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Journal articles on the topic 'Median regression, quantile regression'

Author: Grafiati

Published: 9 March 2023

Last updated: 10 March 2023

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1

Koenker, Roger, and Kevin F. Hallock. "Quantile Regression." Journal of Economic Perspectives 15, no. 4 (2001): 143–56. http://dx.doi.org/10.1257/jep.15.4.143.

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Quantile regression, as introduced by Koenker and Bassett (1978), may be viewed as an extension of classical least squares estimation of conditional mean models to the estimation of an ensemble of models for several conditional quantile functions. The central special case is the median regression estimator which minimizes a sum of absolute errors. Other conditional quantile functions are estimated by minimizing an asymmetrically weighted sum of absolute errors. Quantile regression methods are illustrated with applications to models for CEO pay, food expenditure, and infant birthweight.

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Aviral, Kumar Tiwari, and Krishnankutty Raveesh. "Determinants of Capital Structure: A Quantile Regression Analysis." Studies in Business and Economics 10, no. 1 (2015): 16–34. http://dx.doi.org/10.1515/sbe-2015-0002.

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Abstract In this study, we attempted to analyze the determinants of capital structure for Indian firms using a panel framework and to investigate whether the capital structure models derived from Western settings provide convincing explanations for capital structure decisions of the Indian firms. The investigation is performed using balanced panel data procedures for a sample 298 firms (from the BSE 500 firms based on the availability of data) during 2001-2010. We found that for lowest quantile LnSales and TANGIT are significant with positive sign and NDTS and PROFIT are significant with negat

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CAI, YUZHI. "A COMPARATIVE STUDY OF MONOTONE QUANTILE REGRESSION METHODS FOR FINANCIAL RETURNS." International Journal of Theoretical and Applied Finance 19, no. 03 (2016): 1650016. http://dx.doi.org/10.1142/s0219024916500163.

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Quantile regression methods have been used widely in finance to alleviate estimation problems related to the impact of outliers and the fat-tailed error distribution of financial returns. However, a potential problem with the conventional quantile regression method is that the estimated conditional quantiles may cross over, leading to a failure of the analysis. It is noticed that the crossing over issues usually occur at high or low quantile levels, which are the quantile levels of great interest when analyzing financial returns. Several methods have appeared in the literature to tackle this p

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Chiu, Yohann Moanahere, Fateh Chebana, Belkacem Abdous, Diane Bélanger, and Pierre Gosselin. "Cardiovascular Health Peaks and Meteorological Conditions: A Quantile Regression Approach." International Journal of Environmental Research and Public Health 18, no. 24 (2021): 13277. http://dx.doi.org/10.3390/ijerph182413277.

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Cardiovascular morbidity and mortality are influenced by meteorological conditions, such as temperature or snowfall. Relationships between cardiovascular health and meteorological conditions are usually studied based on specific meteorological events or means. However, those studies bring little to no insight into health peaks and unusual events far from the mean, such as a day with an unusually high number of hospitalizations. Health peaks represent a heavy burden for the public health system; they are, however, usually studied specifically when they occur (e.g., the European 2003 heatwave).

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UTHAMI, IDA AYU PRASETYA, I. KOMANG GDE SUKARSA, and I. PUTU EKA NILA KENCANA. "REGRESI KUANTIL MEDIAN UNTUK MENGATASI HETEROSKEDASTISITAS PADA ANALISIS REGRESI." E-Jurnal Matematika 2, no. 1 (2013): 6. http://dx.doi.org/10.24843/mtk.2013.v02.i01.p021.

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In regression analysis, the method used to estimate the parameters is Ordinary Least Squares (OLS). The principle of OLS is to minimize the sum of squares error. If any of the assumptions were not met, the results of the OLS estimates are no longer best, linear, and unbiased estimator (BLUE). One of the assumptions that must be met is the assumption about homoscedasticity, a condition in which the variance of the error is constant (same). Violation of the assumptions about homoscedasticity is referred to heteroscedasticity. When there exists heteroscedasticity, other regression techniques are

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I. O., Ajao,, Obafemi, O. S., and Osunronbi, F.A. "MEASURING THE IMPACT OF TAU VECTOR ON PARAMETER ESTIMATES IN THE PRESENCE OF HETEROSCEDASTIC DATA IN QUANTILE REGRESSION ANALYSIS." INTERNATIONAL JOURNAL OF MATHEMATICS AND COMPUTER RESEARCH 11, no. 01 (2023): 3220–29. http://dx.doi.org/10.47191/ijmcr/v11i1.15.

