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Dissertations / Theses on the topic 'Quantity quantile regression'

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Published: 9 March 2023

Last updated: 31 July 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Galarza, Morales Christian Eduardo 1988. "Quantile regression for mixed-effects models = Regressão quantílica para modelos de efeitos mistos." [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/306681.

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Orientador: Víctor Hugo Lachos Dávila<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação Científica<br>Made available in DSpace on 2018-08-27T06:40:31Z (GMT). No. of bitstreams: 1 GalarzaMorales_ChristianEduardo_M.pdf: 5076076 bytes, checksum: 0967f08c9ad75f9e7f5df339563ef75a (MD5) Previous issue date: 2015<br>Resumo: Os dados longitudinais são frequentemente analisados usando modelos de efeitos mistos normais. Além disso, os métodos de estimação tradicionais baseiam-se em regressão na média da distribuição considerada, o que leva

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12

Sim, Nicholas. "Modeling Quantile Dependence." Thesis, Boston College, 2009. http://hdl.handle.net/2345/2467.

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Thesis advisor: Zhijie Xiao<br>In recent years, quantile regression has achieved increasing prominence as a quantitative method of choice in applied econometric research. The methodology focuses on how the quantile of the dependent variable is influenced by the regressors, thus providing the researcher with much information about variations in the relationship between the covariates. In this dissertation, I consider two quantile regression models where the information set may contain quantiles of the regressors. Such frameworks thus capture the dependence between quantiles - the quantile of th

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13

Waldmann, Elisabeth Anna [Verfasser]. "Bayesian Structured Additive Quantile Regression / Elisabeth Waldmann." München : Verlag Dr. Hut, 2013. http://d-nb.info/1045126268/34.

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14

Aljuaid, Aziz Awadhallah S. "Bayesian quantile regression using flexible likelihood functions." Thesis, University of Leeds, 2017. http://etheses.whiterose.ac.uk/18442/.

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15

Sartore, Luca. "Quantile Regression and Bass Models in Hydrology." Doctoral thesis, Università degli studi di Padova, 2014. http://hdl.handle.net/11577/3423658.

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Spatiotemporal phenomena related to the rainfall measurements can be characterised by statistical models grounded on physical concepts instead of being identified by spatiotemporal patterns based on standard correlations and related analytical tools. This perspective is useful in understanding if the relationships among neighbouring zones and consecutive years are attributable to latent physical mechanisms. Satellite data are used to examine this theory and provide evidence on empirical basis. A recent hydrological theory, which is based on the concept of self-organisation, consists of simpl

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Bonaccolto, Giovanni. "Quantile regression methods in economics and finance." Doctoral thesis, Università degli studi di Padova, 2016. http://hdl.handle.net/11577/3424494.

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In the recent years, quantile regression methods have attracted relevant interest in the statistical and econometric literature. This phenomenon is due to the advantages arising from the quantile regression approach, mainly the robustness of the results and the possibility to analyse several quantiles of a given random variable. Such as features are particularly appealing in the context of economic and financial data, where extreme events assume critical importance. The present thesis is based on quantile regression, with focus on the economic and financial environment. First of all, we propo

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17

Meneghel, Danilevicz Ian. "Robust linear mixed models, alternative methods to quantile regression for panel data, and adaptive LASSO quantile regression with fixed effects." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPAST176.

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La thèse est constituée de trois chapitres. Le premier s'intéresse au lien entre l’exposition à la pollution de l’air et les affections respiratoires chez les enfants et les adolescents. La cohorte comprend 82 individus observés mensuellement pendant 6 mois. Nous proposons un modèle linéaire mixte robuste combiné à une analyse en composantes principales afin de gérer la multicolinéarité entre les covariables et l’impact des observations extrêmes sur les estimations. Le deuxième chapitre analyse des données de panel au moyen de modèles à effets fixes et utilisant différentes fonction de perte.

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18

Tareghian, Reza. "Application of quantile regression in climate change studies." Wiley, 2012. http://hdl.handle.net/1993/9817.

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Climatic change has been observed in many locations and has been seen to have dramatic impact on a wide range of ecosystems. The traditional method to analyse trends in climatic series is regression analysis. Koenker and Bassett (1978) developed a regression-type model for estimating the functional relationship between predictor variables and any quantile in the distribution of the response variable. Quantile regression has received considerable attention in the statistical literature, but less so in the water resources literature. This study aims to apply quantile regression to problems in wa

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19

Aguilar, Fargas Joan. "Prediction interval modeling using Gaussian process quantile regression." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/100361.

