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Journal articles on the topic 'Quantity quantile regression'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

Pryce, Robert, Bruce Hollingsworth, and Ian Walker. "Alcohol quantity and quality price elasticities: quantile regression estimates." European Journal of Health Economics 20, no. 3 (2018): 439–54. http://dx.doi.org/10.1007/s10198-018-1009-8.

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2

Alamari, Mohammed B., Fatimah A. Almulhim, Ouahiba Litimein, and Boubaker Mechab. "Strong Consistency of Incomplete Functional Percentile Regression." Axioms 13, no. 7 (2024): 444. http://dx.doi.org/10.3390/axioms13070444.

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This paper analyzes the co-fluctuation between a scalar response random variable and a curve regressor using quantile regression. We focus on the situation wherein the output variable is observed with random missing. For this incomplete functional data situation, we estimate the quantile regression by combining two principal nonparametric methods: the local linearity approach (LLA) and the kernel nearest neighbor (KNN) algorithm. We study the asymptotic properties of the constructed estimator by establishing, under general assumptions, uniform consistency over the number of neighborhoods. This

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3

Forthmann, Boris, and Denis Dumas. "Quantity and Quality in Scientific Productivity: The Tilted Funnel Goes Bayesian." Journal of Intelligence 10, no. 4 (2022): 95. http://dx.doi.org/10.3390/jintelligence10040095.

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The equal odds baseline model of creative scientific productivity proposes that the number of high-quality works depends linearly on the number of total works. In addition, the equal odds baseline implies that the percentage of high-quality works and total number of works are uncorrelated. The tilted funnel hypothesis proposes that the linear regression implied by the equal odds baseline is heteroscedastic with residual variance in the quality of work increasing as a function of quantity. The aim of the current research is to leverage Bayesian statistical modeling of the equal odds baseline. P

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4

Julius, Olumide Ilesanmi1* Sadiat Funmilayo Arifalo2. "Heterogeneity Effects of Improved Tomato Farmers' Technical Efficiency in Southwest, Nigeria: An Unconditional Quantile Regression Approach." ISRG Journal of Agriculture and Veterinary Sciences (ISRGJAVS) II, no. II (2025): 14–21. https://doi.org/10.5281/zenodo.15033934.

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<strong>Abstract</strong> <em>Nigeria's present economic situation has raised production costs and directly affected the affordability of farm inputs, endangering both the sustainability of output and the improvement of farmers' efficiency. Furthermore, the consequences of climate change are becoming more noticeable, and one of the industry’s most at risk is agriculture. Therefore, to alleviate all of these problems, improved tomato varieties and sustainable farming techniques must be developed to boost farmers' output. Therefore, the study aimed to measure the efficiency of the farmers

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5

Carreño, Pia, and Andres Silva. "Fruit and vegetable expenditure disparities: evidence from Chile." British Food Journal 121, no. 6 (2019): 1203–19. http://dx.doi.org/10.1108/bfj-06-2018-0365.

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Purpose The purpose of this paper is to explore fruit and vegetable (FV) procurement disparity across income groups. Design/methodology/approach This study uses mean comparison and quintile regression to explain FVs variations. Findings Households from the highest income quantile spend more than two times on FVs than households from the lowest quantile; however, this expenditure disparity is largely mitigated in terms of purchase quantity. This paper presents evidence that, rather than quantity discounts or income neighborhood, the type of store (traditional markets vs supermarkets) plays a re

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6

Idris, N., Rais Rais, and I. T. Utami. "APLIKASI REGRESI KUANTIL PADA KASUS DBD DI KOTA PALU SULAWESI TENGAH." JURNAL ILMIAH MATEMATIKA DAN TERAPAN 15, no. 1 (2018): 108–17. http://dx.doi.org/10.22487/2540766x.2018.v15.i1.10207.

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Palu city is one of the cities with unstable changes of natural conditions. The natural conditions such as the frequency of rainy day, temperature and humidity which are always changeable bring bad impacts and will cause of diseases especially dengue hemorrhagic fever (DBD). Therefore, it needs an action to recognise whether or not the natural condition factor influences the spread of DBD and determines what factors of the natural condition can influence the spread of DBD. This research applied quantile regression in the case of DBD in Palu city. Quantile regression is an analysis technique re

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7

Hlubinka, Daniel, and Miroslav Šiman. "On elliptical quantiles in the quantile regression setup." Journal of Multivariate Analysis 116 (April 2013): 163–71. http://dx.doi.org/10.1016/j.jmva.2012.11.016.

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8

Kostakis, Ioannis, Dimitrios Paparas, Anna Saiti, and Stamatina Papadaki. "Food Consumption within Greek Households: Further Evidence from a National Representative Sample." Economies 8, no. 1 (2020): 17. http://dx.doi.org/10.3390/economies8010017.

