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Academic literature on the topic 'Quantity quantile regression'

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Published: 9 March 2023
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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 (October 1, 2018): 439–54. http://dx.doi.org/10.1007/s10198-018-1009-8.

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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 (November 1, 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. Previous work has examined the tilted funnel by means of frequentist quantile regression, but Bayesian quantile regression based on the asymmetric Laplace model allows for only one conditional quantile at a time. Hence, we propose additional Bayesian methods, including Poisson modeling to study conditional variance as a function of quantity. We use a classical small sample of eminent neurosurgeons, as well as the brms Bayesian R package, to accomplish this work. In addition, we provide open code and data to allow interested researchers to extend our work and utilize the proposed modeling alternatives.
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Carreño, Pia, and Andres Silva. "Fruit and vegetable expenditure disparities: evidence from Chile." British Food Journal 121, no. 6 (June 20, 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 relevant role in explaining the smaller gap in terms of purchase quantity. Research limitations/implications Traditional markets help low-income households access low-cost FVs. Social implications The authors generate evidence to show that traditional markets play a relevant role to supply affordable FV to low-income households. Originality/value The paper used a high-quality and uncommon data set. It is a topic of high social impact.
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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 (May 14, 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 regarding to the functional relationship between one dependent variable with one or more independent variables which can provide accurate and stable results even though there will be outliers. Based on the result of the research, it is obtained that the natural condition factor affected the spread of DBD. This is because from 3 natural conditions only 11 significant or influential quantiles on the tested data, the quantiles are 0,30; 0,35; 0,40; 0,45; 0,50; 0,55; 0,60; 0,65; 0,70; 0,75 and 0,80. Meanwhile the most influential factor of natural conditions in spreading DBD is the frequency of rainy day because it has positive which means that 1 progress of percentage will increase the quantity of DBD case.
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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 (February 25, 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, price and income elasticities are estimated, confirming that the majority of food items are inelastic with respect to price and income except for meat. These findings can be used as a basis for considering food policy implications while evaluating the potential gains from applying specific policies.
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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 (December 30, 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 technological innovation (TI) on quantity and quality of economic growth by employing different panel estimation models over the period 2009-2019 and check the robustness in five ways. Finally, this paper investigates the TI-economic growth link based on the panel quantile regression across the conditional distributions of economic growth levels. Results show that TI has significantly positive effects in terms of both quantity and quality of economic growth, and the promoting effect on the quantity of economic growth is almost four times higher than that of quality under mean estimations by double fixed-effects. The effect on quantity of economic growth is also stronger than that of quality under the conditional distribution, and TI has a stronger impact for regions with higher levels of economic growth.
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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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Shaikh, Imlak. "The Relation between Implied Volatility Index and Crude Oil Prices." Engineering Economics 30, no. 5 (December 14, 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 commodity on each trading day. The neural network estimates explain that future prices of the WTI/USO can be predicted with minimal error, and similar can be used to predict future volatility. The quantile regression results suggest that crude oil prices and OVX are strongly associated. The asymmetric association between the WTI/USO and OVX explains that the volatility feedback effect holds good for the OVX market. Bai and Perron least squares estimate evidence of the presence of a break in the time series. The main results uncover several interesting facts that implied volatility tends to remain calm during the global financial crises and higher throughout the post crisis period. The empirical outcome on the OVX market provides some practical implications for the trader and investor, in which oil futures can serve better to hedge the crude price volatility. The crude oil producer can short hedge enough through volatility futures and options to maintain the future quantity of crude to be produced.
