2021.171190.

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Kumar, Nand Kishor, Raj Kumar Shah, and Suresh Kumar Sahani. "Regression Analysis and Forecasting with Regression Model in Economics." Mikailalsys Journal of Advanced Engineering International 2, no. 2 (2025): 159–70. https://doi.org/10.58578/mjaei.v2i2.5401.

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This work aims to provide a mathematical model that can be applied to prediction and defines this relationship. It helps economists understand how different factors influence economic indicators such as GDP, inflation, unemployment, and market trends. Forecasting using regression models provides valuable insights for policy-making, business strategies, and economic planning.

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Miroshnychenko, V. O. "Residual analysis in regression mixture model." Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics, no. 3 (2019): 8–16. http://dx.doi.org/10.17721/1812-5409.2019/3.1.

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We consider data in which each observed subject belongs to one of different subpopulations (components). The true number of component which a subject belongs to is unknown, but the researcher knows the probabilities that a subject belongs to a given component (concentration of the component in the mixture). The concentrations are different for different observations. So the distribution of the observed data is a mixture of components’ distributions with varying concentrations. A set of variables is observed for each subject. Dependence between these variables is described by a nonlinear regression model. The coefficients of this model are different for different components. An estimator is proposed for these regression coefficients estimation based on the least squares and generalized estimating equations. Consistency of this estimator is demonstrated under general assumptions. A mixture of logistic regression models with continuous response is considered as an example. It is shown that the general consistency conditions are satisfied for this model under very mild assumptions. Performance of the estimator is assessed by simulations and applied for sociological data analysis. Q-Q diagrams are built for visual comparison of residuals’ distributions.

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Katsetos, Anastasios A., and Andrew C. Brendler. "NBTI model development with regression analysis." Microelectronics Reliability 49, no. 12 (2009): 1498–502. http://dx.doi.org/10.1016/j.microrel.2009.06.009.

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9

Liu, Jin, Yingying Ma, and Hansheng Wang. "Semiparametric model for covariance regression analysis." Computational Statistics & Data Analysis 142 (February 2020): 106815. http://dx.doi.org/10.1016/j.csda.2019.106815.

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10

Cai, Tianxi, and Yingye Zheng. "Model Checking for ROC Regression Analysis." Biometrics 63, no. 1 (2007): 152–63. http://dx.doi.org/10.1111/j.1541-0420.2006.00620.x.

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11

Liao, Jun, Alan T. K. Wan, Shuyuan He, and Guohua Zou. "Optimal model averaging for multivariate regression models." Journal of Multivariate Analysis 189 (May 2022): 104858. http://dx.doi.org/10.1016/j.jmva.2021.104858.

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12

Bücher, Axel, Holger Dette, and Gabriele Wieczorek. "Testing model assumptions in functional regression models." Journal of Multivariate Analysis 102, no. 10 (2011): 1472–88. http://dx.doi.org/10.1016/j.jmva.2011.05.014.

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13

Belsley, David A. "Model selection in regression analysis, regression diagnostics and prior knowledge." International Journal of Forecasting 2, no. 1 (1986): 41–52. http://dx.doi.org/10.1016/0169-2070(86)90029-4.

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14

Martinez, Guillermo Domingo, Heleno Bolfarine, and Hugo Salinas. "Bimodal Regression Model." Revista Colombiana de Estadística 40, no. 1 (2017): 65–83. http://dx.doi.org/10.15446/rce.v40n1.51738.

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Regression analysis is a technique widely used in different areas ofhuman knowledge, with distinct distributions for the error term. Itis the case, however, that regression models with bimodal responsesor, equivalently, with the error term following a bimodal distribution are notcommon in the literature, perhaps due to the lack of simple to dealwith bimodal error distributions. In this paper we propose a simpleto deal with bimodal regression model with a symmetric-asymmetricdistribution for the error term for which for some values of theshape parameter it can be bimodal. This new distribution containsthe normal and skew-normal as special cases. A realdata application reveals that the new model can be extremely usefulin such situations.

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Midi, Habshah, S. K. Sarkar, and Sohel Rana. "Collinearity diagnostics of binary logistic regression model." Journal of Interdisciplinary Mathematics 13, no. 3 (2010): 253–67. http://dx.doi.org/10.1080/09720502.2010.10700699.

