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Journal articles on the topic 'Regression of Proportion'

Author: Grafiati

Published: 8 September 2023

Last updated: 19 July 2025

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Wang, Jin, Aizhi Sun, Qian Gao, Fuqiang Zhai, Shen Wu, and Alex A. Volinsky. "Slag material's proportion optimised by polynomial regression." Proceedings of the Institution of Civil Engineers - Construction Materials 167, no. 1 (2014): 8–13. http://dx.doi.org/10.1680/coma.12.00003.

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Milicevic, Mario, Vedran Batos, Adriana Lipovac, and Zeljka Car. "Deep Regression Neural Networks for Proportion Judgment." Future Internet 14, no. 4 (2022): 100. http://dx.doi.org/10.3390/fi14040100.

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Deep regression models are widely employed to solve computer vision tasks, such as human age or pose estimation, crowd counting, object detection, etc. Another possible area of application, which to our knowledge has not been systematically explored so far, is proportion judgment. As a prerequisite for successful decision making, individuals often have to use proportion judgment strategies, with which they estimate the magnitude of one stimulus relative to another (larger) stimulus. This makes this estimation problem interesting for the application of machine learning techniques. In regard to this, we proposed various deep regression architectures, which we tested on three original datasets of very different origin and composition. This is a novel approach, as the assumption is that the model can learn the concept of proportion without explicitly counting individual objects. With comprehensive experiments, we have demonstrated the effectiveness of the proposed models which can predict proportions on real-life datasets more reliably than human experts, considering the coefficient of determination (>0.95) and the amount of errors (MAE < 2, RMSE < 3). If there is no significant number of errors in determining the ground truth, with an appropriate size of the learning dataset, an additional reduction of MAE to 0.14 can be achieved. The used datasets will be publicly available to serve as reference data sources in similar projects.

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Han, Bing, and Nelson Lim. "Estimating conditional proportion curves by regression residuals." Statistics in Medicine 29, no. 13 (2010): 1443–54. http://dx.doi.org/10.1002/sim.3889.

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Lee, Dong-Hee. "Regression Models for Bivariate Semi-continuous Proportion Data." Korean Data Analysis Society 19, no. 2 (2017): 663–73. http://dx.doi.org/10.37727/jkdas.2017.19.2.663.

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Naik, V. D., and P. C. Gupta. "On regression method for estimating a population proportion." Statistical Papers 37, no. 1 (1996): 85–92. http://dx.doi.org/10.1007/bf02926162.

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Salsabila, Santi Wahyu, Achmad Efendi, and Nurjannah Nurjannah. "Simulation Study of Zero Inflated Negative Binomial Regression." CAUCHY: Jurnal Matematika Murni dan Aplikasi 10, no. 1 (2025): 457–68. https://doi.org/10.18860/cauchy.v10i1.32499.

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This study aims at evaluating the performance of Zero Inflated Negative Binomial (ZINB) regression analysis using the Maximum Likelihood Estimation (MLE) approach through simulation study. The research data used are secondary data and simulations. Secondary data was obtained from the Ministry of Health of the Republic of Indonesia in 2023 regarding cases of under-five deaths due to pneumonia with a total of 38 samples. The simulation study is conducted to analyze the performance of ZINB regression based on various sample sizes and proportions of zero values. The results show that the ZINB regression model with the MLE approach produces parameter estimates that tend to be more sensitive to sample size, with improved performance at large sample sizes. The ZINB regression model gives results for a large proportion of zeros and satisfies the excess zero condition. Data with a large proportion of zeros reflects high variability as well as the presence of excess zeros, so the ZINB regression model can provide more stable and precise parameter estimates than those with a lower proportion of zeros. Therefore, the ZINB regression model is effective for data with a high proportion of zeros as it fits the characteristics of the data distribution.

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Galvis, Diana M., Dipankar Bandyopadhyay, and Victor H. Lachos. "Augmented mixed beta regression models for periodontal proportion data." Statistics in Medicine 33, no. 21 (2014): 3759–71. http://dx.doi.org/10.1002/sim.6179.

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Sambhram, S. Patil. "Profitability of Indian Automobile Companies." Journal of Research and Review in Purchasing and Supply Management 2, no. 1 (2024): 40–49. https://doi.org/10.5281/zenodo.14504663.

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<em>Car industry is one of the vital areas in India and it is the advancing business in the country. The business helps the economy in numerous ways. The specialist chose 10 companies. For the investigation of benefit. The time duration of the review are from 2015 to 2024. The review utilized auxiliary information (bookkeeping information). The analytical tests involved proportional examination such as Pooled Regression, Fixed Effect Model, Random Effect Model, First - Differencing and Hausman test (Ph-test) were run and the following results were obtained., some are diminished during the review time frame, net benefit of Companies Expanded significantly. It was additionally confirmed that productivity regarding net benefit proportion, working benefit proportion, return on resources (sales revenue), Raw Material Cost and employees cost (pay) and Power and Fuel Consumption .</em> <em>All the companies mentioned in Intro. are superior during the review time frame. The consequences of Regression demonstrated that tremendous contrasts were tracked down in net benefit. proportion, return on resources and profit per divide between the example organizations and in the event that of working benefit proportion and profit from ventures, no massive contrasts were found among test organizations.</em>

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Nordmann, Tamara, Saskia Dede Davi, Michael Ramharter, and Johannes Mischlinger. "Quantification of the Proportion of Unfavorable Clinical Outcomes among Imported Malaria Patients According to the Degree of Semi-Immunity on Population Level: An Ecological Study." American Journal of Tropical Medicine and Hygiene 105, no. 2 (2021): 477–79. http://dx.doi.org/10.4269/ajtmh.21-0196.

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ABSTRACT. The protective effect of semi-immunity to alleviate clinical complications of malaria remains incompletely understood. This ecological study quantified the proportion of unfavorable clinical outcomes among patient populations with imported malaria as a function of the reported proportion of absent semi-immunity in a patient population. Group-level proportions were extracted from published studies on imported malaria. Linear regression analyses demonstrate a consistent positive trend between the average proportion of absent semi-immunity in patient populations of imported malaria and the proportion of unfavorable clinical outcomes therein. Regression equations provide a group-level estimate of attributable fractions of clinical complications resulting from absent semi-immunity to malaria.

