Relevant bibliographies by topics / Quantity quantile regression

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Quantity quantile regression'

Author: Grafiati
Published: 9 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "Quantity quantile regression"

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

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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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Dissertations / Theses on the topic "Quantity quantile regression"

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RADAELLI, PAOLO. "La Regressione Lineare con i Valori Assoluti." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2004. http://hdl.handle.net/10281/2290.

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The estimation of regression coefficients in the linear model is usually provided by least squares (LS) minimizing the sum of the squares of residuals. An alternative estimator is obtained by minimizing the sum of absolute residuals (MSAE) and was first introduced by Boscovich in 1757 for the straight line. We first provide a short historical background and then we show in detail, from a descriptive point of view, how to obtain the median regression (MSAE) coefficients for the straight line and, for the more general case of the hyperplane, the formulation of the problem as a linear programming
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Rodrigues, Cátia Sofia Martins. "Quais os fatores que determinam o rendimento dos indivíduos em Portugal? - Regressão de Quantis." Master's thesis, Instituto Superior de Economia e Gestão, 2021. http://hdl.handle.net/10400.5/23425.

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Mestrado Bolonha em Métodos Quantitativos para a Decisão Económica e Empresarial<br>Apesar de se ter vindo a verificar, ao longo dos anos, um decréscimo significativo na desigualdade entre rendimentos, este tema ainda é alvo de estudo, principalmente numa abordagem econométrica, onde o principal objetivo passa por identificar e perceber os principais fatores que estão por detrás das desigualdades sentidas. Desta forma, o presente projeto destina-se ao estudo dos fatores que determinam o rendimento dos indivíduos residentes em Portugal, adotando uma abordagem de regressão de quantis, uma vez q
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Guo, Mengmeng. "Generalized quantile regression." Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2012. http://dx.doi.org/10.18452/16569.

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Die generalisierte Quantilregression, einschließlich der Sonderfälle bedingter Quantile und Expektile, ist insbesondere dann eine nützliche Alternative zum bedingten Mittel bei der Charakterisierung einer bedingten Wahrscheinlichkeitsverteilung, wenn das Hauptinteresse in den Tails der Verteilung liegt. Wir bezeichnen mit v_n(x) den Kerndichteschätzer der Expektilkurve und zeigen die stark gleichmßige Konsistenzrate von v-n(x) unter allgemeinen Bedingungen. Unter Zuhilfenahme von Extremwerttheorie und starken Approximationen der empirischen Prozesse betrachten wir die asymptotischen max
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Yu, Keming. "Smooth regression quantile estimation." Thesis, Open University, 1996. http://oro.open.ac.uk/57655/.

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In this thesis, attention will be mainly focused on the local linear kernel regression quantile estimation. Different estimators within this class have been proposed, developed asymptotically and applied to real applications. I include algorithmdesign and selection of smoothing parameters. Chapter 2 studies two estimators, first a single-kernel estimator based on "check function" and a bandwidth selection rule is proposed based on the asymptotic MSE of this estimator. Second a recursive double-kernel estimator which extends Fan et al's (1996) density estimator, and two algorithms are given for
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Sanches, Nathalie C. Gimenes Miessi. "Quantile regression approaches for auctions." Thesis, Queen Mary, University of London, 2014. http://qmro.qmul.ac.uk/xmlui/handle/123456789/8146.

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The goal of this thesis is to propose a new quantile regression approach to identify and estimate the quantiles of the private value conditional distribution in ascending and rst price auctions under the Independent Private Value (IPV) paradigm. The quantile regression framework provides a exible and convenient parametrization of the private value distribution, which is not a ected by the curse of dimensionality. The rst Chapter of the thesis introduces a quantile regression methodology for ascending auctions. The Chapter focuses on revenue analysis, optimal reservation price and its associate
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Jeffrey, Stephen Glenn. "Quantile regression and frontier analysis." Thesis, University of Warwick, 2012. http://wrap.warwick.ac.uk/47747/.

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In chapter 3, quantile regression is used to estimate probabilistic frontiers, i.e. frontiers based on the probability of being dominated. The results from the empirical application using an Italian hotel dataset show rejections of a parametric functional form and a location shift effect, large uncertainty of the estimates of the frontier and wide confidence intervals for the estimates of efficiency. Quantile regression is further developed to estimate thick probabilistic frontiers, i.e. frontiers based on a group of efficient firms. The empirical results show that the differences between the
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Chao, Shih-Kang. "Quantile regression in risk calibration." Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2015. http://dx.doi.org/10.18452/17223.

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Die Quantilsregression untersucht die Quantilfunktion QY |X (τ ), sodass ∀τ ∈ (0, 1), FY |X [QY |X (τ )] = τ erfu ̈llt ist, wobei FY |X die bedingte Verteilungsfunktion von Y gegeben X ist. Die Quantilsregression ermo ̈glicht eine genauere Betrachtung der bedingten Verteilung u ̈ber die bedingten Momente hinaus. Diese Technik ist in vielerlei Hinsicht nu ̈tzlich: beispielsweise fu ̈r das Risikomaß Value-at-Risk (VaR), welches nach dem Basler Akkord (2011) von allen Banken angegeben werden muss, fu ̈r ”Quantil treatment-effects” und die ”bedingte stochastische Dominanz (CSD)”, welches wirtschaf
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Elseidi, Mohammed. "Quantile regression-based seasonal adjustment." Doctoral thesis, Università degli studi di Padova, 2019. http://hdl.handle.net/11577/3423191.