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Zhou, Hua Ren, Yue Hong Qian, Xi Qiang Liu, and Ou Wu. "Multiple Regression Analysis Model on Power Dispatch." Advanced Materials Research 512-515 (May 2012): 953–56. http://dx.doi.org/10.4028/www.scientific.net/amr.512-515.953.

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The multiple linear regression method is used, the method of calculating the active power flow and the unit output is discussed , a simple approximate expression is designed, and the corresponding error value is given. a simple calculation rules of congestion cost is given, calculation rules for the actual cost minus the theoretical costs and requirements of the actual costs is as low as possible to avoid blocking; Block can not be avoided, then try to avoid the wind up.

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Kyung, Minjung. "Bayesian analysis of principal component regression model." Journal of the Korean Data And Information Science Society 30, no. 2 (2019): 247–59. http://dx.doi.org/10.7465/jkdi.2019.30.2.247.

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18

Wood, William C., and Sharon L. O'hare. "A Spreadsheet Model for Teaching Regression Analysis." Journal of Education for Business 67, no. 4 (1992): 233–37. http://dx.doi.org/10.1080/08832323.1992.10117550.

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19

Hed, Sigalit, and David Levin. "A ‘subdivision regression’ model for data analysis." International Journal of Computer Mathematics 91, no. 8 (2014): 1683–712. http://dx.doi.org/10.1080/00207160.2013.859252.

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20

Barrera, Janet D. "Mathematics Academic Performance: Multiple Regression Analysis Model." International Journal of Multidisciplinary: Applied Business and Education Research 5, no. 10 (2024): 3905–10. http://dx.doi.org/10.11594/ijmaber.05.10.08.

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A multiple regression model was established based on the examination of factors of academic performance among BSEd Mathematics students during the academic year 2023-2024 of J.H. Cerilles State College-Dumingag Campus. The data of the participants’ perceptions of the extent of teachers’ support, instructional competence, and participants’ academic engagement were examined to determine if these constituted factors of students’ academic performances. This study employed a quantitative design, utilizing multiple regression analysis. Adapted questionnaire checklists were used, and data were analyzed using a five-point Likert scale. A reliability test using the Cronbach alpha coefficient was determined using Jamovi software. The study included students from the first to fourth year of BSEd Mathematics at J.H. Cerilles State College-Dumingag Campus, Dumingag, Zamboanga del Sur, who are currently pursuing a Bachelor of Secondary Education major in Mathematics for the academic year 2023-2024. The weighted arithmetic mean, frequency, and percentage distribution were used to treat the descriptive questions. The study revealed that the teachers provided a high level of support to students’ mathematics learning and were competent in providing quality instructions among mathematics students. The students exhibited a high level of academic engagement; and performed well in their mathematics major subjects. Teachers’ support, instructional competence, and student academic engagement were significant correlates of mathematics students’ academic performances. The high level of teacher’s support is manifested in the way the teachers encourage the students to explore more problem-solving exercises and assist them whenever they encounter difficulties. The teacher’s instructional competence is evident when they encourage students’ interest, motivation, and participation. There is a high level of academic engagement when the students practice more drills. The students acquired skills and competences in math learning areas. The study recommends that the students may promote self- directed learning and motivation among students to improve their academic engagement and performance in mathematics; the mathematics educators may implement strategies to increase academic engagement through fostering individual tasks and collaboration to develop students’ mathematics skills and dependence; the curriculum designers may design mathematics curricula align with educational standards that promote the development of essential mathematics competencies among students; the school administrators may foster a collaborative school that emphasizes the importance of mathematics education and supports ongoing research and innovation in teaching practices; the parents may advocate for parental involvement in actively supporting children’s mathematical learning by providing resources, creating a supportive home environment, and reinforcing the value of mathematics education; and the future researchers may Investigate effective strategies and interventions for enhancing mathematics education and improving student outcomes in the subject. Mathematics is a crucial subject in education, providing students with essential knowledge and skills. To boost academic performance, teachers need to provide intensive support, instructional competence, and student engagement. Siddiqi (2018) found a significant relationship between teachers' effort and students' academic progress, advocating for improved instruction-based classroom learning and good teacher-student connections.

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21

Zhanatauov, S. U. "INVERSE MODEL OF MULTIPLE LINEAR REGRESSION ANALYSIS." Theoretical & Applied Science 60, no. 04 (2018): 201–12. http://dx.doi.org/10.15863/tas.2018.04.60.38.