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Shim, Jooyong, and Changha Hwang. "Geographically weighted kernel logistic regression for small area proportion estimation." Journal of the Korean Data and Information Science Society 27, no. 2 (2016): 531–38. http://dx.doi.org/10.7465/jkdi.2016.27.2.531.

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Zhang, L., B. Wu, B. Huang, and P. Li. "Nonlinear estimation of subpixel proportion via kernel least square regression." International Journal of Remote Sensing 28, no. 18 (2007): 4157–72. http://dx.doi.org/10.1080/01431160600993454.

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Martínez, Sergio, Maria Rueda, Antonio Arcos, and Helena Martínez. "Estimating the Proportion of a Categorical Variable With Probit Regression." Sociological Methods & Research 49, no. 3 (2018): 809–34. http://dx.doi.org/10.1177/0049124118761771.

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This article discusses the estimation of a population proportion, using the auxiliary information available, which is incorporated into the estimation procedure by a probit model fit. Three probit regression estimators are considered, using model-based and model-assisted approaches. The theoretical properties of the proposed estimators are derived and discussed. Monte Carlo experiments were carried out for simulated data and for real data taken from a database of confirmed dengue cases in Mexico. The probit estimates give valuable results in comparison to alternative estimators. Finally, the proposed methodology is applied to data obtained from an immigration survey.

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Parker, Anthony J., Dipankar Bandyopadhyay, and Elizabeth H. Slate. "A spatial augmented beta regression model for periodontal proportion data." Statistical Modelling: An International Journal 14, no. 6 (2014): 503–21. http://dx.doi.org/10.1177/1471082x14535515.

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Martínez, Sergio, María del Mar Rueda, Antonio Arcos, and Helena Martínez. "Estimating the proportion of a categorical variable with probit regression." Sociological Methods & Research 49, no. 3 (2018): 809–34. https://doi.org/10.5281/zenodo.10583196.

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Rojas Guerra, Renata, Fernando A. Peña-Ramírez, Tatiane Fontana Ribeiro, Gauss Moutinho Cordeiro, and Charles Peixoto Mafalda. "Unit Regression Models to Explain Vote Proportions in the Brazilian Presidential Elections in 2018." Revista Colombiana de Estadística 47, no. 2 (2024): 283–300. https://doi.org/10.15446/rce.v47n2.111306.

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In this paper, we aim to identify the covariates associated with the proportion of votes of candidates elected in Brazilian municipalities with a population of more than 300,000 inhabitants. We analyzed the vote proportions from the 2018 presidential runoff election using distributions within the Generalized Additive Models for Location, Scale, and Shape (GAMLSS) class. Unit distributions are quite useful for modeling vote proportions due to their flexibility to accommodate asymmetry and heavy tails. Furthermore, they provide adequate representations of the physiological properties and the empirical distribution of the data. We _t the beta, simplex, unit gamma, and unit Lindley regression models, considering random and fixed effects components to verify spatial correlation among the municipalities. The beta regression with fixed components regarding Brazilian regions is superior. The covariates with significant effects are the proportion of evangelicals, monthly household income per capita, the political spectrum of the governors' party elected in 2014 and 2018, and if the municipality is the capital of the state. We note that some Brazilian regions impact the vote proportions' mean and dispersion.

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Beh, Chong You, Ee Meng Cheng, Nashrul Fazli Mohd Nasir, et al. "Regression Analysis of the Dielectric and Morphological Properties for Porous Nanohydroxyapatite/Starch Composites: A Correlative Study." International Journal of Molecular Sciences 23, no. 10 (2022): 5695. http://dx.doi.org/10.3390/ijms23105695.

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This paper aims to investigate the dielectric properties, i.e., dielectric constant (ε′), dielectric loss factor (ε″), dielectric tangent loss (tan δ), electrical conductivity (σ), and penetration depth (Dp), of the porous nanohydroxyapatite/starch composites in the function of starch proportion, pore size, and porosity over a broad band frequency range of 5 MHz–12 GHz. The porous nanohydroxyapatite/starch composites were fabricated using different starch proportions ranging from 30 to 90 wt%. The results reveal that the dielectric properties and the microstructural features of the porous nanohydroxyapatite/starch composites can be enhanced by the increment in the starch proportion. Nevertheless, the composite with 80 wt% of starch proportion exhibit low dielectric properties (ε′, ε″, tan δ, and σ) and a high penetration depth because of its highly interconnected porous microstructures. The dielectric properties of the porous nanohydroxyapatite/starch composites are highly dependent on starch proportion, average pore size, and porosity. The regression models are developed to express the dielectric properties of the porous nanohydroxyapatite/starch composites (R2 > 0.96) in the function of starch proportion, pore size, and porosity from 1 to 11 GHz. This dielectric study can facilitate the assessment of bone scaffold design in bone tissue engineering applications.

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Sannier, Christophe, Ronald E. McRoberts, Louis-Vincent Fichet, and Etienne Massard K. Makaga. "Using the regression estimator with Landsat data to estimate proportion forest cover and net proportion deforestation in Gabon." Remote Sensing of Environment 151 (August 2014): 138–48. http://dx.doi.org/10.1016/j.rse.2013.09.015.

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Balqis, Nabila Azarin, Suci Astutik, and Solimun Solimun. "Principal Component Regression Modelling with Variational Bayesian Approach to Overcome Multicollinearity at Various Levels of Missing Data Proportion." JTAM (Jurnal Teori dan Aplikasi Matematika) 6, no. 4 (2022): 1013. http://dx.doi.org/10.31764/jtam.v6i4.10223.

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This study aims to model Principal Component Regression (PCR) using Variational Bayesian Principal Component Analysis (VBPCA) with Ordinary Least Square (OLS) as a method of estimating regression parameters to overcome multicollinearity at various levels of the proportion of missing data. The data used in this study are secondary data and simulation data contaminated with collinearity in the predictor variables with various missing data proportions of 1%, 5%, and 10%. The secondary data used is the Human Depth Index in Java in 2021, complete data without missing values. The results indicate that the multicollinearity in secondary and original data can be optimally overcome as indicated by the smaller standard error value of the regression parameter for the PCR using VBPCA method which is smaller and has a relative efficiency value of less than 1. VBPCA can handle the proportion of missing data to less than 10%. The proportion of missing data causes information from the original variable to decrease, as evidenced by immense MAPE value and the parameter estimation bias that gets bigger. Then the cross validation (Q^2 ) value and the coefficient of determination (adjusted R^2 ) are get smaller as the proportion of missing data increases.