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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 62-65).<br>In this thesis a methodology to construct prediction intervals for a generic black-box point forecast model is presented. The prediction intervals are learned from the forecasts of the black-box model and the actual realizations of the forecasted variable by using quantile regression on the observed prediction error distribution, the distribut

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20

Koutsourelis, Antonios. "Bayesian extreme quantile regression for hidden Markov models." Thesis, Brunel University, 2012. http://bura.brunel.ac.uk/handle/2438/7071.

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The main contribution of this thesis is the introduction of Bayesian quantile regression for hidden Markov models, especially when we have to deal with extreme quantile regression analysis, as there is a limited research to inference conditional quantiles for hidden Markov models, under a Bayesian approach. The first objective is to compare Bayesian extreme quantile regression and the classical extreme quantile regression, with the help of simulated data generated by three specific models, which only differ in the error term’s distribution. It is also investigated if and how the error term’s d

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RENZETTI, STEFANO. "THE WEIGHTED QUANTILE SUM REGRESSION: EXTENSIONS AND APPLICATIONS." Doctoral thesis, Università degli Studi di Milano, 2021. http://hdl.handle.net/2434/818694.

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During these last few years an increasing body of scientific evidence showed that looking at the single exposure to chemicals without considering the mixture effect can cause an underestimate of the chemical exposures risk. This poses also statistical challenges on how to manage more complex datasets. Weighted Quantile Sum (WQS) regression is a new statistical model that allows to deal with this problems. It is able to test the association of the overall environmental exposures with an outcome and to find the main actors in the association between the exposure and the dependent variable. Thro

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Bin, Muhd Noor Nik Nooruhafidzi. "Statistical modelling of ECDA data for the prioritisation of defects on buried pipelines." Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/16392.

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Buried pipelines are vulnerable to the threat of corrosion. Hence, they are normally coated with a protective coating to isolate the metal substrate from the surrounding environment with the addition of CP current being applied to the pipeline surface to halt any corrosion activity that might be taking place. With time, this barrier will deteriorate which could potentially lead to corrosion of the pipe. The External Corrosion Direct Assessment (ECDA) methodology was developed with the intention of upholding the structural integrity of pipelines. Above ground indirect inspection techniques such

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23

Taylor, James William. "Predictive distributions, quantile regression and the combination of forecasts." Thesis, London Business School (University of London), 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338470.

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Al-Hamzawi, Rahim Jabbar Thaher. "Prior elicitation and variable selection for bayesian quantile regression." Thesis, Brunel University, 2013. http://bura.brunel.ac.uk/handle/2438/7501.

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Bayesian subset selection suffers from three important difficulties: assigning priors over model space, assigning priors to all components of the regression coefficients vector given a specific model and Bayesian computational efficiency (Chen et al., 1999). These difficulties become more challenging in Bayesian quantile regression framework when one is interested in assigning priors that depend on different quantile levels. The objective of Bayesian quantile regression (BQR), which is a newly proposed tool, is to deal with unknown parameters and model uncertainty in quantile regression (QR).

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Reed, Craig. "Bayesian parameter estimation and variable selection for quantile regression." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/6118.

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The principal goal of this work is to provide efficient algorithms for implementing the Bayesian approach to quantile regression. There are two major obstacles to overcome in order to achieve this. Firstly, it is necessary to specify a suitable likelihood given that the frequentist approach generally avoids such speci cations. Secondly, sampling methods are usually required as analytical expressions for posterior summaries are generally unavailable in closed form regardless of the prior used. The asymmetric Laplace (AL) likelihood is a popular choice and has a direct link to the frequentist pr

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26

Noufaily, Angela. "Parametric quantile regression based on the generalised gamma distribution." Thesis, Open University, 2011. http://oro.open.ac.uk/54496/.

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Quantile regression offers an extension to regression analysis where a modified version of the least squares method allows the fitting of quantiles at every percentile of the data rather than the mean only. Using the well-known three-parameter generalised gamma distribution to model variation in data, we present a parametric quantile regression study for positive univariate reference charts. The study constitutes an overall package that includes all different stages of parametric modeling starting from model identification to parameter estimation, model selection and finally model checking. We

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COLUMBU, SILVIA. "Parametric modeling of dependence of bivariate quantile regression residuals' signs." Doctoral thesis, Università degli Studi di Cagliari, 2015. http://hdl.handle.net/11584/266587.