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Time series of different nature might be characterised by the presence of deterministic and/or stochastic seasonal patterns. By seasonality, we refer to periodic fluctuations affecting not only the mean but also the shape, the dispersion and in general the density of the variable of interest over time. Using traditional approaches for seasonal adjustment might not be efficient because they do not ensure, for instance, that the adjusted data are free from periodic behaviours in, say, higher-order moments. We introduce a seasonal adjustment method based on quantile regression that is capable of
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Liu, Xi. "Some new developments for quantile regression." Thesis, Brunel University, 2018. http://bura.brunel.ac.uk/handle/2438/16204.

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Quantile regression (QR) (Koenker and Bassett, 1978), as a comprehensive extension to standard mean regression, has been steadily promoted from both theoretical and applied aspects. Bayesian quantile regression (BQR), which deals with unknown parameter estimation and model uncertainty, is a newly proposed tool of QR. This thesis aims to make some novel contributions to the following three issues related to QR. First, whereas QR for continuous responses has received much attention in literatures, QR for discrete responses has received far less attention. Second, conventional QR methods often sh
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Kecojevic, Tatjana. "Bootstrap inference for parametric quantile regression." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/bootstrap-inference-for-parametric-quantile-regression(194021d5-e03f-4f48-bfb8-5156819f5900).html.

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The motivation for this thesis came from the provision of a large data set from Saudi Arabia giving anthropometric measurements of children and adolescents from birth to eighteen years of age, with a requirement to construct growth charts. The construction of these growth charts revealed a number of issues particularly in the respect to statistical inference relating to quantile regression. To investigate a range of different statistical inference procedures in parametric quantile regression in particular the estimation of the confidence limits of the ?th (?? [0, 1]) quantile, a number of sets
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Books on the topic "Quantity quantile regression"

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Hao, Lingxin, and Daniel Naiman. Quantile Regression. SAGE Publications, Inc., 2007. http://dx.doi.org/10.4135/9781412985550.

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Davino, Cristina, Marilena Furno, and Domenico Vistocco. Quantile Regression. John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118752685.

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Marilena, Furno, and Vistocco Domenico. Quantile Regression. John Wiley & Sons Ltd, 2018. http://dx.doi.org/10.1002/9781118863718.

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Hao, Lingxin. Quantile regression. Sage Publications, 2007.

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Firpo, Sergio. Unconditional quantile regressions. National Bureau of Economic Research, 2007.

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Chernozhukov, Victor. Instrumental variable quantile regression. Massachusetts Institute of Technology, Dept. of Economics, 2006.

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Cleophas, Ton J., and Aeilko H. Zwinderman. Quantile Regression in Clinical Research. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82840-0.

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McMillen, Daniel P. Quantile Regression for Spatial Data. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31815-3.

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Fitzenberger, Bernd, Roger Koenker, and José A. F. Machado, eds. Economic Applications of Quantile Regression. Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-662-11592-3.

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Chernozhukov, Victor. Quantile regression with censoring and endogeneity. National Bureau of Economic Research, 2011.

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Book chapters on the topic "Quantity quantile regression"

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Fahrmeir, Ludwig, Thomas Kneib, Stefan Lang, and Brian Marx. "Quantile Regression." In Regression. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34333-9_10.

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Čížek, Pavel. "Quantile Regression." In XploRe® - Application Guide. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57292-0_1.

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Awange, Joseph L., Béla Paláncz, Robert H. Lewis, and Lajos Völgyesi. "Quantile Regression." In Mathematical Geosciences. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-67371-4_12.

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Hooten, Mevin B., and Trevor J. Hefley. "Quantile Regression." In Bringing Bayesian Models to Life. CRC Press, 2019. http://dx.doi.org/10.1201/9780429243653-18.

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Kumar, Sunil. "Quantile Regression." In Python for Accounting and Finance. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54680-8_22.

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Awange, Joseph L., Béla Paláncz, Robert H. Lewis, and Lajos Völgyesi. "Quantile Regression." In Mathematical Geosciences. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-92495-9_13.

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Buchinsky, Moshe. "Quantile Regression." In The New Palgrave Dictionary of Economics. Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_2795.

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Buchinsky, Moshe. "Quantile Regression." In The New Palgrave Dictionary of Economics. Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/978-1-349-95121-5_2795-1.

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Buchinksy, Moshe. "Quantile Regression." In Microeconometrics. Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230280816_25.

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Cleophas, Ton J., and Aeilko H. Zwinderman. "Quantile Regression." In Regression Analysis in Medical Research. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-61394-5_27.

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Conference papers on the topic "Quantity quantile regression"

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Fu, Manxia, and Shuisheng Zhou. "Fused Lasso Additive Quantile Regression." In 2023 International Conference on Computer, Internet of Things and Smart City (CIoTSC). IEEE, 2023. http://dx.doi.org/10.1109/ciotsc60428.2023.00040.