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Jeng, X. Jessie, and Xiongzhi Chen. "Predictor ranking and false discovery proportion control in high-dimensional regression." Journal of Multivariate Analysis 171 (May 2019): 163–75. http://dx.doi.org/10.1016/j.jmva.2018.12.006.

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Reeve, Russell. "Confidence interval of difference of proportions in logistic regression in presence of covariates." Statistical Methods in Medical Research 27, no. 2 (2016): 451–65. http://dx.doi.org/10.1177/0962280216631583.

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Comparison of treatment differences in incidence rates is an important objective of many clinical trials. However, often the proportion is affected by covariates, and the adjustment of the predicted proportion is made using logistic regression. It is desirable to estimate the treatment differences in proportions adjusting for the covariates, similarly to the comparison of adjusted means in analysis of variance. Because of the correlation between the point estimates in the different treatment groups, the standard methods for constructing confidence intervals are inadequate. The problem is more difficult in the binary case, as the comparison is not uniquely defined, and the sampling distribution more difficult to analyze. Four procedures for analyzing the data are presented, which expand upon existing methods and generalize the link function. It is shown that, among the four methods studied, the resampling method based on the exact distribution function yields a coverage rate closest to the nominal.

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Bandyopadhyay, Dipankar, Diana M. Galvis, and Victor H. Lachos. "Augmented mixed models for clustered proportion data." Statistical Methods in Medical Research 26, no. 2 (2014): 880–97. http://dx.doi.org/10.1177/0962280214561093.

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Often in biomedical research, we deal with continuous (clustered) proportion responses ranging between zero and one quantifying the disease status of the cluster units. Interestingly, the study population might also consist of relatively disease-free as well as highly diseased subjects, contributing to proportion values in the interval [0, 1]. Regression on a variety of parametric densities with support lying in (0, 1), such as beta regression, can assess important covariate effects. However, they are deemed inappropriate due to the presence of zeros and/or ones. To evade this, we introduce a class of general proportion density, and further augment the probabilities of zero and one to this general proportion density, controlling for the clustering. Our approach is Bayesian and presents a computationally convenient framework amenable to available freeware. Bayesian case-deletion influence diagnostics based on q-divergence measures are automatic from the Markov chain Monte Carlo output. The methodology is illustrated using both simulation studies and application to a real dataset from a clinical periodontology study.

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Santos, Bruno, and Heleno Bolfarine. "Bayesian analysis for zero-or-one inflated proportion data using quantile regression." Journal of Statistical Computation and Simulation 85, no. 17 (2014): 3579–93. http://dx.doi.org/10.1080/00949655.2014.986733.

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Yudistira, Yudistira, Anang Kurnia, and Agus Mohamad Soleh. "BINOMIAL REGRESSION IN SMALL AREA ESTIMATION METHOD FOR ESTIMATE PROPORTION OF CULTURAL INDICATOR." Indonesian Journal of Statistics and Its Applications 2, no. 2 (2018): 56–62. http://dx.doi.org/10.29244/ijsa.v2i2.63.

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In sampling survey, it was necessary to have sufficient sample size in order to get accurate direct estimator about parameter, but there are many difficulties to fulfill them in practice. Small Area Estimation (SAE) is one of alternative methods to estimate parameter when sample size is not adequate. This method has been widely applied in such variation of model and many fields of research. Our research mainly focused on study how SAE method with binomial regression model is applied to obtained estimate proportion of cultural indicator, especially to estimate proportion of people who appreciate heritages and museums in each regency/city level in West Java Province. Data analysis approach used in our research with resurrected data and variables in order to be compared with previous research. The result later showed that binomial regression model could be used to estimate proportion of cultural indicator in Regency/City in Indonesia with better result than direct estimation method.

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Zhang, Fengjuan, Xiaohui Zhang, Zhilei Xu, Keliang Dong, Zhiwei Li, and Yubo Liu. "Cleaning of Abnormal Wind Speed Power Data Based on Quartile RANSAC Regression." Energies 17, no. 22 (2024): 5697. http://dx.doi.org/10.3390/en17225697.

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The combined complexity of wind turbine systems and harsh operating conditions pose significant challenges to the accuracy of operational data in Supervisory Control and Data Acquisition (SCADA) systems. Improving the precision of data cleaning for high proportions of stacked abnormalities remains an urgent problem. This paper deeply analyzes the distribution characteristics of abnormal data and proposes a novel method for abnormal data cleaning based on a classification processing framework. Firstly, the first type of abnormal data is cleaned based on operational criteria; secondly, the quartile method is used to eliminate sparse abnormal data to obtain a clearer boundary line; on this basis, the Random Sample Consensus (RANSAC) algorithm is employed to eliminate stacked abnormal data; finally, the effectiveness of the proposed algorithm in cleaning abnormal data with a high proportion of stacked abnormalities is verified through case studies, and evaluation indicators are introduced through comparative experiments to quantitatively assess the cleaning effect. The research results indicate that the algorithm excels in cleaning effectiveness, efficiency, accuracy, and rationality of data deletion. The cleaning accuracy improvement is particularly significant when dealing with a high proportion of stacked anomaly data, thereby bringing significant value to wind power applications such as wind power prediction, condition assessment, and fault detection.

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Gumpertz, Marcia L., Chi-tsung Wu, and John M. Pye. "Logistic Regression for Southern Pine Beetle Outbreaks with Spatial and Temporal Autocorrelation." Forest Science 46, no. 1 (2000): 95–107. http://dx.doi.org/10.1093/forestscience/46.1.95.