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The ordinary least squares (OLS) regression models only the conditional mean of the response and is computationally less expensive. Quantile regression on the other hand is more expensive and rigorous but capable of handling vectors of quantiles and outliers. Quantile regression does not assume a particular parametric distribution for the response, nor does it assume a constant variance for the response, unlike least squares regression. This paper examines the impact of various quantiles (tau vector) on the parameter estimates in the models generated by the quantile regression analysis. Two da

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Conaway, Mark. "Reference data and quantile regression." Muscle & Nerve 40, no. 5 (2009): 751–52. http://dx.doi.org/10.1002/mus.21562.

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Pan, Wen-Tsao, and Yungho Leu. "An Analysis of Bank Service Satisfaction Based on Quantile Regression and Grey Relational Analysis." Mathematical Problems in Engineering 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/1475148.

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Bank service satisfaction is vital to the success of a bank. In this paper, we propose to use the grey relational analysis to gauge the levels of service satisfaction of the banks. With the grey relational analysis, we compared the effects of different variables on service satisfaction. We gave ranks to the banks according to their levels of service satisfaction. We further used the quantile regression model to find the variables that affected the satisfaction of a customer at a specific quantile of satisfaction level. The result of the quantile regression analysis provided a bank manager with

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Sánchez, Luis, Víctor Leiva, Helton Saulo, Carolina Marchant, and José M. Sarabia. "A New Quantile Regression Model and Its Diagnostic Analytics for a Weibull Distributed Response with Applications." Mathematics 9, no. 21 (2021): 2768. http://dx.doi.org/10.3390/math9212768.

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Standard regression models focus on the mean response based on covariates. Quantile regression describes the quantile for a response conditioned to values of covariates. The relevance of quantile regression is even greater when the response follows an asymmetrical distribution. This relevance is because the mean is not a good centrality measure to resume asymmetrically distributed data. In such a scenario, the median is a better measure of the central tendency. Quantile regression, which includes median modeling, is a better alternative to describe asymmetrically distributed data. The Weibull

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Olsen, Cody S., Amy E. Clark, Andrea M. Thomas, and Lawrence J. Cook. "Comparing Least-squares and Quantile Regression Approaches to Analyzing Median Hospital Charges." Academic Emergency Medicine 19, no. 7 (2012): 866–75. http://dx.doi.org/10.1111/j.1553-2712.2012.01388.x.

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Kong, Efang, and Yingcun Xia. "A SINGLE-INDEX QUANTILE REGRESSION MODEL AND ITS ESTIMATION." Econometric Theory 28, no. 4 (2012): 730–68. http://dx.doi.org/10.1017/s0266466611000788.

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Models with single-index structures are among the many existing popular semiparametric approaches for either the conditional mean or the conditional variance. This paper focuses on a single-index model for the conditional quantile. We propose an adaptive estimation procedure and an iterative algorithm which, under mild regularity conditions, is proved to converge with probability 1. The resulted estimator of the single-index parametric vector is root-n consistent, asymptotically normal, and based on simulation study, is more efficient than the average derivative method in Chaudhuri, Doksum, an

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Yang, Ming, Sheng Luo, and Stacia DeSantis. "Bayesian quantile regression joint models: Inference and dynamic predictions." Statistical Methods in Medical Research 28, no. 8 (2018): 2524–37. http://dx.doi.org/10.1177/0962280218784757.

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In the traditional joint models of a longitudinal and time-to-event outcome, a linear mixed model assuming normal random errors is used to model the longitudinal process. However, in many circumstances, the normality assumption is violated and the linear mixed model is not an appropriate sub-model in the joint models. In addition, as the linear mixed model models the conditional mean of the longitudinal outcome, it is not appropriate if clinical interest lies in making inference or prediction on median, lower, or upper ends of the longitudinal process. To this end, quantile regression provides

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CAHYANI, NI WAYAN YUNI, I. GUSTI AYU MADE SRINADI, and MADE SUSILAWATI. "PERBANDINGAN TRANSFORMASI BOX-COX DAN REGRESI KUANTIL MEDIAN DALAM MENGATASI HETEROSKEDASTISITAS." E-Jurnal Matematika 4, no. 1 (2015): 8. http://dx.doi.org/10.24843/mtk.2015.v04.i01.p081.

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Ordinary least square (OLS) is a method that can be used to estimate the parameter in linear regression analysis. There are some assumption which should be satisfied on OLS, one of this assumption is homoscedasticity, that is the variance of error is constant. If variance of the error is unequal that so-called heteroscedasticity. The presence heteroscedasticity can cause estimation with OLS becomes inefficient. Therefore, heteroscedasticity shall be overcome. There are some method that can used to overcome heteroscedasticity, two among those are Box-Cox power transformation and median quantile

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ANKARALI, Handan, Özge YILMAZ, Münevver KIZILAY, İlknur ARSLANOĞLU, and Duygu AYDIN. "The Use of Nonparametric Quantile Regression and Least Median of Squares Regression for Construction of Growth Curves of Weight." Turkiye Klinikleri Journal of Medical Sciences 33, no. 3 (2013): 692–701. http://dx.doi.org/10.5336/medsci.2012-30442.