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The aim of this study is to characterize the relationship between food consumption and socio-demographic characteristics in several groups of individuals. This is achieved by capturing the quantity of food purchased in categories on a microeconomic level. The empirical analysis is approached through the estimation of (a) expanded generalized linear models, (b) quantile regression models, (c) quadratic almost ideal demand system models and (d) Deaton’s (1988) approach. The results reveal that the composition of a household has a significant impact on the quantity of food consumed. In addition,

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9

Lipovetsky, Stan. "Quantile Regression." Technometrics 48, no. 3 (2006): 445–46. http://dx.doi.org/10.1198/tech.2006.s410.

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10

Jurečková, Jana. "Quantile Regression." Journal of the American Statistical Association 101, no. 476 (2006): 1723. http://dx.doi.org/10.1198/jasa.2006.s143.

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11

Das, Kiranmoy, Martin Krzywinski, and Naomi Altman. "Quantile regression." Nature Methods 16, no. 6 (2019): 451–52. http://dx.doi.org/10.1038/s41592-019-0406-y.

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12

ALTIN YAVUZ, Arzu, and Ebru GÜNDOĞAN AŞIK. "Quantile Regression." Uluslararası Muhendislik Arastirma ve Gelistirme Dergisi 9, no. 2 (2017): 137–46. http://dx.doi.org/10.29137/umagd.352530.

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13

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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14

Chernozhukov, Victor, Antonio F. Galvao, Xuming He, and Zhijie Xiao. "Quantile regression." Journal of Econometrics 213, no. 1 (2019): 1–3. http://dx.doi.org/10.1016/j.jeconom.2019.04.002.

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15

Park, Cheolwoo, Thomas C. M. Lee, and Jan Hannig. "Multiscale Exploratory Analysis of Regression Quantiles Using Quantile SiZer." Journal of Computational and Graphical Statistics 19, no. 3 (2010): 497–513. http://dx.doi.org/10.1198/jcgs.2010.09120.

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16

Paindaveine, Davy, and Miroslav Šiman. "Computing multiple-output regression quantile regions from projection quantiles." Computational Statistics 27, no. 1 (2011): 29–49. http://dx.doi.org/10.1007/s00180-011-0231-y.

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17

Dodge, Yadolah, and Jana Jurečková. "Estimation of quantile density function based on regression quantiles." Statistics & Probability Letters 23, no. 1 (1995): 73–78. http://dx.doi.org/10.1016/0167-7152(94)00097-r.

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18

Fatimata, LO, D. B. Ba, and D. Aba. "STATISTICAL INFERENCE IN THE GENERALIZED EXTREME VALUE REGRESSION MODEL BASED ON SIMULATION STUDY." Journal of Computer Science and Applied Mathematics 5, no. 2 (2023): 103–16. http://dx.doi.org/10.37418/jcsam.5.2.5.

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Generalized extreme value (GEV) regression model is widely used when the dependent variable $Y$ represents a rare event. In this case the logistic regression model shows relevant drawbacks. The quantile function of the GEV distribution is used as link function to investigate the relationship between the binary outcome $Y$ and a set of potential predictors $\mathbf X$. Maximum likelihood estimators in this model has been proposed, and their asymptotic properties recently established. We conduct a detailed simulation study of its numerical properties. We evaluate its accuracy and the quality of

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19

Hlubinka, Daniel, and Miroslav Šiman. "On generalized elliptical quantiles in the nonlinear quantile regression setup." TEST 24, no. 2 (2014): 249–64. http://dx.doi.org/10.1007/s11749-014-0405-3.

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20

Cao, Jialei, and Chenran Ge. "Research on the Impact of Technology Innovation on Quantity and Quality of Economic Growth in the Yangtze River Delta of China: A Comparative Study." International Journal of Sustainable Development and Planning 16, no. 8 (2021): 1455–64. http://dx.doi.org/10.18280/ijsdp.160806.

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High-quality economic development (HQED) has recently become a crucial sustainable growth mode in China, which pursues economic growth while maintaining social equity and green ecology. The HQED of the Yangtze River Delta (YRD) has played an exemplary role in achieving the leap from “China speed” to “China Quality”. In this paper, we first use the entropy-weight multidimensional comprehensive evaluation method to calculate the HQED index as a proxy for the quality of economic growth. Then, using panel data of 41 cities in the YRD, we conduct a comparative study to examine impacts of technologi

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21

Sueyoshi, Toshiyuki. "EMPIRICAL REGRESSION QUANTILE." Journal of the Operations Research Society of Japan 34, no. 3 (1991): 250–62. http://dx.doi.org/10.15807/jorsj.34.250.

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22

Тюрин, А. С. "Adaptive quantile regression." МОДЕЛИРОВАНИЕ, ОПТИМИЗАЦИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ 12, no. 1(44) (2024): 16. http://dx.doi.org/10.26102/2310-6018/2024.44.1.016.