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Lipovetsky, Stan. "Quantile Regression." Technometrics 48, no. 3 (August 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 (December 1, 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 problem. Defining the sample quantiles as a solution of a minimization problem, quantile regression, introduced by Koenker and Bassett (1978) provides an extension of this methodology in order to obtain regression coefficients of the hyperplane for a generic quantile of the dependent variable.We introduce quantile regression showing that the use of different loss functions: quadratic, absolute and asymmetric absolute leads respectively to least squares, median and quantile regression. In this thesis we extend these results to the linear regression for quantity quantiles. We first show that quantity quantiles can be defined as the solution to a minimization problem and then we extend the result to the linear regression framework. We finally deal with another use of absolute values in the regression context, in particular we consider the problem of the estimation of the regression coefficients by minimizing the Gini mean difference of the residuals; we show that this apporach fall in the class of R-estimators.
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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
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 que grupos de indivíduos com diferentes valores de rendimento podem ter comportamentos distintos. Para tal, foram utilizados dados provenientes do Instituto Nacional de Estatística (INE) que permitiram construir o modelo estimado. A variável em estudo é o rendimento anual dos residentes em Portugal, no ano de 2019, e o modelo conta com oito regressores que caracterizam não só o indivíduo, incluindo, nomeadamente, a sua idade, sexo ou estado civil, mas também a sua instituição empregadora, incluindo variáveis como a dimensão, número de horas de trabalho, entre outras. Com o desenvolvimento do projeto e tendo em conta a análise aos resultados da estimação, é possível concluir que existem fatores, nomeadamente o género, nível de educação e região onde o indivíduo reside, responsáveis pela diferença significativa no valor do rendimento anual dos residentes em Portugal. No entanto, esta diferença não é uniforme para todos os grupos de indivíduos e comporta-se de maneira diferente quando comparados grupos de indivíduos com rendimentos mais baixos, médios ou altos. Este comportamento não linear permitiu ainda compreender a vantagem da utilização do método de regressão de quantis face ao método econométrico mais comum, a regressão linear, cujo objetivo é estimar o efeito das diferentes variáveis explicativas nos valores médios da variável dependente. A base de dados utilizada foi construída utilizando o software SQL Developer e a análise foi conduzida com recurso ao Stata.
Despite the fact that, over the years, there has been a significant decrease in income inequality, this issue is still a subject under study, mainly in an econometric approach, with the aim of studying and understanding the factors behind those inequalities. The main focus of this project is to identify and study the factors that determine the income of individuals living in Portugal, adopting a quantile regression approach, since individuals with different wages may have different behaviors. For this purpose, a regression model was created, using data from Statistics Portugal. The variable under study is the annual income of residents in Portugal, in 2019, and the model has several regressors that not only characterize the individual, such as their age, sex or marital status, but also the company, such as their dimension and number of working hours. With the development of this project and taking into account the estimation results, it is possible to conclude that there are factors, namely the individual's gender, level of education and region where he lives, responsible for the significant difference in the value of the annual income of residents in Portugal. However, these differences are not uniform for all groups of individuals, since there is a different behavior when comparing groups of individuals with lower, medium or high income. This nonlinear behavior also allowed to understand the advantage of using quantile regression over the most common econometric method, linear regression, whose objective is to estimate the effect of different explanatory variables on the average values of the dependent variable. The database used was built using SQL Developer and the analysis was conducted with software Stata.
info:eu-repo/semantics/publishedVersion
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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 maximalen Abweichungen sup06x61 |v_n(x) − v(x)|. Nach Vorbild der asymptotischen Theorie konstruieren wir simultane Konfidenzb änder um die geschätzte Expektilfunktion. Wir entwickeln einen funktionalen Datenanalyseansatz um eine Familie von generalisierten Quantilregressionen gemeinsam zu schätzen. Dabei gehen wir in unserem Ansatz davon aus, dass die generalisierten Quantile einige gemeinsame Merkmale teilen, welche durch eine geringe Anzahl von Hauptkomponenten zusammengefasst werden können. Die Hauptkomponenten sind als Splinefunktionen modelliert und werden durch Minimierung eines penalisierten asymmetrischen Verlustmaßes gesch¨atzt. Zur Berechnung wird ein iterativ gewichteter Kleinste-Quadrate-Algorithmus entwickelt. Während die separate Schätzung von individuell generalisierten Quantilregressionen normalerweise unter großer Variablit¨at durch fehlende Daten leidet, verbessert unser Ansatz der gemeinsamen Schätzung die Effizienz signifikant. Dies haben wir in einer Simulationsstudie demonstriert. Unsere vorgeschlagene Methode haben wir auf einen Datensatz von 150 Wetterstationen in China angewendet, um die generalisierten Quantilkurven der Volatilität der Temperatur von diesen Stationen zu erhalten