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Pereira, Sara, Flávio Bastos, Carla Santos, et al. "Variation and Predictors of Gross Motor Coordination Development in Azorean Children: A Quantile Regression Approach." International Journal of Environmental Research and Public Health 19, no. 9 (2022): 5417. http://dx.doi.org/10.3390/ijerph19095417.

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We investigated the development of gross motor coordination (GMC) as well as its predictors in school-aged Azorean children. The sample included 181 children (90 girls), followed consecutively for 4 years from 6 to 9 years of age. GMC was assessed with the Körperkoordinationstest für Kinder, and predictors included body mass index, standing long jump, 50-yard dash, and shuttle run. The changes in GMC and the effects of predictors were analyzed with mean-modeling as well as quantile regression. In the latter, we considered the following three quantiles (Q): Q20, Q50, and Q80 as markers of low,

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Mohamad Yunus, Norhanishah, and Norehan Abdullah. "A Quantile Regression Analysis of Absorptive Capacity in the Malaysian Manufacturing Industry." Malaysian Journal of Economic Studies 59, no. 1 (2022): 153–70. http://dx.doi.org/10.22452/mjes.vol59no1.8.

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Using a set of absorptive capacity proxies, we present new empirical findings on the role of absorptive capacity in assimilating the technology effects from the presence of multinational corporations (MNCs) in the Malaysian manufacturing industry. We applied a quantile regression estimator to explicitly gauge the level of absorptive capacity among workers by their levels of education at different quantiles of the conditional FDI distribution during the period of 2000–2018. We conclude that the medium-high technology industries benefit more from FDI if the workers’ absorptive capacity level rea

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T, Nwakuya, M., and Nduka, E. C. "Investigating Performance of Composite Quantile Regression with and without Penalization." Scholars Journal of Physics, Mathematics and Statistics 9, no. 5 (2022): 85–91. http://dx.doi.org/10.36347/sjpms.2022.v09i05.002.

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The performance of composite quantile regression with and without penalization was investigated and results compared to Lasso Quantile Regression (LQR), Lasso Regression (LR) and Ridge Regression (RR). The mean square error, Akiake information criteria and mean absolute percentage error were used as the comparative criteria. The comparison was illustrated using real dataset and simulated data sets of sample sizes n=30,100,300,500 and 1000. Five consecutive quantiles; 0.19, 0.39, 0.59, 0.79 and 0.99 were used for the quantile regression methods while the lasso regression and ridge regression we

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Yang, Yunwen, Anne L. Adolph, Maurice R. Puyau, Firoz A. Vohra, Nancy F. Butte, and Issa F. Zakeri. "Modeling energy expenditure in children and adolescents using quantile regression." Journal of Applied Physiology 115, no. 2 (2013): 251–59. http://dx.doi.org/10.1152/japplphysiol.00295.2013.

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Advanced mathematical models have the potential to capture the complex metabolic and physiological processes that result in energy expenditure (EE). Study objective is to apply quantile regression (QR) to predict EE and determine quantile-dependent variation in covariate effects in nonobese and obese children. First, QR models will be developed to predict minute-by-minute awake EE at different quantile levels based on heart rate (HR) and physical activity (PA) accelerometry counts, and child characteristics of age, sex, weight, and height. Second, the QR models will be used to evaluate the cov

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Tang, Shaowu, Jong-Hyeon Jeong, and Chi Song. "Fractional logistic regression for censored survival data." Journal of Statistical Research 51, no. 2 (2018): 101–14. http://dx.doi.org/10.47302/jsr.2017510201.

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In the analysis of time-to-event data, e.g. from cancer studies, the group effect of main interest such as treatment effect of a chemo-therapy often needs to be adjusted by confounding factors (possibly continuous) such as hormonal receptor status, age at diagnosis, and pathological tumor size, when the study outcome is affected by their imbalanced distributions across the comparison groups. The median, or quantile, is a popular summary measure for censored survival data due to its robustness. In this paper, first the logistic regression is extended to fractional responses transformed from cen

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Teng, Xiaodong, Yanzhi Wang, Aiguo Wang, Bao-Guang Chang, and Kun-Shan Wu. "Environmental, Social, Governance Risk and Corporate Sustainable Growth Nexus: Quantile Regression Approach." International Journal of Environmental Research and Public Health 18, no. 20 (2021): 10865. http://dx.doi.org/10.3390/ijerph182010865.