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In this thesis, we propose a non-parametric method to study the dependence of the quantiles of a multivariate response conditional on a set of covariates. We define a statistic that measures the conditional probability of concordance of the signs of the residuals of the conditional quantiles of each univariate response. The probability of concordance is bounded from below by the value of largest possible negative dependence and from above by that of largest possible positive dependence. The value corresponding to the case of independence is contained in the interior of that interval. We

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Palapelas, Kantola Philip. "Extreme Quantile Estimation of Downlink Radio Channel Quality." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177657.

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The application area of Fifth Generation New Radio (5G-NR) called Ultra-Reliable and Low-Latency Communication (URLLC) requires a reliability, the probability of receiving and decoding a data packet correctly, of 1 - 10^5. For this requirement to be fulfilled in a resource-efficient manner, it is necessary to have a good estimation of extremely low quan- tiles of the channel quality distribution, so that appropriate resources can be distributed to users of the network system. This study proposes and evaluates two methods for estimating extreme quantiles of the downlink channel quality distrib

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Huang, Shui-mei, and 黃秀梅. "quantile regression." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/59580896304481039057.

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Lo, Yi, and 羅驛. "Weighted Quantile Regression." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/31421059248782021412.

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Rodrigues, Sofia Bernardo. "Credit VaR and VaR in credit default swaps." Doctoral thesis, 2013. http://hdl.handle.net/10071/8798.

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JEL Classification: C01, C02<br>This thesis presents two applications of Value at Risk (VaR) estimation: Credit VaR and VaR in Credit Default Swaps (CDS). I compare Credit VaR estimates based on different correlation assumptions, using Gaussian and t copulas, with the observed loss in a credit portfolio of a Portuguese financial institution, for a time series of 72 monthly observations, covering the period between 2004 and 2009. I provide empirical evidence that some of the assumptions made by rating agencies to evaluate CDOs are inadequate in stress situations like the financial crisis

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Reich, BJ, M. Fuentes, and DB Dunson. "Bayesian Spatial Quantile Regression." Thesis, 2011. http://hdl.handle.net/10161/2981.

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Tropospheric ozone is one of the six criteria pollutants regulated by the United States Environmental Protection Agency under the Clean Air Act and has been linked with several adverse health effects, including mortality. Due to the strong dependence on weather conditions, ozone may be sensitive to climate change and there is great interest in studying the potential effect of climate change on ozone, and how this change may affect public health. In this paper we develop a Bayesian spatial model to predict ozone under different meteorological conditions, and use this model to study spatial and

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SABBI, ALBERTO. "Mixed effect quantile and M-quantile regression for spatial data." Doctoral thesis, 2020. http://hdl.handle.net/11573/1456341.

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Observed data are frequently characterized by a spatial dependence; that is the observed values can be influenced by the "geographical" position. In such a context it is possible to assume that the values observed in a given area are similar to those recorded in neighboring areas. Such data is frequently referred to as spatial data and they are frequently met in epidemiological, environmental and social studies, for a discussion see Haining, (1990). Spatial data can be multilevel, with samples being composed of lower level units (population, buildings) nested within higher level units (census

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Lamarche, Carlos Eduardo. "Quantile regression for panel data /." 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3242908.

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Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.<br>Source: Dissertation Abstracts International, Volume: 67-11, Section: A, page: 4289. Adviser: Roger Koenker. Includes bibliographical references (leaves 134-138) Available on microfilm from Pro Quest Information and Learning.

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"Robust Quantile Regression Using L2E." Thesis, 2012. http://hdl.handle.net/1911/70304.

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Quantile regression, a method used to estimate conditional quantiles of a set of data ( X, Y ), was popularized by Koenker and Bassett (1978). For a particular quantile q , the q th quantile estimate of Y given X = x can be found using an asymmetrically-weighted, absolute-loss criteria. This form of regression is considered to be robust, in that it is less affected by outliers in the data set than least-squares regression. However, like standard L 1 regression, this form of quantile regression can still be affected by multiple outliers. In this thesis, we propose a method for improving robustn

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吳國傑. "Penalized Estimation for Quantile Regression." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/09966629602473364803.