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22

Tansu, Ayse, and Zarar Naeem. "Fuzzy Regression Analysis with a proposed model." Technium: Romanian Journal of Applied Sciences and Technology 4, no. 10 (2022): 250–73. http://dx.doi.org/10.47577/technium.v4i10.8121.

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Regression analysis refers to methods by which estimates are made for the model parameters from the knowledge of the values of a given input-output data set. The aim of this research this research is to find a suitable model and determine the ‘best’ values of the parameters of the model from the given data. In the statistical regression analysis, deviations between the observed output values and corresponding values predicted by the model are attributed to random errors. It is often assumed that the distribution of these random errors is Gaussian. On the other hand, in fuzzy regression analysis the deviations (errors) are attributed to the imprecision or the vagueness of the system structure or data. The research proposed a new fuzzy linear programming model. The new proposed model is compared with the models used in the literature which are Tanaka, Hojati and Tansu regression models. The results are presented and comparison has been done for each model. Eleven different applications have been mentioned. Then the comparison of results of all the application regarding each similarity measure of goodness of fit is stated in the paper.

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Conti, Marcelo Enrique, Domenico Cucina, and Mauro Mecozzi. "Regression Analysis Model Applied to Biomonitoring Studies." Environmental Modeling & Assessment 13, no. 4 (2007): 553–65. http://dx.doi.org/10.1007/s10666-007-9113-7.

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24

Christensen, Erik. "Multivariate survival analysis using Cox's regression model." Hepatology 7, no. 6 (1987): 1346–58. http://dx.doi.org/10.1002/hep.1840070628.

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25

Hafsa, Fathima, Juveria Soha, Fathima Rida, and Hifsa Naaz Syeda. "An Analysis of Car Price Prediction Using Machine Learning." Research and Reviews: Advancement in Cyber Security 2, no. 2 (2025): 33–40. https://doi.org/10.5281/zenodo.15308198.

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<em>Car price prediction is a critical task in the automotive industry, enabling buyers, sellers, and financial institutions to make informed and objective decisions. This research focuses on applying machine learning techniques, specifically Linear Regression and Lasso Regression to predict used car prices based on multiple factors including fuel type, transmission, seller type, vehicle age, and kilometers driven. The dataset was carefully preprocessed to handle missing values and encode categorical variables, ensuring the data was suitable for model training. Both models were evaluated using R² scores, with Linear Regression achieving high accuracy and Lasso Regression providing a more simplified model by reducing overfitting. The findings demonstrate that even basic regression models can deliver reliable predictions, highlighting the potential of machine learning to improve transparency and efficiency in car pricing within real-world applications.</em>

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26

Azad, Abdulhafedh. "Incorporating Zero-Inflated Poisson (ZIP) Regression Model in Crash Frequency Analysis." International Journal of Novel Research in Interdisciplinary Studies 10, no. 1 (2023): 6–18. https://doi.org/10.5281/zenodo.7632596.

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<strong>Abstract:</strong> This paper addresses the Zero-inflated Poisson (ZIP) regression model as an effective way to handle the excess zeros that usually exist in vehicle crash data and to allow for possible overdispersion in the data. The ZIP model is based on a zero-inflated probability distribution, that allows for frequent zero-valued observations. When the number of zeros is large that the data do not fit standard distributions (e.g., normal, Poisson, binomial, negative-binomial, and beta), the data is referred to as zero inflated. A dual state crash system is assumed in the ZIP model, in which one state is the zero crash state that can be regarded as virtually safe during the observation period, while the other state is the non-zero crash state. This paper starts by applying a multiple linear regression model, a Poisson regression model, a Negative Binomial regression model and then introduces the ZIP model to analyze the 2013-2015 crash data for the Interstate I-94 in the State of Minnesota in the US. Results show that the ZIP model overperformed the other models by fitting the crash data much better and was able to capture almost all the independent variables in the model and make them statistically significant in the analysis after being insignificant by the other models. <strong>Keywords:</strong> Zero-Inflated Poisson Regression, ZIP model, Crash Frequency, Multiple Linear Regression, Poisson Regression, Negative Binomial Regression. <strong>Title:</strong> Incorporating Zero-Inflated Poisson (ZIP) Regression Model in Crash Frequency Analysis <strong>Author:</strong> Azad Abdulhafedh <strong>International Journal of Novel Research in Interdisciplinary Studies</strong> <strong>ISSN 2394-9716</strong> <strong>Vol. 10, Issue 1, January 2023 - February 2023</strong> <strong>Page No: 6-18</strong> <strong>Novelty Journals</strong> <strong>Website: www.noveltyjournals.com</strong> <strong>Published Date: 11-February-2022</strong> <strong>DOI: https://doi.org/10.5281/zenodo.7632596</strong> <strong>Paper Download Link (Source)</strong> <strong>https://www.noveltyjournals.com/upload/paper/Incorporating%20Zero-Inflated-11022023-2.pdf</strong>