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Despite a huge body of literature revealing that the effect of environmental, social and governance (ESG) scores on a firms’ financial performance and value, it lacks the empirical research on the nexus between corporate sustainable growth and ESG risk in the existing research. The paper aims to examine the nexus between ESG risk and corporate sustainable growth. This study utilizes a quantile regression approach to explore how ESG risk affects corporate sustainable growth (proxied by sustainable growth rate, SGR). The ordinary least squares estimation results confirm that ESG significantly ne

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Maswanganyi, Norman, Caston Sigauke, and Edmore Ranganai. "Prediction of Extreme Conditional Quantiles of Electricity Demand: An Application Using South African Data." Energies 14, no. 20 (2021): 6704. http://dx.doi.org/10.3390/en14206704.

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It is important to predict extreme electricity demand in power utilities as the uncertainties in the future of electricity demand distribution have to be taken into consideration to achieve the desired goals. The study focused on the prediction of extremely high conditional quantiles (between 0.95 and 0.9999) and extremely low quantiles (between 0.001 and 0.05) of electricity demand using South African data. The paper discusses a comparative analysis of the additive quantile regression model with an extremal mixture model and a nonlinear quantile regression model. The estimated quantiles at ea

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Lavín, Felipe Vásquez, Ricardo Flores, and Verónica Ibarnegaray. "A Bayesian quantile binary regression approach to estimate payments for environmental services." Environment and Development Economics 22, no. 2 (2016): 156–76. http://dx.doi.org/10.1017/s1355770x16000255.

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AbstractStated preference approaches, such as contingent valuation, focus mainly on the estimation of the mean or median willingness to pay (WTP) for an environmental good. Nevertheless, these two welfare measures may not be appropriate when there are social and political concerns associated with implementing a payment for environmental services (PES) scheme. In this paper the authors used a Bayesian estimation approach to estimate a quantile binary regression and the WTP distribution in the context of a contingent valuation PES application. Our results show that the use of other quantiles fra

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Mboup, Bassirou, Christophe Le Tourneau, and Aurélien Latouche. "Insights for Quantifying the Long-Term Benefit of Immunotherapy Using Quantile Regression." JCO Precision Oncology, no. 5 (January 2021): 173–76. http://dx.doi.org/10.1200/po.20.00164.

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PURPOSE Immunotherapy has been approved to treat many tumor types. However, one characteristic of this therapeutic class is that survival benefit is due to late immune response, which leads to a delayed treatment effect. Quantifying the benefit, if any, of such treatment, will thus require other metrics than the usual hazard ratio and different approaches have been proposed to quantify the long-term response of immunotherapy. METHOD In this paper, we suggest to use quantile regression for survival data to quantify the long-term benefit of immunotherapy. Our motivation is that this approach is

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McGreevy, Katharine M., Stuart R. Lipsitz, Jeffrey A. Linder, Eric Rimm, and David G. Hoel. "Using Median Regression to Obtain Adjusted Estimates of Central Tendency for Skewed Laboratory and Epidemiologic Data." Clinical Chemistry 55, no. 1 (2009): 165–69. http://dx.doi.org/10.1373/clinchem.2008.106260.

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Abstract Background: Laboratory studies often involve analyses of highly skewed data for which means are not an adequate measure of central tendency because they are sensitive to outliers. Attempts to transform skewed data to symmetry are not always successful, and medians are better measures of central tendency for such skewed distributions. When medians are compared across groups, confounding can be an issue, so there is a need for adjusted medians. Methods: We illustrate the use of quantile regression to obtain adjusted medians. The method is illustrated by use of skewed nutrient data obtai

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Jiang, Shan, Xiaoyu Ma, Meng Li, et al. "Association between dietary mineral nutrient intake, body mass index, and waist circumference in U.S. adults using quantile regression analysis NHANES 2007–2014." PeerJ 8 (May 4, 2020): e9127. http://dx.doi.org/10.7717/peerj.9127.

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Objective Mineral nutrients play an important role in maintaining material and energy metabolism. Reports on mineral nutrient intakes and body mass index (BMI) and waist circumference (WC) are rare in the United States. This study examined the relationship between BMI, WC and dietary mineral intakes. Method We used the data from National Health and Nutrition Examination Survey 2007–2014. Nutrient intakes were adjusted for energy according to the residual adjustment method. We used the quantile regression model to analyze the relationship between BMI, WC under different distributions and the av

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Newey, Whitney K., and James L. Powell. "Efficient Estimation of Linear and Type I Censored Regression Models Under Conditional Quantile Restrictions." Econometric Theory 6, no. 3 (1990): 295–317. http://dx.doi.org/10.1017/s0266466600005284.