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Abstract Regional outbreaks of southern pine beetle (Dendroctonus frontalis Zimm.) show marked spatial and temporal patterns. While these patterns are of interest in themselves, we focus on statistical methods for estimating the effects of underlying environmental factors in the presence of spatial and temporal autocorrelation. The most comprehensive available information on outbreaks consists of binary data, specifically, annual presence or absence of outbreak for individual counties within the southern United States. We demonstrate a method for modeling spatially correlated proportions, such as the proportion of years that a county experiences outbreak, based on annual outbreak presence or absence data for counties in three states (NC, SC, and GA) over 31 yr. In this method, the proportion of years in outbreak is predicted using a marginal logistic regression model with spatial autocorrelation among counties, with adjustment of variance terms to account for temporal autocorrelation. This type of model describes the probability of outbreak as a function of explanatory variables such as host availability, physiography, climate, hurricane incidence, and management type. Explicitly including spatial autocorrelation in the model improves estimates of the probability of outbreak for a particular county and of the importance of the various explanatory variables. For. Sci. 46(1):95-107.

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Barclay, H. J., P. C. Pang, and D. F. W. Pollard. "Aboveground biomass distribution within trees and stands in thinned and fertilized Douglas-fir." Canadian Journal of Forest Research 16, no. 3 (1986): 438–42. http://dx.doi.org/10.1139/x86-080.

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Nine years after thinning (removal of 2/3 of the basal area) and fertilization (at 448 kg N ha−1, applied as urea), 34-year-old Douglas-fir trees (Pseudotsugamenziesii (Mirb.) Franco) were destructively sampled. The dry weights of seven aboveground components were determined and regression equations from dbh were developed. Differences among treatments were shown for all biomass components and the proportions of the total biomass allocated to the various components. Specifically, thinning decreased the proportion of biomass allotted to wood, bark, and dead branches, while increasing the proportions in foliage and live branches; fertilization increased the proportion of biomass in branches, but had negligible effects on the proportions of other components.

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Tyler, Jeffrey A. "Effects of Water Velocity, Group Size, and Prey Availability on the Stream-Drift Capture Efficiency of Blacknose Dace, Rhinicthys atratulus." Canadian Journal of Fisheries and Aquatic Sciences 50, no. 5 (1993): 1055–61. http://dx.doi.org/10.1139/f93-122.

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In laboratory flow-tank experiments, I determined the effects of water velocity, group size, and prey arrival rate on the proportion of drifting prey a group of blacknose dace, Rhinicthys atratulus, captured. Water velocity, group size, and the interaction between the two accounted for significant proportions of the variance found in the stream-drift capture efficiency of the fish. Neither prey arrival rate nor any of the interactions which included prey arrival rate explained a significant proportion of the variance in the capture data. I present a regression relating water velocity and group size to the proportion of drift items fish capture that should be valuable for future studies offish habitat selection. Further analysis of the regression found an optimal water velocity for drift-feeding blacknose dace at between 24 and 27 cm∙s−1. Because of the significant interaction between water velocity and group size, the advantage of foraging in sites with optimal water velocity decreased as the number of intraspecific competitors increased.

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Sihombing, Pardomuan Robinson. "Comparison Of Normal-Based and Beta-Based Regression Models on Ratio/ Proportion Data." Jurnal Ekonomi Dan Statistik Indonesia 2, no. 1 (2022): 19–23. http://dx.doi.org/10.11594/jesi.02.01.03.

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This study compares the regression using the assumption of a normal distribution with a beta distribution on ratio/proportion data. The data used is the Gini ratio data as the dependent variable and the percentage of the poor, economic growth and unemployment as independent variables in 2021. The data used is sourced from the Central Statistics Agency. The criteria for selecting the best model are based on the smallest AIC and BIC criteria. The results obtained by the beta regression model are better than the model based on the normal distribution. This result is reflected by the probability value of the model suitability test and the error value which the smaller AIC and BIC reflect. The poverty variable has a significant effect on the Gini ratio. On the other hand, there is not enough evidence that the variables of economic growth and open unemployment affect the Gini ratio. From the results obtained, it is hoped that the government will be able to implement appropriate policies in overcoming inequality so that every level of society can feel welfare without exception.

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Ramakrishnaiah, Y. S., Manish Trivedi, and Konda Satish. "ON THE SMOOTHED PARAMETRIC ESTIMATION OF MIXING PROPORTION UNDER FIXED DESIGN REGRESSION MODEL." Statistics in Transition New Series 20, no. 1 (2019): 87–102. http://dx.doi.org/10.21307/stattrans-2019-005.

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Liu, Pengyi, Kam Chuen Yuen, Liu-Cang Wu, Guo-Liang Tian, and Tao Li. "Zero-one-inflated simplex regression models for the analysis of continuous proportion data." Statistics and Its Interface 13, no. 2 (2020): 193–208. http://dx.doi.org/10.4310/sii.2020.v13.n2.a5.

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Westfall, James A. "Prediction of standing tree defect proportion using logistic regression and ordered decision thresholds." Canadian Journal of Forest Research 43, no. 12 (2013): 1085–91. http://dx.doi.org/10.1139/cjfr-2013-0330.

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In forest inventories, it is often of interest to calculate amounts of usable wood volume in trees. This usually requires knowledge of how much of the total volume is unusable (cull) due to form or decay deficiencies. This information is primarily obtained when collecting data on sample plots, although the assessments are often difficult and subjective. To provide an alternative, methods were developed to estimate individual-tree cull attributes. The procedure initially involves classification using logistic regression to assign trees to one of three categories (no cull, intermediate cull, entirely cull) based on probability cut points. Subsequently, trees classified as having intermediate cull are assigned a cull amount predicted from a generalized linear regression model. The best results for cull prediction were obtained using cut points that minimized absolute prediction error; however, better prediction of net cubic volume of trees was realized when the errors were weighted by tree size. The model-based approach may be particularly useful in obtaining temporal consistency, such that trend estimates may better reflect the actual change in forest resources.

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Poudel, K. P., and H. Temesgen. "Methods for estimating aboveground biomass and its components for Douglas-fir and lodgepole pine trees." Canadian Journal of Forest Research 46, no. 1 (2016): 77–87. http://dx.doi.org/10.1139/cjfr-2015-0256.