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Altun, Emrah. "Weighted-exponential regression model: An alternative to the gamma regression model." International Journal of Modeling, Simulation, and Scientific Computing 10, no. 06 (2019): 1950035. http://dx.doi.org/10.1142/s1793962319500351.

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In this study, weighted-exponential regression model is proposed for modeling the right-skewed response variable as an alternative to the gamma regression model. The maximum likelihood, method of moments, least-squares and weighted least-squares estimation methods are used to estimate unknown parameters of re-parametrized weighted-exponential distribution. The simulation study is conducted to compare the efficiencies of parameter estimation methods. An application on coalition duration dataset is given to demonstrate the usefulness of proposed regression model against the gamma regression model. The residual analysis is performed to evaluate the accuracy of the fitted model. Empirical findings show that the weighted-exponential regression model provides better fits than the gamma regression model and could be a good choice for modeling the right-skewed response variable.

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28

Ng, Set Foong, Yee Ming Chew, Pei Eng Chng, and Kok Shien Ng. "An Insight of Linear Regression Analysis." Scientific Research Journal 15, no. 2 (2018): 1. http://dx.doi.org/10.24191/srj.v15i2.5477.

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Regression models are developed in various field of applications to help researchers to predict certain variables based on other predictor variables. The dependent variables in the regression model are estimated by a number of independent variables. Model utility test is a hypothesis testing procedure in regression to verify if there is a useful relationship between the dependent variable and the independent variable. The hypothesis testing procedure that involves p-value is commonly used in model utility test. A new technique that involves coefficient of determination R2 in model utility test is developed in this paper. The effectiveness of the model utility test in testing the significance of regression model is evaluated using simple linear regression model with the significance level α = 0.01, 0.025 and 0.05. The study in this paper shows that a regression model that is declared to be a significant model by using model utility test, however it fails to guarantee a strong linear relationship between the independent variable and dependent variable. Based on the evaluation presented in this paper, it is shown that the p-value approach in model utility test is not a good technique in evaluating the significance of a regression model. The results of this study could serve as a reference for other researchers applying regression analysis in their studies.

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Ng, Set Foong, Yee Ming Chew, Pei Eng Chng, and Kok Shien Ng. "An Insight of Linear Regression Analysis." Scientific Research Journal 15, no. 2 (2018): 1. http://dx.doi.org/10.24191/srj.v15i2.9347.

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Regression models are developed in various field of applications to help researchers to predict certain variables based on other predictor variables. The dependent variables in the regression model are estimated by a number of independent variables. Model utility test is a hypothesis testing procedure in regression to verify if there is a useful relationship between the dependent variable and the independent variable. The hypothesis testing procedure that involves p-value is commonly used in model utility test. A new technique that involves coefficient of determination R2 in model utility test is developed in this paper. The effectiveness of the model utility test in testing the significance of regression model is evaluated using simple linear regression model with the significance level α = 0.01, 0.025 and 0.05. The study in this paper shows that a regression model that is declared to be a significant model by using model utility test, however it fails to guarantee a strong linear relationship between the independent variable and dependent variable. Based on the evaluation presented in this paper, it is shown that the p-value approach in model utility test is not a good technique in evaluating the significance of a regression model. The results of this study could serve as a reference for other researchers applying regression analysis in their studies.

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Shetty, Soumya, Janet Jyothi Dsouza, and Iqbal Thonse Hawaldar. "Rolling regression technique and cross-sectional regression: A tool to analyze Capital Asset Pricing Model." Investment Management and Financial Innovations 18, no. 4 (2021): 241–51. http://dx.doi.org/10.21511/imfi.18(4).2021.21.