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We consider the linear regression model with censored dependent variable, where the disturbance terms are restricted only to have zero conditional median (or other prespecified quantile) given the regressors and the censoring point. Thus, the functional form of the conditional distribution of the disturbances is unrestricted, permitting heteroskedasticity of unknown form. For this model, a lower bound for the asymptotic covariance matrix for regular estimators of the regression coefficients is derived. This lower bound corresponds to the covariance matrix of an optimally weighted censored leas

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Hu, Tao, and Baosheng Liang. "A New Class of Estimators Based on a General Relative Loss Function." Mathematics 9, no. 10 (2021): 1138. http://dx.doi.org/10.3390/math9101138.

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Motivated by the relative loss estimator of the median, we propose a new class of estimators for linear quantile models using a general relative loss function defined by the Box–Cox transformation function. The proposed method is very flexible. It includes a traditional quantile regression and median regression under the relative loss as special cases. Compared to the traditional linear quantile estimator, the proposed estimator has smaller variance and hence is more efficient in making statistical inferences. We show that, in theory, the proposed estimator is consistent and asymptotically nor

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Lakshmanasamy, T. "THE DIFFERENTIAL ECONOMIC BENEFITS OF RURAL ELECTRIFICATION IN INDIA: QUANTILE REGRESSION ESTIMATION." MAN, ENVIRONMENT AND SOCIETY 3, no. 1 (2022): 175–91. http://dx.doi.org/10.47509/mes.2022.v03i01.13.

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Rural electrification not only provides affordable modern energy to rural households at a cheaper price but also improves the quality of life and economic development of the rural sector. The welfare gains of electricity are not the same across households. This paper tries to understand who benefits the most from rural electrification - the poor or the rich rural households. The differential effects of rural electrification on household income and expenditures on health and children’s education are estimated using the 2011-2012 IHDS-II survey data applying the quantile regression method. The e

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Lopez-Martin, Manuel, Antonio Sanchez-Esguevillas, Luis Hernandez-Callejo, Juan Ignacio Arribas, and Belen Carro. "Additive Ensemble Neural Network with Constrained Weighted Quantile Loss for Probabilistic Electric-Load Forecasting." Sensors 21, no. 9 (2021): 2979. http://dx.doi.org/10.3390/s21092979.

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This work proposes a quantile regression neural network based on a novel constrained weighted quantile loss (CWQLoss) and its application to probabilistic short and medium-term electric-load forecasting of special interest for smart grids operations. The method allows any point forecast neural network based on a multivariate multi-output regression model to be expanded to become a quantile regression model. CWQLoss extends the pinball loss to more than one quantile by creating a weighted average for all predictions in the forecast window and across all quantiles. The pinball loss for each quan

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Artemenko, Vladislav, and Volodymyr Petrovych. "REGRESSION MAXIMUM AND ITS USE TO HYDROECOLOGICAL RESEARCH." AUTOMOBILE ROADS AND ROAD CONSTRUCTION, no. 111 (June 30, 2022): 200–205. http://dx.doi.org/10.33744/0365-8171-2022-111-200-205.

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One of the actual problems of hydrochemistry there is influence to solar activity on condition chemical substance in water environment. The goal of the work it is possible estimation relationship some hydrochemical factors and solar activity (Wolf's Numbers W). To this effect the research was given for concentration 〖NH〗_4^+, 〖NO〗_2^- , 〖NO〗_3^- in water large flat river of Ukraine (1991 … 2010 years). The ion concentrations denominated in mg on litre. Were they also analysed given over Wolf's numbers for this period (1991 … 2010 years). Since these dependencies for the best reveals itself at

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Nascimento, Moysés, Paulo Eduardo Teodoro, Isabela de Castro Sant’Anna, et al. "Influential Points in Adaptability and Stability Methods Based on Regression Models in Cotton Genotypes." Agronomy 11, no. 11 (2021): 2179. http://dx.doi.org/10.3390/agronomy11112179.

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The aim of this work was to answer the following question: can influential points modify the recommendation of genotypes, based on regression methods, in the presence of genotype × environment (G × E)? Therefore, we compared the parameters of the adaptability and stability of three methodologies based on regression in the presence of influential points. Specifically, were evaluated methods based on simple, non-parametric and quantile (τ = 0.50) regressions. The dataset used in this work corresponds to 18 variety trials of cotton cultivars that were conducted in the 2013–2014 and 2014–2015 crop

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Egbunike, Francis Chinedu, Ardi Gunardi, Udunze Ugochukwu, and Atang Hermawan. "Internal Corporate Governance Mechanisms and Corporate Tax Avoidance in Nigeria: A Quantile Regression Approach." Jurnal Ilmiah Akuntansi dan Bisnis 16, no. 1 (2021): 20. http://dx.doi.org/10.24843/jiab.2021.v16.i01.p02.