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Estimating aboveground biomass and its components requires sound statistical formulation and evaluation. Using data collected from 55 destructively sampled trees in different parts of Oregon, we evaluated the performance of three groups of methods to estimate total aboveground biomass and (or) its components based on the bias and root mean squared error (RMSE) that they produced. The first group of methods used an analytical approach to estimate total and component biomass using existing equations and produced biased estimates for our dataset. The second group of methods used a system of equations fitted with seemingly unrelated regression (SUR) and were superior to the first group of methods in terms of bias and RMSE. The third group of methods predicted the proportion of biomass in each component using beta regression, Dirichlet regression, and multinomial log-linear regression. The predicted proportions were then applied to the total aboveground biomass to obtain the amount of biomass in each component. The multinomial log-linear regression approach consistently produced smaller RMSEs compared with both SUR methods. The beta and Dirichlet regressions were superior to both SUR methods except for Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) branch biomass, for which the simple SUR method produced smaller RMSE compared with the beta and Dirichlet regressions.

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Ribeiro, Tatiane Fontana, Fernando A. Peña-Ramírez, Renata Rojas Guerra, and Gauss M. Cordeiro. "Another unit Burr XII quantile regression model based on the different reparameterization applied to dropout in Brazilian undergraduate courses." PLOS ONE 17, no. 11 (2022): e0276695. http://dx.doi.org/10.1371/journal.pone.0276695.

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In many practical situations, there is an interest in modeling bounded random variables in the interval (0, 1), such as rates, proportions, and indexes. It is important to provide new continuous models to deal with the uncertainty involved by variables of this type. This paper proposes a new quantile regression model based on an alternative parameterization of the unit Burr XII (UBXII) distribution. For the UBXII distribution and its associated regression, we obtain score functions and observed information matrices. We use the maximum likelihood method to estimate the parameters of the regression model, and conduct a Monte Carlo study to evaluate the performance of its estimates in samples of finite size. Furthermore, we present general diagnostic analysis and model selection techniques for the regression model. We empirically show its importance and flexibility through an application to an actual data set, in which the dropout proportion of Brazilian undergraduate animal sciences courses is analyzed. We use a statistical learning method for comparing the proposed model with the beta, Kumaraswamy, and unit-Weibull regressions. The results show that the UBXII regression provides the best fit and the most accurate predictions. Therefore, it is a valuable alternative and competitive to the well-known regressions for modeling double-bounded variables in the unit interval.

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Addini, Fida Fathiyah. "REGRESSION ANALYSIS: RELATIONSHIP BETWEEN COVID-19 VACCINATION AND POSITIVITY RATE IN INDONESIA." Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika 4, no. 2 (2023): 787–97. http://dx.doi.org/10.46306/lb.v4i2.335.

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Covid-19 has become a global epidemic since it first emerged in late 2019. Indonesia has made various efforts to prevent Covid-19, both through government policies and mass vaccination. Vaccine availability in mass vaccination policies is not sufficient because vaccines must be well accepted by the general public. One way to gain public trust is to make the impact of vaccination transparent by modeling its impact on the proportion of Covid-19 positive cases. In this study, we performed modeling using two main data sets. The first data is the number of people who received the first or second vaccine between July 1, 2021 and January 1, 2022. The second data is the positive rate (%) from July 15, 2021 to January 15, 2022. The results of this study show that increasing the proportion of the population vaccinated against Covid-19 with both 1st and 2nd doses can reduce the prevalence of Covid-19 cases in Indonesia. Moreover, increasing the proportion of second vaccinations further reduces the prevalence of Covid-19 cases compared to increasing the proportion of first vaccinations

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Piyatadsananon, Pantip, and Ekkaluk Salakkham. "URBAN GREEN SPACE ESTIMATION FOR HEAT HOTSPOT MITIGATION IN A SMALL CITY, BURIRAM MUNICIPALITY, THAILAND." Suranaree Journal of Science and Technology 30, no. 4 (2023): 010251(1–13). http://dx.doi.org/10.55766/sujst-2023-04-e01487.

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Heat hotspots cause environmental and health problems to residents in urban areas. Green space has been used to reduce the temperature in urban areas. However, the size and location of the green space that can reduce the temperature in those areas are challenging. This study aims to identify the heat hotspot of an urban area and estimate the green space proportion to reduce the heat hotspot. Therefore, the Split Window method (SW) was initially employed to calculate the Land Surface Temperature (LST) data from the Landsat series in the summertime of 2014, 2016, and 2018. The LST data 2018 were used in heat hotspot investigation using Moran's I and Getis-Ord Gi*. The results show the clustering patterns of LST occurring in barren lands, racetracks, and built-up areas in Buriram Municipality. Then, the monthly regression modeling between the green space proportions and LST was analyzed and applied to the hotspot areas. The green space proportions were represented by estimating in regression models showing the ratio of green space and decreasing temperature in hotspot areas. As a result, the green space proportion around 45% of the area is suggested to mitigate the heat hotspot. The explored green space proportion was applied to the 2014 and 2016 data to assess the feasibility of hotspot mitigation. This research presents a simplified technic that will enable urban planners to estimate the green space proportion to reduce the heat hotspots effectively.

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Chen, Ying, Yiya Yang, Yumei Liang, Manting Liu, Wei Xiao, and Xiaofang Hu. "Retrospective analysis of crescent score in clinical prognosis of IgA nephropathy." Open Medicine 17, no. 1 (2022): 205–15. http://dx.doi.org/10.1515/med-2022-0414.

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Abstract The scoring of crescents (Cs) was recently added to the Oxford classification for IgA nephropathy (IgAN). Because of the short-term use of the C score in clinical practice, its validity and applicability need to be verified. We, retrospectively, analyzed the clinicopathological data of 144 primary IgAN patients diagnosed at our hospital from March 2017 to March 2019 and with complete ≥6-month follow-up data. We found that the C score was positively correlated with the Lee’s classification in the assessment of renal pathological changes and significantly correlated with increased proteinuria and decreased estimated glomerular filtration rate. Univariate Cox regression analysis showed an association of C formation with IgAN prognosis, and multivariate Cox regression indicated Cs as an independent prognosis factor. The optimal proportion of Cs for prognosis prediction by the receiver operating characteristic curve was 11%. Kaplan–Meier survival curve revealed a significantly decreased renal survival rate in patients with C proportions ≥11%. Further multivariate Cox regression analysis confirmed that the C proportion ≥11% is an independent risk factor for poor prognosis of IgAN patients. Our findings demonstrate that Cs are independently related to the prognosis of patients with IgAN, and the proportion of Cs ≥11% is an independent risk factor for poor outcomes.