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碩士<br>國立清華大學<br>統計學研究所<br>99<br>Quantile regression (QR) describes the relationship between the response variable and the exploratory variables through some specific quantiles, which has been applied to a wide range of data and different fields. Under the linear model assumption, Zou and Yuan (2008) proposed the composite quantile regression (CQR) to incorporate several quantiles at a time in the estimation function. In theory, CQR has better estimation precision when the linear model assumption holds, but it is not adequate and biased when the assumption is wrong. Without the linear model ass

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Yan, Yin-Jhen, and 顏吟真. "Geographically Weighted Autoregressive Quantile Regression." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/23202505988854401388.

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碩士<br>淡江大學<br>統計學系碩士班<br>100<br>Geographically Weighted Regression (GWR; Brunsdon et al., 1998) and Quantile Regression (QR; Koenker and Bassett, 1978) are two important tools respectively in geography and econometrics in analyzing various issues of empirical studies. The former is designed to explore spatial nonstationarity and the latter is constructed to model relationships among variables across the whole distribution of a dependent variable. While both of these methods have been widely used in literature, they seem to be two unconnected lines of knowledge inquiry until recently (Chen et a

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Chiang, Chih-Lun, and 姜智倫. "Decomposition of Gender Discrimination: Quantile Regression." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/62ey25.

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碩士<br>國立暨南國際大學<br>經濟學系<br>96<br>This research simultaneously uses the quantile regression model which is recently developed and the traditional mean regression model to estimate the male and female wage function. And penetrating the different models of decomposition of gender difference, discusses the marginal return difference, the discrimination degree change and the cross time tendency of men and women employed under the different wage level in Taiwan. Aside from the well-developed ordinary least square analyze in the existing literature. Three conclusions are found in this paper. First

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Wong, Jia-Cong, and 翁嘉聰. "Bayesian Asymmetric Causality in Quantile Regression." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/49345125503369074425.

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碩士<br>逢甲大學<br>統計與精算所<br>97<br>The purpose of this thesis is to propose a nonlinear Granger-causality test over a range of quantile levels in financial market. We consider quantile regression with heteroskedastic errors to discuss causal relation between futures and stock returns, while the traditional regression model cannot fully obtain the behavior of extreme values. To investigate the linkage for international stock markets, the proposed causality test includes three features: asymmetry, heteroskedasticity and quantile causal effect. We use Bayesian Markov chain Monte Carlo methods to inves

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Chang, Ting-Wei, and 張庭威. "Gibrat''s Law：Application of Quantile Regression." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/11358072765132021735.

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碩士<br>淡江大學<br>產業經濟學系碩士班<br>94<br>Gibrat’s Law indicates that the growth rate of a given firm is independent of its size. In the literature, both supporting and opposing opinions coexist. Scholars investigating firms exceeding the minimum scale tended to agree with Gibrat’s Law; for example, Hart and Prais (1956). In contrast, scholars investigating small firms tended to disagree with Gibrat’s Law; for example, Dunne and Hughes (1994). Recently, Lotti et al. (2003) analyzed the data of Italian manufacturing firms over the period from 1987 to 1993 and used quantile regression techniques to test

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Wang, Chu-Chun, and 王筑羣. "Test of CAPM by Quantile Regression." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/46159136613787070441.

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碩士<br>淡江大學<br>財務金融學系碩士班<br>94<br>This paper proposes that if we consider the real circumstance of market which may not be the high risky high return and the assumed model is under the non-linear state, how we can do the analysis and measurement for the CAPM? We select the monthly data from July of 1926 to August of 2005, and try to use the model of Fama and MacBeth (1973) as the basis in this text. We also use the method of Quantile Regression to discuss the relationship between the market risk of investment portfolio and the rate of return, in addition, we can identify the assumption of the p

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Ayilara, Olawale Fatai. "Quantile regression with rank-based samples." 2016. http://hdl.handle.net/1993/31918.

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Quantile Regression, as introduced by Koenker, R. and Bassett, G. (1978), provides a complete picture of the relationship between the response variable and covariates by estimating a family of conditional quantile functions. Also, it offers a natural solution to challenges such as; homoscedasticity and sometimes unrealistic normality assumption in the usual conditional mean regression. Most of the results for quantile regression are based on simple random sampling (SRS). In this thesis, we study the quantile regression with rank-based sampling methods. Rank-based sampling methods have a

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吳晉輝. "Quantile Regression for Censored Cost Data." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/7e95q9.