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Shoji, Yoshifumi, and Masahiro Yukawa. "Robust Quantile Regression Under Unreliable Data." In 2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2024. https://doi.org/10.1109/apsipaasc63619.2025.10849248.

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Rueda-Toicen, Antonio, and Allan A. Zea. "Estimating Parcel Delivery Day via Quantile Regression." In 2024 11th IEEE Swiss Conference on Data Science (SDS). IEEE, 2024. http://dx.doi.org/10.1109/sds60720.2024.00014.

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Amnuaypongsa, Worachit, Wijarn Wangdee, and Jitkomut Songsiri. "Probabilistic Solar Power Forecasting Using Multi-Objective Quantile Regression." In 2024 18th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, 2024. http://dx.doi.org/10.1109/pmaps61648.2024.10667174.

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Jiang, Haozhe, Xiangyu Kong, Xiyuan Zhang, Zhengtao Wang, and Mingxing Guo. "Deep Learning-based Quantile Regression for Demand Response Potential Assessment." In 2024 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2024. http://dx.doi.org/10.1109/pesgm51994.2024.10689120.

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Lo, Hsin-Yen, Yuan-Kang Wu, and Wen-Shan Tan. "Weighted Quantile Regression-Based Probabilistic Forecasting for Solar Photovoltaic Systems." In 2024 IEEE Industry Applications Society Annual Meeting (IAS). IEEE, 2024. https://doi.org/10.1109/ias55788.2024.11023678.

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Alsultani, Ameer B., Omer S. Alkhafaf, J. Geoffrey Chase, and Balázs Benyó. "A Multi-Feature Quantile Regression Approach for Insulin Sensitivity Prediction." In 2025 IEEE 19th International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE, 2025. https://doi.org/10.1109/saci66288.2025.11030158.

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Wen, Yuxin, Donna AlHakeem, Paras Mandal, et al. "Performance Evaluation of Probabilistic Methods Based on Bootstrap and Quantile Regression to Quantify PV Power Point Forecast Uncertainty." In 2020 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2020. http://dx.doi.org/10.1109/pesgm41954.2020.9281380.

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Huang, Liqi, Xin Wei, Peikang Zhu, Yun Gao, Mingkai Chen, and Bin Kang. "Federated Quantile Regression over Networks." In 2020 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2020. http://dx.doi.org/10.1109/iwcmc48107.2020.9148186.

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Kevin Michael Brannan and Donald Paul Butcher. "TMDL Development Using Quantile Regression." In TMDL 2010: Watershed Management to Improve Water Quality Proceedings, 14-17 November 2010 Hyatt Regency Baltimore on the Inner Harbor, Baltimore, Maryland USA. American Society of Agricultural and Biological Engineers, 2010. http://dx.doi.org/10.13031/2013.35780.

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Reports on the topic "Quantity quantile regression"

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Carlier, Guillaume, Alfred Galichon, and Victor Chernozhukov. Vector quantile regression. Institute for Fiscal Studies, 2014. http://dx.doi.org/10.1920/wp.cem.2014.4814.

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Lee, Sokbae (Simon), and Le-Yu Chen. Sparse Quantile Regression. The IFS, 2020. http://dx.doi.org/10.1920/wp.cem.2020.3020.

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Firpo, Sergio, Nicole Fortin, and Thomas Lemieux. Unconditional Quantile Regressions. National Bureau of Economic Research, 2007. http://dx.doi.org/10.3386/t0339.

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Chetverikov, Denis, Yukun Liu, and Aleh Tsyvinski. Weighted-Average Quantile Regression. National Bureau of Economic Research, 2022. http://dx.doi.org/10.3386/w30014.

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Graham, Bryan, Jinyong Hahn, Alexandre Poirier, and James Powell. Quantile Regression with Panel Data. National Bureau of Economic Research, 2015. http://dx.doi.org/10.3386/w21034.

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Powell, James L., Alexandre Poirier, Bryan S. Graham, and Jinyong Hahn. Quantile regression with panel data. Institute for Fiscal Studies, 2015. http://dx.doi.org/10.1920/wp.cem.2015.1215.

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Koenker, Roger. Quantile regression 40 years on. The IFS, 2017. http://dx.doi.org/10.1920/wp.cem.2017.3617.

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Chernozhukov, Victor, Tetsuya Kaji, and Ivan Fernandez-Val. Extremal quantile regression: an overview. The IFS, 2017. http://dx.doi.org/10.1920/wp.cem.2017.6517.

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Severini, Thomas A., Elie Tamer, and Tatiana V. Komarova. Quantile uncorrelation and instrumental regressions. Institute for Fiscal Studies, 2010. http://dx.doi.org/10.1920/wp.cem.2010.2610.

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Chernozhukov, Victor, Iván Fernández-Val, and Amanda Kowalski. Quantile Regression with Censoring and Endogeneity. National Bureau of Economic Research, 2011. http://dx.doi.org/10.3386/w16997.

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