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Peng, Ziyan. "Study on the influencing factors of the STR based on linear regression-take China as example." Theoretical and Natural Science 39, no. 1 (2024): 60–67. http://dx.doi.org/10.54254/2753-8818/39/20240563.

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In recent years, the student-teacher ratio (STR) has received widespread attention in the field of education. By now, the STR has become an important indicator of education. This article first theoretically analyzes the relationship between the STR and the proportion of the school-age population in the middle school stage to the total population of the country, teacher gender ratio, the proportion of education expenditure in GDP, and GDP per capita, predicting that there is a positive correlation between STR and the proportion of school-age population in the total population, and there is a negative correlation between STR and the rate of women teacher, the proportion of education funds in GDP, and GDP per capita. Then this article establishes the regression equation of STR and the above variables through the data of secondary schools in China in the past 30 years. And it finds that there is a positive correlation between STR and the proportion of the school-age population in the middle school stage to the total population of the country, and there is a negative correlation between STR and the rate of women teachers, and GDP per capita, and there is no linear relation between STR and the proportion of education funds in GDP. Also, this article finds that proportion of the proportion of the school-age population in the middle school stage to the total population of the country, rate of women teachers and GDP per capita have collinearity relationship.

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Ahmed, Naseer, Mohamad Syahrizal Halim, Gotam Das, et al. "Analysis of Lower Facial Third and Dental Proportions to Predict Maxillary Anterior Teeth Width in the Pakistani Population." Symmetry 14, no. 4 (2022): 723. http://dx.doi.org/10.3390/sym14040723.

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Obtaining the size of the maxillary anterior teeth when performing an esthetic smile rehabilitation can be a difficult task. Metrics based on dental proportions to assist in the process are required. This study aimed to evaluate the lower facial third proportions i.e., the interalar, interphiltral, and intercommisural distance with dental proportions in predicting maxillary anterior teeth width in Pakistani citizens. This analytical study was conducted on 230 participants. Front face and retracted smile photographs were captured for all the participants, followed by maxillary impression making. The cast was then converted to 3D models for analysis. The data were entered into SPSS-25. Descriptive statistics were carried out for frequency, mean, standard deviation, and percentage calculation of gender, teeth widths, horizontal mid facial proportions, and age of the participants. Independent t-test was applied for analysis of gender and arch side disparity. Regression analyses were performed to analyze the relationship between independent variables (gender, age, weight, and height) and dependent variables (horizontal facial proportion, dental proportion). A p-value of ≤0.05 was considered statistically significant. The interphiltral distance (IPLD) modified with Preston proportion (PRP) showed no significant difference with combined central incisor width, whereas a significant difference was found with golden proportion (GP), 70% recurrent esthetic dental (RED) proportion, and golden percentage (GM) modification. However, the interalar (IAD) and intercommisural distance (ICoD) modified with dental proportions showed a significant difference with maxillary anterior teeth width. The width of maxillary anterior teeth determined by plaster dental cast and 3D dental cast showed no significant difference. The ICoD, IAD, and IPLD could not be used to determine combined central incisor and intercanine width directly. The interphiltral distance modified with Preston proportion is a reliable method to predict combined central incisor width in the population studied. There was a significant difference in gender disparity when ICD, IAD, and IPLD were modified with dental proportions, except in the case of IPLD by the Preston proportion group. The golden proportion, 70% RED proportion, and golden percentage by lower facial third facial proportions are not reliable methods to predict maxillary anterior teeth width.

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Li, Xiaoqin, Xiao Yang, Zude Ding, Xi Du, and Jincheng Wen. "ECC Design Based on Uniform Design Test Method and Alternating Conditional Expectation." Mathematical Problems in Engineering 2019 (October 1, 2019): 1–14. http://dx.doi.org/10.1155/2019/9575897.

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Engineered cementitious composites (ECC) have higher ultimate tensile strains than normal concrete. The mechanical properties of ECC strongly depend on raw materials and the mix proportions. The uniform design test method and alternating conditional expectation, which is a nonparametric regression analysis method, were used to design the ECC mix proportion. According to the regression analysis, the optimized W/B, S/B, and F/B ranges could be obtained as 0.35–0.42, 0.25–0.3, and 0.02, respectively. The tested proportions for validation were randomly adopted within the range of W/B, S/B, and F/B. The uniaxial compression, tension, and four-point bending tests were conducted to verify the material behaviour of the designed ECC. Results showed that all the specimens had large ultimate tensile strains and high fracture energy capacities, and strain hardening was also observed. The fibers were found to be uniformly distributed in the specimens by using a scanning electron microscope. This paper may provide theoretical and practical guidance for the ECC and other cement-based material mix proportion design.

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Sijabat, Yacobo P., Heni Hirawati, and Deni Ramdani. "The influence of the proportion of foreign ownership and financial ratio on banking performance in Indonesia in the period of 2017-2019." Management Journal of Binaniaga 5, no. 02 (2020): 157. http://dx.doi.org/10.33062/mjb.v5i2.393.

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This research examined the influence of foreign proportions in the shares of banking companies and financial ratios on the performance of companies listed on the Indonesia Stock Exchange in the period 2014-2018.The proportion of foreigners in a banking company uses a comparison of the total amount of foreign shares with the total share ownership.While the proxies of financial ratios are measured using several ratios, including Non-Performing Loans (NPL), Loan to Deposit (LDR), Capital Adequacy Ratio (CAR) and BOPO.Performance proxies are measured using ROA and ROE proxies. The method used is data panel regression using stata 13.0 software.The results of research on the proportion of foreign to the company's performance have a significant effect on ROA alone.Keywords: Banking Company, Financial Ratios, Proportion of Foreign Ownership

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Wang, Dewei, Haiming Zhou, and K. B. Kulasekera. "A semi-local likelihood regression estimator of the proportion based on group testing data." Journal of Nonparametric Statistics 25, no. 1 (2013): 209–21. http://dx.doi.org/10.1080/10485252.2012.750726.