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The Capital Asset Pricing Model (henceforth, CAPM) is considered an extensively used technique to approximate asset pricing in the field of finance. The CAPM holds the power to explicate stock movements by means of its sole factor that is beta co-efficient. This study focuses on the application of rolling regression and cross-sectional regression techniques on Indian BSE 30 stocks. The study examines the risk-return analysis by using this modern technique. The applicability of these techniques is being viewed in changing business environments. These techniques help to find the effect of selected variables on average stock returns. A rolling regression study rolls the data for changing the windows for every 3-month period for three years. The study modifies the model with and without intercept values. This has been applied to the monthly prices of 30 BSE stocks. The study period is from January 2009 to December 2018. The study revealed that beta is a good predictor for analyzing stock returns, but not the intercept values in the developed model. On the other hand, applying cross-section regression accepts the null hypothesis. α, β, β2 ≠ 0. Therefore, a researcher is faced with the task of finding limitations of each methodology and bringing the best output in the model.

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YANG, MIIN-SHEN, and HWEI-MING CHEN. "FUZZY CLASS LOGISTIC REGRESSION ANALYSIS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12, no. 06 (2004): 761–80. http://dx.doi.org/10.1142/s0218488504003193.

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Distribution mixtures are used as models to analyze grouped data. The estimation of parameters is an important step for mixture distributions. The latent class model is generally used as the analysis of mixture distributions for discrete data. In this paper, we consider the parameter estimation for a mixture of logistic regression models. We know that the expectation maximization (EM) algorithm was most used for estimating the parameters of logistic regression mixture models. In this paper, we propose a new type of fuzzy class model and then derive an algorithm for the parameter estimation of a fuzzy class logistic regression model. The effects of the explanatory variables on the response variables are described. The focus is on binary responses for the logistic regression mixture analysis with a fuzzy class model. An algorithm, called a fuzzy classification maximum likelihood (FCML), is then created. The mean squared error (MSE) based accuracy criterion for the FCML and EM algorithms to the parameter estimation of logistic regression mixture models are compared using the samples drawn from logistic regression mixtures of two classes. Numerical results show that the proposed FCML algorithm presents good accuracy and is recommended as a new tool for the parameter estimation of the logistic regression mixture models.

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Chen, Jia, and Junke Kou. "Nonparametric Pointwise Estimation for a Regression Model with Multiplicative Noise." Journal of Function Spaces 2021 (October 11, 2021): 1–10. http://dx.doi.org/10.1155/2021/1599286.

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In this paper, we consider a general nonparametric regression estimation model with the feature of having multiplicative noise. We propose a linear estimator and nonlinear estimator by wavelet method. The convergence rates of those regression estimators under pointwise error over Besov spaces are proved. It turns out that the obtained convergence rates are consistent with the optimal convergence rate of pointwise nonparametric functional estimation.

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Huang, Zhensheng, Zhen Pang, Bingqing Lin, and Quanxi Shao. "Model structure selection in single-index-coefficient regression models." Journal of Multivariate Analysis 125 (March 2014): 159–75. http://dx.doi.org/10.1016/j.jmva.2013.12.006.

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Swati Gupta. "Optimized Fused Regression Model for Regression Algorithms." Journal of Information Systems Engineering and Management 10, no. 9s (2025): 685–97. https://doi.org/10.52783/jisem.v10i9s.1295.

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One or more independent variables are compared to a dependent variable using regression analysis. Prediction and inference are its key goals. This strategy helps identify data patterns and trends to estimate constant outputs from variables. This research examines how Gradient Adaptive Moment Estimation Optimiser and ensemble multiple linear regression may improve regression task prediction. OFRM efficacy is assessed using six datasets from distinct sectors. six datasets from different domains were utilised to test OFRM. Test it against five regression models. Apply strict criteria and test OFRM extensively to establish its impact on anticipated accuracy, robustness, and generalisability. OFRM dominates individual regression on all datasets. This research shows OFRM's performance in regression scenarios to advance ensemble learning. This paper emphasises the necessity to combine optimisation and ensemble techniques to enhance regression models for real-world applications. Regression model performance on NFT datasets was evaluated using MSE, RMSE, MAE, and R³ measures. OGFR predicts accurately with the lowest MSE (1.04), RMSE (2.21), and MAE (1.29). Best fit is achieved with a R² value of 0.85, accounting for 85% of sample variance. By outperforming DNR and KNN with R² of 0.55 and MSE of 1.91, OGFR is the top model for NFT dataset predictions with a R² of 0.75 and MSE of 1.24.