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Актуальность темы исследования обусловлена растущей потребностью в быстрых и точных инструментах построения математических моделей. В данной работе рассматриваются подходы к построению адаптивной квантильной регрессии, так как выбор оптимального квантиля в процессе обучения может сэкономить большое количество времени исследователя. Правильный выбор квантиля может существенно улучшить показатели модели на тестовых наборах данных и, как следствие, позволит получать более надежные прогнозы при реальном использовании такой математической модели. Разработанный подход представляет собой комбинацию м

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23

Rodrigues, T., J. L. Dortet-Bernadet, and Y. Fan. "Pyramid Quantile Regression." Journal of Computational and Graphical Statistics 28, no. 3 (2019): 732–46. http://dx.doi.org/10.1080/10618600.2019.1575225.

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24

Spokoiny, Vladimir, Weining Wang, and Wolfgang Karl Härdle. "Local quantile regression." Journal of Statistical Planning and Inference 143, no. 7 (2013): 1109–29. http://dx.doi.org/10.1016/j.jspi.2013.03.008.

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25

Tong, Hongzhi, and Qiang Wu. "Moving quantile regression." Journal of Statistical Planning and Inference 205 (March 2020): 46–63. http://dx.doi.org/10.1016/j.jspi.2019.06.003.

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26

Yu, Keming, and Rana A. Moyeed. "Bayesian quantile regression." Statistics & Probability Letters 54, no. 4 (2001): 437–47. http://dx.doi.org/10.1016/s0167-7152(01)00124-9.

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27

Chernozhukov, Victor. "Extremal quantile regression." Annals of Statistics 33, no. 2 (2005): 806–39. http://dx.doi.org/10.1214/009053604000001165.

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28

Dodge, Yadolah, and Joe Whittaker. "Partial quantile regression." Metrika 70, no. 1 (2008): 35–57. http://dx.doi.org/10.1007/s00184-008-0177-4.

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29

Trzpiot, Grażyna. "Spatial Quantile Regression." Comparative Economic Research. Central and Eastern Europe 15, no. 4 (2013): 265–79. http://dx.doi.org/10.2478/v10103-012-0040-8.

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In a number of applications, a crucial problem consists in describing and analyzing the influence of a vector Xi of covariates on some real-valued response variable Yi. In the present context, where the observations are made over a collection of sites, this study is more difficult, due to the complexity of the possible spatial dependence among the various sites. In this paper, instead of spatial mean regression, we thus consider the spatial quantile regression functions. Quantile regression has been considered in a spatial context. The main aim of this paper is to incorporate quantile regressi

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30

Xiao, Zhijie. "Quantile cointegrating regression." Journal of Econometrics 150, no. 2 (2009): 248–60. http://dx.doi.org/10.1016/j.jeconom.2008.12.005.

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31

Aloqaili, Murtadha Jaafar, and Rahim Alhamzawi. "Bayesian Composite Quantile Regression with Composite Group Bridge Penalty." NeuroQuantology 20, no. 2 (2022): 173–79. http://dx.doi.org/10.14704/nq.2022.20.2.nq22269.

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We study composite quantile regression (CQReg) with composite group bridge penalty for model selection and estimation. Compared to conventional mean regression, composite quantile regression (CQR) is an efficient and robust estimation approach. A simple and efficient algorithm was developed for posterior inference using a pseudo composite asymmetric Laplace distribution which can be formulated as a location-scale mixture of normals. The composite group bridge priors were formulated as a scale mixture of multivariate uniforms. We assess the performance of the proposed method using simulation st

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32

Radaelli, Paolo, and Michele Zenga. "Quantity quantiles linear regression." Statistical Methods and Applications 17, no. 4 (2007): 455–69. http://dx.doi.org/10.1007/s10260-007-0071-7.

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33

Kuan, Chung-Ming, Christos Michalopoulos, and Zhijie Xiao. "Quantile Regression on Quantile Ranges - A Threshold Approach." Journal of Time Series Analysis 38, no. 1 (2016): 99–119. http://dx.doi.org/10.1111/jtsa.12204.

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34

Li, Ruosha, and Limin Peng. "Assessing quantile prediction with censored quantile regression models." Biometrics 73, no. 2 (2016): 517–28. http://dx.doi.org/10.1111/biom.12627.

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35

Fernandes, Marcelo, Emmanuel Guerre, and Eduardo Horta. "Smoothing Quantile Regressions." Journal of Business & Economic Statistics 39, no. 1 (2019): 338–57. http://dx.doi.org/10.1080/07350015.2019.1660177.

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36

So, Byung-Jin, Hyun-Han Kwon, and Jung-Hee An. "Trend Analysis of Extreme Precipitation Using Quantile Regression." Journal of Korea Water Resources Association 45, no. 8 (2012): 815–26. http://dx.doi.org/10.3741/jkwra.2012.45.8.815.