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The main objective of the study was to investigate the effect of corporate governance on tax avoidance of quoted manufacturing firms in Nigeria. The study focused on internal corporate governance mechanisms and specifically examined the effect of board size, board independence, board diligence, CEO duality, and audit committee diligence. The ex post facto research design was adopted. The population comprised of all quoted manufacturing companies on the Nigerian Stock Exchange (NSE). The sample was purposively drawn as all companies in the consumer goods sector of the NSE. The study relied on s

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Rahman, Ayesha S., and Ataur Rahman. "Application of Principal Component Analysis and Cluster Analysis in Regional Flood Frequency Analysis: A Case Study in New South Wales, Australia." Water 12, no. 3 (2020): 781. http://dx.doi.org/10.3390/w12030781.

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This paper examines the applicability of principal component analysis (PCA) and cluster analysis in regional flood frequency analysis. A total of 88 sites in New South Wales, Australia are adopted. Quantile regression technique (QRT) is integrated with the PCA to estimate the flood quantiles. A total of eight catchment characteristics are selected as predictor variables. A leave-one-out validation is applied to determine the efficiency of the developed statistical models using an ensemble of evaluation diagnostics. It is found that the PCA with QRT model does not perform well, whereas cluster/

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Oware, Kofi Mintah, and T. Mallikarjunappa. "Corporate social responsibility and debt financing of listed firms: a quantile regression approach." Journal of Financial Reporting and Accounting 19, no. 4 (2021): 615–39. http://dx.doi.org/10.1108/jfra-07-2020-0202.

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Purpose The purpose of the study is to examine the effect of corporate social responsibility (CSR) on debt financing (natural logarithm of debt and leverage ratios) of listed firms. Design/methodology/approach Using content analysis for data extraction, the study examines listed firms on the Bombay Stock Exchange (BSE) from 2010 to 2019 financial year. It uses a quantile regression and panel fixed effect regression as the model's application. Findings The study shows that CSR expenditure has a positive and strong correlation with debt financing (i.e. natural logarithm of long-term and short-te

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Porter, W. C., C. L. Heald, D. Cooley, and B. Russell. "Investigating the observed sensitivities of air quality extremes to meteorological drivers via quantile regression." Atmospheric Chemistry and Physics Discussions 15, no. 10 (2015): 14075–109. http://dx.doi.org/10.5194/acpd-15-14075-2015.

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Abstract. Air pollution variability is strongly dependent on meteorology. However, quantifying the impacts of changes in regional climatology on pollution extremes can be difficult due to the many non-linear and competing meteorological influences on the production, transport, and removal of pollutant species. Furthermore, observed pollutant levels at many sites show sensitivities at the extremes that differ from those of the overall mean, indicating relationships that would be poorly characterized by simple linear regressions. To address this challenge, we apply quantile regression to observe

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Porter, W. C., C. L. Heald, D. Cooley, and B. Russell. "Investigating the observed sensitivities of air-quality extremes to meteorological drivers via quantile regression." Atmospheric Chemistry and Physics 15, no. 18 (2015): 10349–66. http://dx.doi.org/10.5194/acp-15-10349-2015.

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Abstract. Air pollution variability is strongly dependent on meteorology. However, quantifying the impacts of changes in regional climatology on pollution extremes can be difficult due to the many non-linear and competing meteorological influences on the production, transport, and removal of pollutant species. Furthermore, observed pollutant levels at many sites show sensitivities at the extremes that differ from those of the overall mean, indicating relationships that would be poorly characterized by simple linear regressions. To address this challenge, we apply quantile regression to observe

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Bhattacharya, Shibaprasad, Kanak Kalita, Robert Čep, and Shankar Chakraborty. "A Comparative Analysis on Prediction Performance of Regression Models during Machining of Composite Materials." Materials 14, no. 21 (2021): 6689. http://dx.doi.org/10.3390/ma14216689.

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Modeling the interrelationships between the input parameters and outputs (responses) in any machining processes is essential to understand the process behavior and material removal mechanism. The developed models can also act as effective prediction tools in envisaging the tentative values of the responses for given sets of input parameters. In this paper, the application potentialities of nine different regression models, such as linear regression (LR), polynomial regression (PR), support vector regression (SVR), principal component regression (PCR), quantile regression, median regression, ri

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SHARMA, ANDY. "Modelling disparities in health services utilisation for older Blacks: a quantile regression framework." Ageing and Society 35, no. 8 (2014): 1657–83. http://dx.doi.org/10.1017/s0144686x14000440.