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Zhou, Yushan. "Stock Forecasting Based on Linear Regression Model and Nonlinear Machine Learning Regression Model." Advances in Economics, Management and Political Sciences 57, no. 1 (2024): 7–13. http://dx.doi.org/10.54254/2754-1169/57/20230364.

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To enhance the accuracy of stock price prediction for Netflix and provide individuals with a comprehensive understanding of stock trading prices, this study constructs a predictive model by employing three distinct approaches: a linear regression model, a Long Short-term Memory (LSTM) artificial neural network, and a Gated Recursive Unit (GRU) which serves as a component of the LSTM architecture. A prediction scheme is devised, utilizing historical stock data spanning from 2002 to 2022 for Netflix. The primary objective is to forecast the stock price of Netflix for the subsequent 20-day period. To evaluate the efficacy of the three models, a rigorous assessment is conducted employing robust evaluation indices. The outcomes of this analysis will enable a determination of the fitting adequacy of each model, thereby facilitating the identification of the most suitable approach for accurate stock price prediction in the context of Netflix. This research endeavors to contribute to the field of stock market analysis by leveraging advanced predictive modeling techniques for enhanced forecasting accuracy and insightful decision-making.

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ISHIBUCHI, Hisao, Hideo TANAKA, and Kazunori NAGASAKA. "Interval Data Analysis by Revised Interval Regression Model." Transactions of the Society of Instrument and Control Engineers 25, no. 11 (1989): 1218–24. http://dx.doi.org/10.9746/sicetr1965.25.1218.

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Kyung, Minjung. "Bayesian analysis of latent factor quantile regression model." Journal of the Korean Data And Information Science Society 33, no. 5 (2022): 755–73. http://dx.doi.org/10.7465/jkdi.2022.33.5.755.

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Emir, Senol, Hasan Dincer, Umit Hacioglu, and Serhat Yuksel. "Random Regression Forest Model using Technical Analysis Variables." International Journal of Finance & Banking Studies (2147-4486) 5, no. 3 (2016): 85–102. http://dx.doi.org/10.20525/ijfbs.v5i3.461.

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The purpose of this study is to explore the importance and ranking of technical analysis variables in Turkish banking sector. Random Forest method is used for determining importance scores of inputs for eight banks in Borsa Istanbul. Then two predictive models utilizing Random Forest (RF) and Artificial Neural Networks (ANN) are built for predicting BIST-100 index and bank closing prices. Results of the models are compared by three metrics namely Mean Absolute Error (MAE), Mean Square Error (MSE), Median Absolute Error (MedAE). Findings show that moving average (MAV-100) is the most important variable for both BIST -100 index and bank closing prices. Therefore, investors should follow this technical indicator with respect to Turkish banks. In addition ANN shows better performance for all metrics.

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Hu, De Xiu, Zhi Qi Zhou, Yong Li, and Xiao Long Wu. "Dam Safety Analysis Based on Stepwise Regression Model." Advanced Materials Research 204-210 (February 2011): 2158–61. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.2158.

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The simulating and predicting analysis model is studied by the deformation monitoring data of Bikou earth-rockfill dam. Based on the least squares method of Statistics principles, the stepwise regression model has been established of earth-rockfill dam deformation displacement, which is used to fit and forecast the measured deformation data sequences of dam. The results shows that the deformation monitoring model of Bikou earth-rockfill dam having higher fitting precision, longer predict cycle, can be better applied to the fitting and prediction of dam deformation monitoring data.

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Petrucci, Alessandra, Nicola Salvati, and Chiara Seghieri. "Autologistic regression model for poverty mapping and analysis." Advances in Methodology and Statistics 1, no. 1 (2004): 225–34. http://dx.doi.org/10.51936/zmuo1724.

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Poverty mapping in developing countries has become an increasingly important tool in the search for ways to improve living standards in an economically and environmentally sustainable manner. Although the classical econometric methods provide information on the geographic distribution of poverty, they do not take into account the spatial dependence of the data and generally they do not consider any environmental information. Methods which use spatial analysis tools are required to explore such spatial dimensions of poverty and its linkages with the environmental conditions. This study applies a spatial analysis to determine those variables that affect household poverty and to estimate the number of poor people in the target areas.

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Kyung, Minjung. "Bayesian analysis of quantile principal component regression model." Journal of the Korean Data And Information Science Society 32, no. 4 (2021): 739–55. http://dx.doi.org/10.7465/jkdi.2021.32.4.739.