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Park, Min-Gue. "Model-Based Prediction of the Population Proportion and Distribution Function Using a Logistic Regression." Communications for Statistical Applications and Methods 15, no. 5 (2008): 783–91. http://dx.doi.org/10.5351/ckss.2008.15.5.783.

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Sutton, S. Scott, Joseph Magagnoli, Tammy H. Cummings, and James W. Hardin. "Odds of Achieving Target Serum Uric Acid Levels Among Gout Patients: The Role of Rurality in Outcomes and Treatment Adherence." Journal of Primary Care & Community Health 14 (January 2023): 215013192311673. http://dx.doi.org/10.1177/21501319231167379.

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Objective: The goal of this paper was to analyze patient outcomes related to gout treatment including, serum uric acid (sUA) measures and treatment adherence across patients in metropolitan, micropolitan or rural counties. Methods: We conducted a drug-disease cohort study among patients with gout initiating urate lowering therapy. The proportion of patients with sUA < 6 mg/dL at 1 year of follow-up is compared over the cohort groups using a chi-square test and adjusted logistic regression. Adherence to urate lowering therapy was calculated using the proportion of days covered (PDC). A T-test was used to compare the average PDC and an adjusted logistic regression model was used to estimate the odds of a PDC greater than 80%. Results: A total of 9922 patients were included in the study. Most patients were in a metropolitan (77.4%) area, followed by micropolitan (11.8%) and finally, (10.8%) in a rural area. We found no statistically significant difference among the proportion of patients achieving target sUA of <6 mg/dL, 37.17% among metropolitan patients, 38.9% among micropolitan patients, and 37.7% for those in a rural area, P-value = .502. The proportion of patients achieving 80% treatment adherence was 49.92% in the metropolitan, 51.78% in the micropolitan, and 55.05% in the rural areas, P-value = .005. Adjusted regression models showed no statistically significant difference in proportions achieving target sUA levels or 80% adherence. Conclusions: Urban patients treated for gout did not have better gout outcomes compared to rural patients. Future research should consider provider-based interventions to improve outcomes.

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Ai, Dongmei, Gang Liu, Xiaoxin Li, Yuduo Wang, and Man Guo. "Calculation of immune cell proportion from batch tumor gene expression profile based on support vector regression." Journal of Bioinformatics and Computational Biology 18, no. 05 (2020): 2050030. http://dx.doi.org/10.1142/s0219720020500304.

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In addition to tumor cells, a large number of immune cells are found in the tumor microenvironment (TME) of cancer patients. Tumor-infiltrating immune cells play an important role in tumor progression and patient outcome. We improved the relative proportion estimation algorithm of immune cells based on RNA-seq gene expression profiling and solved the multiple linear regression model by support vector regression ([Formula: see text]-SVR). These steps resulted in increased robustness of the algorithm and more accurate calculation of the relative proportion of different immune cells in cancer tissues. This method was applied to the analysis of infiltrating immune cells based on 41 pairs of colorectal cancer tissues and normal solid tissues. Specifically, we compared the relative fractions of six types of immune cells in colorectal cancer tissues to those found in normal solid tissue samples. We found that tumor tissues contained a higher proportion of CD8 T cells and neutrophils, while B cells and monocytes were relatively low. Our pipeline for calculating immune cell proportion using gene expression profile data can be freely accessed from GitHub at https://github.com/gutmicrobes/EICS.git.

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Ran, Jianguo, Heng Liu, and Jiqing Luo. "The color matching design based on polynomial regression." Textile Research Journal 92, no. 7-8 (2021): 1235–45. http://dx.doi.org/10.1177/00405175211054222.

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Camouflage spots are mostly color patches of different colors. Compared with the traditional empirical color matching method, computer color matching can quickly and effectively calculate the proportion of red, yellow, and blue primary colors. In general, camouflage computer color matching in the practical application of the initial formula is not accurate and multiple color modification still cannot match. First, we measured the chroma coordinates of 98 standard swatches made of red, green, and blue coatings in different proportions by the OHSP-660 spectral reflectance tester. Then, we adopted polynomial regression algorithm by Statistical Product and Service Solutions (SPSS) to obtain the regression equation between [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]. Finally, the fitting equation is used to obtain the paint color scheme in the coordinates of [Formula: see text], [Formula: see text], and [Formula: see text] in arbitrary uniform color space, so as to realize the purpose of computer color matching. The experimental results show that the minimum color difference is 1.691 ( L* a* b* unit) the maximum color difference is 2.497, which meets the design requirements of camouflage and target color difference being no more than 3. Our work provides a theoretical basis for the further development of computer color matching system in camouflage.

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Lord, Jennifer, Shamarial Roberson, and Agricola Odoi. "A retrospective investigation of spatial clusters and determinants of diabetes prevalence: scan statistics and geographically weighted regression modeling approaches." PeerJ 11 (April 20, 2023): e15107. http://dx.doi.org/10.7717/peerj.15107.

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Background Diabetes and its complications represent a significant public health burden in the United States. Some communities have disproportionately high risks of the disease. Identification of these disparities is critical for guiding policy and control efforts to reduce/eliminate the inequities and improve population health. Thus, the objectives of this study were to investigate geographic high-prevalence clusters, temporal changes, and predictors of diabetes prevalence in Florida. Methods Behavioral Risk Factor Surveillance System data for 2013 and 2016 were provided by the Florida Department of Health. Tests for equality of proportions were used to identify counties with significant changes in the prevalence of diabetes between 2013 and 2016. The Simes method was used to adjust for multiple comparisons. Significant spatial clusters of counties with high diabetes prevalence were identified using Tango’s flexible spatial scan statistic. A global multivariable regression model was fit to identify predictors of diabetes prevalence. A geographically weighted regression model was fit to assess for spatial non-stationarity of the regression coefficients and fit a local model. Results There was a small but significant increase in the prevalence of diabetes in Florida (10.1% in 2013 to 10.4% in 2016), and statistically significant increases in prevalence occurred in 61% (41/67) of counties in the state. Significant, high-prevalence clusters of diabetes were identified. Counties with a high burden of the condition tended to have high proportions of the population that were non-Hispanic Black, had limited access to healthy foods, were unemployed, physically inactive, and had arthritis. Significant non-stationarity of regression coefficients was observed for the following variables: proportion of the population physically inactive, proportion with limited access to healthy foods, proportion unemployed, and proportion with arthritis. However, density of fitness and recreational facilities had a confounding effect on the association between diabetes prevalence and levels of unemployment, physical inactivity, and arthritis. Inclusion of this variable decreased the strength of these relationships in the global model, and reduced the number of counties with statistically significant associations in the local model. Conclusions The persistent geographic disparities of diabetes prevalence and temporal increases identified in this study are concerning. There is evidence that the impacts of the determinants on diabetes risk vary by geographical location. This implies that a one-size-fits-all approach to disease control/prevention would be inadequate to curb the problem. Therefore, health programs will need to use evidence-based approaches to guide health programs and resource allocation to reduce disparities and improve population health.