Generalized quantile regressions, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We denote $v_n(x)$ as the kernel smoothing estimator of the expectile curves. We prove the strong uniform consistency rate of $v_{n}(x)$ under general conditions. Moreover, using strong approximations of the empirical process and extreme value theory, we consider the asymptotic maximal deviation $\sup_{ 0 \leqslant x \leqslant 1 }|v_n(x)-v(x)|$. According to the asymptotic theory, we construct simultaneous confidence bands around the estimated expectile function. We develop a functional data analysis approach to jointly estimate a family of generalized quantile regressions. Our approach assumes that the generalized quantiles share some common features that can be summarized by a small number of principal components functions. The principal components are modeled as spline functions and are estimated by minimizing a penalized asymmetric loss measure. An iteratively reweighted least squares algorithm is developed for computation. While separate estimation of individual generalized quantile regressions usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 150 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations
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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 bandwidth selection. In Chapter 3, a comparison is carried out of local constant fitting and local linear fitting using MSEs of the estimates as a criterion. Chapter 4 gives a theoretical summary and a simulation study of local linear kernel estimation of conditional distribution function. This has a special interest in itself as well as being related to regression quantiles. In Chapter 5, a kernel-version method of LMS (Cole and Green, 1992) is considered. The method proposed, which is still a semi-parametric one, is based on a general idea of local linear kernel approach of log-likelihood model. Chapter 6 proposes a two-step method of smoothing regression quantiles called BPK. The method considered is based on the idea of combining k- NN method with Healy's et al (1988) partition rule, and correlated regression model are involved. In Chapter 7, methods of regression quantile estimation are compared for different underlying models and design densities in a simulation study. The ISE criterion of interior and boundary points is used as a basis for these comparisons. Three methods are recommended for quantile regression in practice, and they are double kernel method, LMS method and Box partition kernel method (BPK). In Chapter 8, attention is turned to a novel idea of local polynomial roughness penalty regression model, where a purely theoretical framework is considered.
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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 associated screening level. An empirical application for the USFS timber auctions suggests an optimal reservation price policy with a probability of selling the good as low as 58% for some auctions with two bidders. The second Chapter tries to address this issue by considering a risk averse seller with a CRRA utility function. A numerical exercise based on the USFS timber auctions shows that increasing the CRRA of the sellers is su cient to give more reasonable policy recommendations and a higher probability of selling the auctioned timber lot. The third Chapter develops a quantile regression methodology for rst-price auction. The estimation method combines local polynomial, quantile regression and additive sieve methods. It is shown in addition that the new quantile regression methodology is not subject to boundary issues. The choice of smoothing parameters is also discussed.
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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 inefficient and efficient firms at lower quantiles of the conditional distribution function are from the coefficient (85 percent of the total effect) and the residual effects (25 percent) and at higher quantiles from the coefficient (68 percent) and the regressor effects (22 percent). The results from the Monte Carlo simulations in chapter 4 show that under the correctly assumed stochastic frontier models, the probabilistic frontiers can have the lowest bias and mean squared error of the efficiency estimates. When outliers or location-scale shift effects are included, more preference is towards the probabilistic frontiers. The nonparametric probabilistic frontiers are nearly always preferable to Data Envelopment Analysis and Free Disposable Hull. In chapter 5, a fixed effects quantile regression estimator is used to estimate a cost frontier and efficiency levels for a panel dataset of English NHS Trusts. Waiting times elasticities are estimated from -0.14 to 0.17 in the cross-sectional models and -0.008 to 0.03 in the panel models. Cost minimisation ranged from 33 to 60 days in the cross-sectional model and from 37 to 54 days in the panel model. The results show that the effects of the inputs and control variables vary depending on the efficiency of the Trusts. The efficiency estimates reveal very different conclusions depending on the model choice.
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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 wirtschaftliche Konzepte zur Messung der Effektivit ̈at einer Regierungspoli- tik oder einer medizinischen Behandlung sind. Die Entwicklung eines Verfahrens zur Quantilsregression stellt jedoch eine gro ̈ßere Herausforderung dar, als die Regression zur Mitte. Allgemeine Regressionsprobleme und M-Scha ̈tzer erfordern einen versierten Umgang und es muss sich mit nicht- glatten Verlustfunktionen besch ̈aftigt werden. Kapitel 2 behandelt den Einsatz der Quantilsregression im empirischen Risikomanagement w ̈ahrend einer Finanzkrise. Kapitel 3 und 4 befassen sich mit dem Problem der h ̈oheren Dimensionalit ̈at und nichtparametrischen Techniken der Quantilsregression.