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Castillo Tumaille, Guillermo Isaac, Ana Maria Guerra Tejada, and Eva Maria de Lourdes Loaiza Massuh. "Life expectancy analysis from a multiple regression model." Universidad Ciencia y Tecnología 25, no. 110 (2021): 198–207. http://dx.doi.org/10.47460/uct.v25i110.492.

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The present study aims to establish the relationship between the indicators of life expectancy, education and income, which are part of the human development index. The methodology used was a descriptive and correlational statistical analysis, with a population of 2480 families from the location of Virgen de Fátima - Guayas - Ecuador which made possible the analysis of the quality of social and economical development within this sector. The results show that the education index is directly related to life expectancy, which proves that there would be significant changes in life quality if it were invested in educational programs. Keywords: Multiple linear regression, life expectancy, education, per capita income. References [1]V. V. Karina Temporelli, «Relación entre esperanza de vida e ingreso. Un análisis para América Latina y el Caribe,» Lecturas de Economia, nº 75, pp. 61-85, 211. [2]E. Gómez, T. Bolaños, J. Riascos, «La educación y el ingreso como determinantes de la esperanza de vida en Colombia - 2002-2012,» Tendencias, vol. XVII, nº 2, pp. 31-55, 2016. [3]D. Strijker, G,.Bosworth, G. Bouter, «"Research methods in rural studies: Qualitative, quantitative and mixed methods",» Journal of Rural Studies, vol. 78, pp. 262-270, 2020. [4]W. Luo, Y. Xie, «"Economic growth, income inequality and life expectancy in China",» Social Science & Medicine, vol. 256, p. 113046, Julio 2020. [5]M. Escobar Bravo, D. Puga González, Monserrat Martín Baranera, «"Análisis de la esperanza de vida libre de discapacidad a lo largo de la biografía: de la madurez a la vejez",» Gaceta Sanitaria, vol. 26, nº 4, pp. 330-335, 2012. [6]R. Kotschy, «"Health dynamics shape life-cycle incomes",» Journal of Health Economics, vol. 75, p. 102398, 2021. [7]L. Díaz Serrano, «"The duration of compulsory education and the transition to secondary education: Panel data evidence from low-income countries",» International Journal of Educational Development, vol. 75, p. 102189, 2020. [8]Programa de las Naciones Unidas para el Desarrollo (PNUD), «"Índices e indicadores de desarrollo humano",» Estados Unidos, 2018. [9]P. Nolan, J. Sender, «"Death rates, life expentancy and China's economic reforms: A critique of A.K. Sen",» World Development, vol. 20, nº 9, pp. 1279-1303, 1992. [10]J B.Soriano, D.Rojas, J. Alonso, J-M.Antó, P. Joan, E. Fernández, lA. L.Garcia, F. Benavides,, «"La carga de enfermedad en España: resultados del Estudio de la Carga Global de las Enfermedades 2016",» Medicina clinica, vol. 151, nº 5, pp. 171-190, 14 septiembre 2018. [11]V. Kontis, J. E Bennett, C. D Mathers, G. Li, K. capataz, M. Ezzati, «"Future life expectancy in 35 industrialised countries: projections with a Bayesian model ensemble",» The Lancet, vol. 389, nº 10076, pp. 1323-1335, 2017. [12]A. Novak, Z.Cepar, A. Tronco, «"El papel de los años esperados de escolaridad entre los determinantes de la esperanza de vida",» INDER SCIENCE ONLINE, vol. 20, nº 1, 23 mayo 2016. [13]S. Rangel-Rigotti, S. Rodrigues & Guimarães-Rodrigues, «"A re-examination of the expected years of schooling: What can it tell us?",» econstor, nº 117, 2013. [14]F. Noorbakhsh, A. Paloni, A. Youssef, «"Human Capital and FDI Inflows to Developing Countries: New Empirical Evidence",» World Development, vol. 29, nº 9, pp. 1593-1610, septiembre 2001. [15]D. Filmer, H. Rogers, N. Angrist, S. Sabarwal, «"Learning-adjusted years of schooling (LAYS): Defining a new macro measure of education",» Economics of Education Review, vol. 77, p. 101971, 2020. [16]B. Huat Ver, S. Gorard, «"Effective classroom instructions for primary literacy: A critical review of the causal evidence",» International Journal of Educational Research, vol. 102, p. 101577, 2020. [17] A Smith «Naturaleza y causa de la riqueza de las naciones,» Fondo de Cultura Económica., 1776. [18]G. Kaya Uyanıka, N. Güler, «"A Study on Multiple Linear Regression Analysis",» Procedia - Ciencias sociales y del comportamiento, vol. 106, pp. 234-240, 2013. [19]L. Nathans, F. Oswald , K. Nimon, «Interpreting Multiple Linear Regression: A Guidebook of Variable,» Practical Assessment, Research, and Evaluation, vol. 17, nº 0, pp. 1-19, Abril 2012. [20]M. Tranmer, J. Murphy, M. Elliot and M. Pampaka, «"Multiple Linear Regression (2nd Edition)",» enero 2020. [En línea]. Disponible: https://hummedia.manchester.ac.uk/institutes/cmist/a. [21]Revista de Ciencias Sociales, «Universidad de Costa Rica,» vol. IV, nº 94, 2001. [22]A. Mehdi Riazi, G. Hessameddin Ghanbar and B. Fazel c, «"The contexts, theoretical and methodological orientation of EAP research: Evidence from empirical articles published in the Journal of English for Academic Purposes",» Journal of English for Academic Purposes, vol. 48, p. 100925, 2020. [23]GAD Virgen De Fatima, «"Plan De Desarrollo Y Ordenamiento Territorial",» virgen de Fatima - Ecuador, 2015. [24]E. Montero Rojas, «Educación e ingreso como predictores de la esperanzade vida: evidencias de un análisis de regresión aplicadoa indicadores de desarrollo humano,» Ciencias Sociales, pp. 51-60, 2001. [25]K. Bennett, M. Foreman, & Ezzati, «Esperanza de vida futura en 35 países industrializados: proyecciones con un conjunto de modelos bayesianos,» The Lancet, vol. 385, nº 10076, pp. 1323-1335, 2017.