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Fitri, Fadhilah, Melin Wanike Ketrin, and Mawanda Almuhayar. "MODELING TOTAL FERTILITY RATE IN INDONESIA: A COMPARISON OF FOURIER SERIES REGRESSION AND ELASTIC NET REGRESSION." BAREKENG: Jurnal Ilmu Matematika dan Terapan 19, no. 3 (2025): 2017–28. https://doi.org/10.30598/barekengvol19iss3pp2017-2028.

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The Total Fertility Rate (TFR) describes population growth and socioeconomic development of a country. This statistic plays an important role in predicting future social and economic conditions. Indonesia has experienced a steady decline in TFR over the past few decades, which can be a serious problem if this trend continues. Therefore, the factor influencing the decline must be found. The independent variables include the percentage of women graduating high school, percentage of the poor population, poverty gap index, poverty severity index, prevalence of inadequate food consumption, proportion of people living below 50 percent of median income, unemployment rate, infant mortality rate, child mortality rate, and percentage of ever-married women aged 15–49 years using contraception methods. The aim of this study is to compare both Fourier Series Regression and Elastic Net Regression models to see which approximation can capture the TRF phenomenon that occurs in Indonesia and identify the causes of its decline. Fourier Regression is chosen because there is a repetition of patterns in several variables. Moreover, this data is experiencing multicollinearity; hence, Elastic-net Regression is the best way because this method overcomes the limitations of each Ridge and Lasso approach. These models are compared to see which is more suitable to capture the relationships between these factors and TFR. The best model obtained will provide a clearer understanding of Indonesia's underlying drivers of fertility decline. The result is that the Fourier Series Regression can model all variables better than the Elastic-net Regression, and the independent variables can explain the proportion of variance in the dependent variables by 97.91%, with all the independent variables significantly affecting the Total Fertility Rate.

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Wu, Long, Yan Hua Hu, and Xue Tao Qiao. "Study for Seeking Optimal Mix Proportion of Resin Concrete." Advanced Materials Research 311-313 (August 2011): 1689–94. http://dx.doi.org/10.4028/www.scientific.net/amr.311-313.1689.

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In the study, uniform design method is used to define the mix proportion of resin concrete. According to results obtained from the experiment, the optimal mix proportion of resin concrete can be got through regression analysis by using SPSS software, and MATLAB software together. The methods not only reduce the number of tests and the test costs greatly, but also provide an accurate and reliable design concept for the application of resin concrete in practical engineering.

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Chen, Peili, Lili Chen, Xiaolong Zhao, and Quanya Sun. "The Association of Mean Plasma Glucose and In hospital Death Proportion: A Retrospective, Cohort Study of 162,169 In-Patient Data." International Journal of Endocrinology 2021 (January 12, 2021): 1–7. http://dx.doi.org/10.1155/2021/1513683.

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Aims. To investigate the association between mean plasma glucose and inhospital death proportion. Methods. We retrospectively collected 162,169 inpatient data in Huashan Hospital from January 2012 to December 2015. Mean plasma glucose was calculated and considered as the average glycemia control during hospitalization. Patients were stratified into six groups according to mean plasma glucose. Nonlinear regression was performed to determine the associations between mean plasma glucose and inhospital death proportion, medical cost, and length of stay. Multivariate logistic regressions were performed to evaluate the relationship of mean plasma glucose and outcomes controlling for confounders including age, gender, and others. Subgroup analyses were performed on basis of whether they were surgical patients, ICU patients, patients with diabetes, or others. Results. Of the 162,169 hospitalized participants, 53.32% were male and 989 died during hospitalization. Nonlinear regression showed there were positive and significant associations between mean plasma glucose and death proportion, medical cost, and length of stay ( P < 0.001 for all). Multivariate logistic regressions showed that, compared with group B, a statistically significant association between mean plasma glucose and predicted outcome was apparent, with the odds ratios (95% confidence interval) of 5.79 (3.51–9.55), 2.85 (2.40–3.38), 6.29 (5.24–7.54), 9.34 (7.51–11.62), and 23.52 (16.64–33.26), for group A, group C, group D, group E, and group F, respectively. There was a U-shaped association between mean plasma glucose and death proportion. Subgroup analyses showed similar associations between mean plasma glucose and death proportion, medical cost, and length of stay as in the whole sample. Conclusions. There was a U-curve association between mean plasma glucose with inhospital death proportion. Mean plasma glucose was associated positively with medical cost and length of stay.

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Silva Junior, Sérgio Henrique Almeida da, Simone M. Santos, Cláudia Medina Coeli, and Marilia Sá Carvalho. "Assessment of participation bias in cohort studies: systematic review and meta-regression analysis." Cadernos de Saúde Pública 31, no. 11 (2015): 2259–74. http://dx.doi.org/10.1590/0102-311x00133814.

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Abstract The proportion of non-participation in cohort studies, if associated with both the exposure and the probability of occurrence of the event, can introduce bias in the estimates of interest. The aim of this study is to evaluate the impact of participation and its characteristics in longitudinal studies. A systematic review (MEDLINE, Scopus and Web of Science) for articles describing the proportion of participation in the baseline of cohort studies was performed. Among the 2,964 initially identified, 50 were selected. The average proportion of participation was 64.7%. Using a meta-regression model with mixed effects, only age, year of baseline contact and study region (borderline) were associated with participation. Considering the decrease in participation in recent years, and the cost of cohort studies, it is essential to gather information to assess the potential for non-participation, before committing resources. Finally, journals should require the presentation of this information in the papers.

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