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ABSTRACTWith the on-going ageing of the United States population, resolving health disparities continues to be a prominent and worthwhile goal, particularly in the areas of promoting minority health and reducing racial/ethnic disparities. This analysis employs the 2004 and 2005 Household Component records from the Medical Expenditures Panel Survey, which correspond to data files H89 and H97, to examine utilisation by race across the entire distribution function; more specifically, applying the behavioural model of health services utilisation and employing a Quantile Regression (QR) framework.

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Lee, MinJae, Mohammad H. Rahbar, and Hooshang Talebi. "A nonparametric method for assessment of interactions in a median regression model for analyzing right censored data." Statistical Methods in Medical Research 28, no. 4 (2018): 1170–87. http://dx.doi.org/10.1177/0962280217751518.

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We propose a nonparametric test for interactions when we are concerned with investigation of the simultaneous effects of two or more factors in a median regression model with right censored survival data. Our approach is developed to detect interaction in special situations, when the covariates have a finite number of levels with a limited number of observations in each level, and it allows varying levels of variance and censorship at different levels of the covariates. Through simulation studies, we compare the power of detecting an interaction between the study group variable and a covariate

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Nwakuya, Nwakuya, M. T, and Onyegbuchulam B. O. "Quantile Regression-based Multiple Imputation of Skewed Data with Different Percentages of Missingness." Scholars Journal of Physics, Mathematics and Statistics 9, no. 4 (2022): 41–45. http://dx.doi.org/10.36347/sjpms.2022.v09i04.002.

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This study investigates the Quantile Regression-Based Multiple Imputation (QR-based MI) on a simulated right skewed data with 5% and 25% missing data points. Quantile regression analysis on three data sets that comprises of the complete skewed data without missing values, data set with 5% missing values and data set with 25% missing values was performed at 0.25, 0.5, 0.75 and 0.95 quantiles. The data sets with 5% and 25% missing values were imputed using QR-based MI technique, giving rise to two complete data sets. This analysis was performed using both transformed and untransformed version of

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Qian, Zhonghua, Luyao Wang, Xin Chen, Hui Zhang, and Zimeng Li. "Heteroscedastic Characteristics of Precipitation with Climate Changes in China." Atmosphere 13, no. 12 (2022): 2116. http://dx.doi.org/10.3390/atmos13122116.

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With global warming, previous studies have found nonuniformity responses of precipitation because of regional differences. However, climate change affects the mean, extreme, and data structure of precipitation. Quantile regression, which can reflect every part of the trends of data, was used to detect responses of each part of precipitation in China. The V2.0 dataset of daily precipitation grid data (0.5° × 0.5°) from 1961 to 2020 in China was used as practical observation data. Daily precipitation in 2015–2100 from the China Model BCC-CSM2-MR of scenarios SSP2-4.5 and SSP5-8.5 were chosen as

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Shahsavarinia, Kavous, Hassan Soleimanpour, Sepideh Harzand Jadidi, Mohammad Saadati, Aida Javanmardi, and Gilani Neda. "Determinants of Hospital Length of Stay among Burn Patients Using Quantile Regression." Depiction of Health 12, no. 3 (2021): 262–72. http://dx.doi.org/10.34172/doh.2021.26.

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Background and Objectives Burns is one of the most common and potentially dangerous public health problems. Burn patients' hospitalization facilitates the provision of medical services. However, prolonging the length of hospital stay can not only impose an economic cost, but also cause various infections in patients. Various factors affect patients’ hospitalization length including patients age and gender, burn cause, anatomic place and its severity. Identifying and considering these factors will reduce burn patient’s hospitalization and its consequences. The aim of this study was to evaluate

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Sousa, Kleber Morais de, and Paulo Aguiar do Monte. "Public expenditure composition and fiscal decentralization in Brazilian local governments: an analysis through unconditional quantile regression with longitudinal data." Revista de Administração Pública 55, no. 6 (2021): 1333–54. http://dx.doi.org/10.1590/0034-761220200864.

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Abstract This paper investigates the effect of increasing fiscal decentralization on the composition of public expenditures of Brazilian local governments. The research is innovative, demonstrating that the heterogeneity of expenditure scale influences the correlation between fiscal decentralization and public expenditure of local governments. The sample consisted of unbalanced panel data of 5,565 municipalities for 17 years from 2000 to 2016. The analysis used unconditional quantile regression with panel data. The main findings were: (i) fiscal decentralization affects public expenditure in B

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Noor, Farhana, Orpita U. Laz, Khaled Haddad, Mohammad A. Alim, and Ataur Rahman. "Comparison between Quantile Regression Technique and Generalised Additive Model for Regional Flood Frequency Analysis: A Case Study for Victoria, Australia." Water 14, no. 22 (2022): 3627. http://dx.doi.org/10.3390/w14223627.