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Rahimian Azad, Zahra, and Afshin Fallah. "Bayesian Model Averahing in Inverse Gaussian Regression Analysis." Journal of Statistical Sciences 15, no. 1 (2021): 97–118. http://dx.doi.org/10.52547/jss.15.1.6.

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Wang, Hui Li, and Zhong Ke Shi. "Hierarchical Regression Model for Truck Collision Severity Analysis." Advanced Materials Research 671-674 (March 2013): 2889–92. http://dx.doi.org/10.4028/www.scientific.net/amr.671-674.2889.

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Collisions involving trucks have long been a major safety concern for the collision severity. This paper describes the rationale and construction of a hierarchical model that can be used to assess severity of truck collisions in a freeway network. The outcome of models and associated data analysis revealed that presence of ramp and freeway segment length were important factors affecting truck safety performance. Furthermore, weather condition was found to be a significant factor in the severity of truck collisions. Using these models, practitioners can identify freeway sites where truck crashes are more likely to occur and then take measure to mitigate the severity.

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Utkin, Lev V. "Regression analysis using the imprecise Bayesian normal model." International Journal of Data Analysis Techniques and Strategies 2, no. 4 (2010): 356. http://dx.doi.org/10.1504/ijdats.2010.037477.

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Xie, Feng-Chang, and Bo-Cheng Wei. "Diagnostics analysis in censored generalized Poisson regression model." Journal of Statistical Computation and Simulation 77, no. 8 (2007): 695–708. http://dx.doi.org/10.1080/10629360600581316.

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Wong, George Y., and William M. Mason. "The Hierarchical Logistic Regression Model for Multilevel Analysis." Journal of the American Statistical Association 80, no. 391 (1985): 513–24. http://dx.doi.org/10.1080/01621459.1985.10478148.

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Smith, James A., and Alan F. Karr. "Flood Frequency Analysis Using the Cox Regression Model." Water Resources Research 22, no. 6 (1986): 890–96. http://dx.doi.org/10.1029/wr022i006p00890.

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Drezner, Zvi, George A. Marcoulides, and Said Salhi. "Tabu search model selection in multiple regression analysis." Communications in Statistics - Simulation and Computation 28, no. 2 (1999): 349–67. http://dx.doi.org/10.1080/03610919908813553.

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Dong, Guozhu, and Vahid Taslimitehrani. "Pattern-Aided Regression Modeling and Prediction Model Analysis." IEEE Transactions on Knowledge and Data Engineering 27, no. 9 (2015): 2452–65. http://dx.doi.org/10.1109/tkde.2015.2411609.

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