Quantile regression studies the conditional quantile function QY|X(τ) on X at level τ which satisfies FY |X QY |X (τ ) = τ , where FY |X is the conditional CDF of Y given X, ∀τ ∈ (0,1). Quantile regression allows for a closer inspection of the conditional distribution beyond the conditional moments. This technique is par- ticularly useful in, for example, the Value-at-Risk (VaR) which the Basel accords (2011) require all banks to report, or the ”quantile treatment effect” and ”condi- tional stochastic dominance (CSD)” which are economic concepts in measuring the effectiveness of a government policy or a medical treatment. Given its value of applicability, to develop the technique of quantile regression is, however, more challenging than mean regression. It is necessary to be adept with general regression problems and M-estimators; additionally one needs to deal with non-smooth loss functions. In this dissertation, chapter 2 is devoted to empirical risk management during financial crises using quantile regression. Chapter 3 and 4 address the issue of high-dimensionality and the nonparametric technique of quantile regression.
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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 capturing different forms of deterministic and/or stochastic seasonal patterns. Given a variable of interest, by describing its seasonal behaviour over an approximation of the entire conditional distribution, we are capable of removing seasonal patterns affecting the mean and/or the variance, or seasonal patterns varying over quantiles of the conditional distribution. In the first part of this work, we provide a proposed approach to deal with the deterministic seasonal pattern cases. We provide empirical examples based on simulated and real data where we compare our proposal to least-squares approaches. The results are in favour of the proposed approach in case if the seasonal patterns change across quantiles. In the second part of this work, we improve the proposed approach flexibly to account for the essential effect of the structural breaks in the time series. Again, we compare the proposed methods to segmented-least squares and provide several empirical examples based on simulated and real data that are affected by both the structural breaks and seasonal patterns. The results, in case of stochastic periodic behaviour, are in favour of the proposed approaches especially when the seasonal patterns change across quantiles.
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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 show that QR curves crossing lead to invalid distributions for the response. In particular, given a set of covariates, it may turn out, for example, that the predicted 95th percentile of the response is smaller than the 90th percentile for some values of the covariates. Third, mean-based clustering methods are widely developed, but need improvements to deal with clustering extreme-type, heavy tailed-type or outliers problems. This thesis focuses on methods developed over these three challenges: modelling quantile regression with discrete responses, ensuring non-crossing quantile curves for any given sample and modelling tails for collinear data with outliers. The main contributions are listed as below: * The first challenge is studied in Chapter 2, in which a general method for Bayesian inference of regression models beyond the mean with discrete responses is developed. In particular, this method is developed for both Bayesian quantile regression and Bayesian expectile regression. This method provides a direct Bayesian approach to these regression models with a simple and intuitive interpretation of the regression results. The posterior distribution under this approach is shown to not only be coherent to the response variable, irrespective of its true distribution, but also proper in relation to improper priors for unknown model parameters. * Chapter 3 investigates a new kernel-weighted likelihood smoothing quantile regression method. The likelihood is based on a normal scale-mixture representation of an asymmetric Laplace distribution (ALD). This approach benefits of the same good design adaptation just as the local quantile regression (Spokoiny et al., 2014) does and ensures non-crossing quantile curves for any given sample. * In Chapter 4, we introduce an asymmetric Laplace distribution to model the response variable using profile regression, a Bayesian non-parametric model for clustering responses and covariates simultaneously. This development allows us to model more accurately for clusters which are asymmetric and predict more accurately for extreme values of the response variable and/or outliers. In addition to the three major aforementioned challenges, this thesis also addresses other important issues such as smoothing extreme quantile curves and avoiding insensitive to heteroscedastic errors as well as outliers in the response variable. The performances of all the three developments are evaluated via both simulation studies and real data analysis.
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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 of simulated data in which various error structures are imposed including homoscedastic and heteroscedastic structures were developed. Methods from the statistical literature were then compared with a method proposed within this thesis based on the idea of Silverman's (1986) kernel smoothing. This proposed bootstrapping method requires the estimation of the conditional variance function of the fitted quantile. The performance of a variety of variance estimation methods combined within the proposed bootstrapping procedure are assessed under various data structures in order to examine the performance of the proposed bootstrapping approach. The validity of the proposed bootstrapping method is then illustrated using the Saudi Arabian anthropometric data.
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Books on the topic "Quantity quantile regression"