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37

Shaikh, Imlak. "The Relation between Implied Volatility Index and Crude Oil Prices." Engineering Economics 30, no. 5 (2019): 556–66. http://dx.doi.org/10.5755/j01.ee.30.5.21611.

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Crude oil is a global commodity traded across the world market. The prices of the commodity over an extended period for crude oil have been analyzed using daily prices of crude oil futures and the implied volatility index (OVX). This paper aims to find the predictability of various parameters on the basis of time using neural network and quantile regression methods. Several estimates have been shown based on Barone, Adesi, and Whaley’s (BAW) model of neural network. Estimation parameters include opening, closing, highest and lowest price of the commodity and volumes traded for a given commodit

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38

Jurečková, Jana, Jan Picek, and Martin Schindler. "Empirical regression quantile processes." Applications of Mathematics 65, no. 3 (2020): 257–69. http://dx.doi.org/10.21136/am.2020.0295-19.

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39

Yoon, Jong In. "Understanding the Quantile Regression." Korean Society of Human and Nature 3, no. 1 (2022): 157–74. http://dx.doi.org/10.54913/hn.2022.3.1.157.

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In linear regression, the regression coefficient represents the change in the response variable produced by a one unit increase in the preditcor variable associated with that coefficient. The quantile regression parameter estimates the change in a specified quantile of the response variable produced by a one unit change in the predictor variable. In investigating the relationship between the employment growth and a set of predictors, the quantile regression allows comparing how some percentiles of the employment growth of firms may be more affected by certain firm’s characteristics than other

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40

Galvao and Poirier. "Quantile Regression Random Effects." Annals of Economics and Statistics, no. 134 (2019): 109. http://dx.doi.org/10.15609/annaeconstat2009.134.0109.

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41

Méndez-Civieta, Álvaro, M. Carmen Aguilera-Morillo, and Rosa E. Lillo. "Fast partial quantile regression." Chemometrics and Intelligent Laboratory Systems 223 (April 2022): 104533. http://dx.doi.org/10.1016/j.chemolab.2022.104533.

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42

Seo, Kang-Min, Sung-Wan Bang, and Myoung-Shic Jhun. "Bootstrapping Composite Quantile Regression." Korean Journal of Applied Statistics 25, no. 2 (2012): 341–50. http://dx.doi.org/10.5351/kjas.2012.25.2.341.

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43

Koo, Ja-Yong, Kwi Wook Park, Byung Won Kim, Kwang-Rae Kim, and Changyi Park. "Structured kernel quantile regression." Journal of Statistical Computation and Simulation 83, no. 1 (2013): 179–90. http://dx.doi.org/10.1080/00949655.2011.631923.

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44

Kang, Jongkyeong, Sungwan Bang, and Myoungshic Jhun. "Hierarchically penalized quantile regression." Journal of Statistical Computation and Simulation 86, no. 2 (2015): 340–56. http://dx.doi.org/10.1080/00949655.2015.1014038.

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45

Huang, Hanwen, and Zhongxue Chen. "Bayesian composite quantile regression." Journal of Statistical Computation and Simulation 85, no. 18 (2015): 3744–54. http://dx.doi.org/10.1080/00949655.2015.1014372.

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46

Peng, Limin, and Jason P. Fine. "Competing Risks Quantile Regression." Journal of the American Statistical Association 104, no. 488 (2009): 1440–53. http://dx.doi.org/10.1198/jasa.2009.tm08228.

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47

Reich, Brian J., Montserrat Fuentes, and David B. Dunson. "Bayesian Spatial Quantile Regression." Journal of the American Statistical Association 106, no. 493 (2011): 6–20. http://dx.doi.org/10.1198/jasa.2010.ap09237.

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48

Li, Youjuan, and Ji Zhu. "L1-Norm Quantile Regression." Journal of Computational and Graphical Statistics 17, no. 1 (2008): 163–85. http://dx.doi.org/10.1198/106186008x289155.

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49

Hahn, Jinyong. "Bootstrapping Quantile Regression Estimators." Econometric Theory 11, no. 1 (1995): 105–21. http://dx.doi.org/10.1017/s0266466600009051.

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The asymptotic variance matrix of the quantile regression estimator depends on the density of the error. For both deterministic and random regressors, the bootstrap distribution is shown to converge weakly to the limit distribution of the quantile regression estimator in probability. Thus, the confidence intervals constructed by the bootstrap percentile method have asymptotically correct coverage probabilities.

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50

Chakraborty, Biman. "On multivariate quantile regression." Journal of Statistical Planning and Inference 110, no. 1-2 (2003): 109–32. http://dx.doi.org/10.1016/s0378-3758(01)00277-4.

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