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碩士<br>國立嘉義大學<br>農藝學系研究所<br>106<br>Because cost responses are generally skewed to the right, this paper proposes to model the quantile of cost to handle covariate information. Cost data usually have the problem of induced informative censoring when some subjects are not traced until the endpoint of study so that their total costs are observed incompletely. Due to induced informative censoring, we use an inverse probability of censoring weighted (IPCW) estimating equation to obtain regression coefficients under the quantile model. The perturbation resampling method is employed to estimate the st

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LIN, TZU-LING, and 林姿伶. "Insurance and Economic Growth: Quantile Regression." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/zhqa6a.

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博士<br>逢甲大學<br>金融博士學位學程<br>106<br>This study examines the long-run equilibrium and short-term causality between insurance market development and economic growth. The study group includes the seven major industrial countries (Group of 7, G7). The sample period is from 1980 to 2014. The study divides the development of the insurance market into life insurance, property insurance, and total insurance. It examines insurance density, insurance penetration and insurance premium income in Direct US Dollars. This study not only considers the long-term relationship between insurance demand and gross dom

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Chu, Wei-Chieh, and 朱韋杰. "Panel Data Quantile Regression with Endogeneity." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/84hjd7.

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Ling, Wodan. "Quantile regression for zero-inflated outcomes." Thesis, 2019. https://doi.org/10.7916/d8-rre7-sw52.

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Zero-inflated outcomes are common in biomedical studies, where the excessive zeros indicate some special but undetectable events. Quantile regression is potentially advantageous in analyzing zero-inflated outcomes due to two reasons. First, compared to parametric models such as the zero-inflated Poisson and two-part model, quantile regression gives robust and accurate estimation by avoiding likelihood specification and can capture the tail events and heterogeneity over the outcome distribution. Second, while the mean-based regression may be misinterpreted for a zero-inflated outcome, the inter

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Antunes, Manuel António Marques. "Regional innovation heterogeneity in Europe : a quantile regression analysis." Master's thesis, 2016. http://hdl.handle.net/10400.14/21746.

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O propósito desta tese é o de analisar a existência de heterogeneidade a nível regional em termos de comportamento de inovação entre Regiões Europeias. Foram estimados regressões de quantis com uma variável dependente discreta, uma avançada ferramenta econométrica indicada para analises empíricas evolucionistas, o que representa uma novidade na área de inovação regional. Aplicando quantis a uma contagem de amostra de 67 regiões Europeias, e usando quarto quantis, concluímos uma evidência de heterogeneidade. A heterogeneidade existe em termos de performance de inovação regional e entre os fator

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MERLO, LUCA. "On quantile regression models for multivariate data." Doctoral thesis, 2022. http://hdl.handle.net/11573/1613037.

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The goal of this thesis is to bridge the gap between univariate and multivariate quantiles by extending the study of univariate quantile regression and its generalizations to multivariate responses. The statistical analysis focuses on a multivariate framework where we consider vector-valued quantile functions associated with multivariate distributions, providing inferential procedures and establishing the asymptotic properties of the proposed estimators. We illustrate their applicability in a wide variety of scientific settings, including time series, longitudinal and clustered data. The disse

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Tsai, Min-Jen, and 蔡敏仁. "Internationalization and Wage Inequality: Quantile Regression Analysis." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/x42sza.

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碩士<br>國立暨南國際大學<br>經濟學系<br>96<br>The purpose of this study is to estimate skilled and unskilled male wage inequality of Taiwanese manufacturing focusing on globalization covariates, foreign trade and outward direct investment in particular, from 1989 to 2006. Quantile regression model and traditional ordinary least square regression model are used to address the issues with the combined data from Manpower Utilization Survey, Trade Data and Outward Investment Data in Taiwan. The empirical findings suggest that the effect of internationalization on wage inequality varies across different developm

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Wang, Yini. "Three Essays on Time Series Quantile Regression." Thesis, 2012. http://hdl.handle.net/1974/7340.

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This dissertation considers quantile regression models with nonstationary or nearly nonstationary time series. The first chapter outlines the thesis and discusses its theoretical and empirical contributions. The second chapter studies inference in quantile regressions with cointegrated variables allowing for multiple structural changes. The unknown break dates and regression coefficients are estimated jointly and consistently. The conditional quantile estimator has a nonstandard limit distribution. A fully modified estimator is proposed to remove the second-order bias and nuisance parameters a

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