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For design flood estimation in ungauged catchments, Regional Flood Frequency Analysis (RFFA) is commonly used. Most of the RFFA methods are primarily based on linear modelling approaches, which do not account for the inherent nonlinearity of rainfall-runoff processes. Using data from 114 catchments in Victoria, Australia, this study employs the Generalised Additive Model (GAM) in RFFA and compares the results with linear method known as Quantile Regression Technique (QRT). The GAM model performance is found to be better for smaller return periods (i.e., 2, 5 and 10 years) with a median relativ

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Khan, Kiren S., Jessica Logan, Laura M. Justice, Ryan P. Bowles, and Shayne B. Piasta. "The Contribution of Vocabulary, Grammar, and Phonological Awareness Across a Continuum of Narrative Ability Levels in Young Children." Journal of Speech, Language, and Hearing Research 64, no. 9 (2021): 3489–503. http://dx.doi.org/10.1044/2021_jslhr-20-00403.

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Purpose Narrative skill represents a higher-level linguistic skill that shows incremental development in the preschool years. During these years, there are considerable individual differences in this skill, with some children being highly skilled narrators (i.e., precocious) relative to peers of their age. In this study, we explored the contribution of three lower-level language skills to a range of narrative abilities, from children performing below expected levels for their age to those performing much higher than the expected levels for their age. We speculated that individual differences i

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Shrivastava, Shreya, Vandana Patil, Madhavi Shelke, Madhura Anvikar, Aditya Mathur, and Ashish Pathak. "Assessment of school readiness of children and factors associated with risk of inadequate school readiness in Ujjain, India: an observational study." BMJ Paediatrics Open 3, no. 1 (2019): e000509. http://dx.doi.org/10.1136/bmjpo-2019-000509.

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ObjectiveSchool readiness is a condition or state indicating that the child is ready to learn in a formal educational set-up. The objective of this study was to estimate the prevalence of and factors associated with school readiness in urban schoolchildren in Ujjain, India.MethodsThis cross-sectional study was conducted from February 2016 to March 2017. Two English-medium schools were conveniently selected. All children aged 5–7 years were eligible to participate. A subscale of Differential Ability Scales-Second Edition, namely ‘school readiness scale’, was used to assess school readiness in t

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Xu, Xiaocang, and Linhong Chen. "Projection of Long-Term Care Costs in China, 2020–2050: Based on the Bayesian Quantile Regression Method." Sustainability 11, no. 13 (2019): 3530. http://dx.doi.org/10.3390/su11133530.

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The aging population in China highlights the significance of elderly long-term care (LTC) services. The number of people aged 65 and above increased from 96 million in 2003 to 150 million in 2016, some of whom were disabled due to chronic diseases or the natural effects of aging on bodily functions. Therefore, the measurement of future LTC costs is of crucial value. Following the basic framework but using different empirical methods from those presented in previous literature, this paper attempts to use the Bayesian quantile regression (BQR) method, which has many advantages over traditional l

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Cherdantsev, D. V., A. V. Stroev, E. S. Mangalova, N. V. Kononova, and O. V. Chubarova. "The use of ridge regression for estimating the severity of acute pancreatitis." Bulletin of Siberian Medicine 18, no. 3 (2019): 107–15. http://dx.doi.org/10.20538/1682-0363-2019-3-107-115.

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Purpose. Increasing of treatment efficiency for patients with acute pancreatitis by improving objective means of determining the severity of acute pancreatitis.Materials and method. The study was based on a retrospective analysis of 130 cases of acute pancreatitis: 47 cases from «Krasnoyarsk Regional Clinical Hospital» and 83 cases from «Regional Interdistrict Clinical Hospital No 20 named after I.S. Berzon» in the period from 2015 to 2017. The raw data was pre-processed. In particular, different methods (median, linear regression) were used to fill the missing values in the observation matrix

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Tobechukwu, Nwakuya Maureen. "Quantile Generalized Additive Model a Robust Alternative to Generalized Additive Model." International Journal of Mathematical Research 10, no. 1 (2021): 12–18. http://dx.doi.org/10.18488/journal.24.2021.101.12.18.

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Nonparametric regression is an approach used when the structure of the relationship between the response and the predictor variable is unknown. It tries to estimate the structure of this relationship since there is no predetermined form. The generalized additive model (GAM) and quantile generalized additive (QGAM) model provides an attractive framework for nonparametric regression. The QGAM focuses on the features of the response beyond the central tendency, while the GAM focuses on the mean response. The analysis was done using gam and qgam packages in R, using data set on live-births, fertil

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Peng, Limin, Joanne Wuu, and Michael Benatar. "Developing reference data for nerve conduction studies: An application of quantile regression." Muscle & Nerve 40, no. 5 (2009): 763–71. http://dx.doi.org/10.1002/mus.21489.

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