1

Hao, Lingxin, and Daniel Naiman. Quantile Regression. 2455 Teller Road, Thousand Oaks California 91320 United States of America: SAGE Publications, Inc., 2007. http://dx.doi.org/10.4135/9781412985550.

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

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

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Hao, Lingxin. Quantile regression. Thousand Oaks, Calif: Sage Publications, 2007.

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

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

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Cleophas, Ton J., and Aeilko H. Zwinderman. Quantile Regression in Clinical Research. Cham: 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. Berlin, Heidelberg: 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. Heidelberg: 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. Cambridge, MA: National Bureau of Economic Research, 2011.

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Book chapters on the topic "Quantity quantile regression"

1

Fahrmeir, Ludwig, Thomas Kneib, Stefan Lang, and Brian Marx. "Quantile Regression." In Regression, 597–620. Berlin, Heidelberg: 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, 19–48. Berlin, Heidelberg: 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, 359–404. Cham: 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, 205–20. Boca Raton, FL : CRC Press, Taylor & Francis Group, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429243653-18.

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Buchinsky, Moshe. "Quantile Regression." In The New Palgrave Dictionary of Economics, 11065–73. London: 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, 1–9. London: 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, 202–13. London: 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, 453–67. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-61394-5_27.

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Jurečková, Jana. "Regression Quantile and Averaged Regression Quantile Processes." In Analytical Methods in Statistics, 53–62. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51313-3_3.

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Kohn, Wolfgang, and Riza Öztürk. "Quantils-Regression." In Springer-Lehrbuch, 337–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-50442-0_31.

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Conference papers on the topic "Quantity quantile regression"

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Wen, Yuxin, Donna AlHakeem, Paras Mandal, Shantanu Chakraborty, Yuan-Kang Wu, Tomonobu Senjyu, Sumit Paudyal, and Tzu-Liang Tseng. "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. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2010. http://dx.doi.org/10.13031/2013.35780.

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Bhat, Harish S., Nitesh Kumar, and Garnet J. Vaz. "Towards scalable quantile regression trees." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363741.

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Natesan Ramamurthy, Karthikeyan, Kush R. Varshney, and Moninder Singh. "Quantile regression for workforce analytics." In 2013 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2013. http://dx.doi.org/10.1109/globalsip.2013.6737097.

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Fagundes, Roberta A. A., Renata M. C. R. de Souza, and Yanne M. G. Soares. "Quantile regression of interval-valued data." In 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 2016. http://dx.doi.org/10.1109/icpr.2016.7900025.

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Ballings, Michel, Dries Benoit, and Dirk Van den Poel. "RFM Variables Revisited Using Quantile Regression." In 2011 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2011. http://dx.doi.org/10.1109/icdmw.2011.148.

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Dichandra, D., I. Fithriani, and S. Nurrohmah. "Parameter estimation of Bayesian quantile regression." In PROCEEDINGS OF THE 6TH INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES 2020 (ISCPMS 2020). AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0059103.

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Zhou Lihui. "Quantile regression model and application profile." In 2010 International Conference on Computer Application and System Modeling (ICCASM 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccasm.2010.5622905.

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de Oliveira, Augusto Born, Sebastian Fischmeister, Amer Diwan, Matthias Hauswirth, and Peter F. Sweeney. "Why you should care about quantile regression." In the eighteenth international conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2451116.2451140.

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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, December 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, June 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. Cambridge, MA: National Bureau of Economic Research, July 2007. http://dx.doi.org/10.3386/t0339.

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Chetverikov, Denis, Yukun Liu, and Aleh Tsyvinski. Weighted-Average Quantile Regression. Cambridge, MA: National Bureau of Economic Research, May 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. Cambridge, MA: National Bureau of Economic Research, March 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, March 2015. http://dx.doi.org/10.1920/wp.cem.2015.1215.

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Koenker, Roger. Quantile regression 40 years on. The IFS, August 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, December 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, September 2010. http://dx.doi.org/10.1920/wp.cem.2010.2610.

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Dagli, Suzette, Paul Mariano, and Arjan Paulo Salvanera. Quantile Debt Fan Charts. Asian Development Bank, June 2022. http://dx.doi.org/10.22617/wps220242-2.

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This paper presents debt fan charts constructed using the quantile regression approach for nine developing member countries of ADB. Macroeconomic and fiscal determinants of debt are forecasted using quantile regression and the resulting projections are shown in the fan charts for India, Indonesia, Kazakhstan, Malaysia, the People’s Republic of China, the Philippines, the Republic of Korea, Sri Lanka, and Thailand. Furthermore, the fan charts present the uncertainty in the path of debt, especially in the aftermath of the COVID-19 pandemic.
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