Relevant bibliographies by topics / M-quantile regression

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Academic literature on the topic 'M-quantile regression'

Author: Grafiati
Published: 22 February 2023

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Journal articles on the topic "M-quantile regression"

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Otto-Sobotka, Fabian, Nicola Salvati, Maria Giovanna Ranalli, and Thomas Kneib. "Adaptive semiparametric M-quantile regression." Econometrics and Statistics 11 (July 2019): 116–29. http://dx.doi.org/10.1016/j.ecosta.2019.03.001.

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Alfò, Marco, Nicola Salvati, and M. Giovanna Ranallli. "Finite mixtures of quantile and M-quantile regression models." Statistics and Computing 27, no. 2 (2016): 547–70. http://dx.doi.org/10.1007/s11222-016-9638-1.

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Dreassi, Emanuela, M. Giovanna Ranalli, and Nicola Salvati. "Semiparametric M-quantile regression for count data." Statistical Methods in Medical Research 23, no. 6 (2014): 591–610. http://dx.doi.org/10.1177/0962280214536636.

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Borgoni, Riccardo, Paola Del Bianco, Nicola Salvati, Timo Schmid, and Nikos Tzavidis. "Modelling the distribution of health-related quality of life of advanced melanoma patients in a longitudinal multi-centre clinical trial using M-quantile random effects regression." Statistical Methods in Medical Research 27, no. 2 (2016): 549–63. http://dx.doi.org/10.1177/0962280216636651.

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Health-related quality of life assessment is important in the clinical evaluation of patients with metastatic disease that may offer useful information in understanding the clinical effectiveness of a treatment. To assess if a set of explicative variables impacts on the health-related quality of life, regression models are routinely adopted. However, the interest of researchers may be focussed on modelling other parts (e.g. quantiles) of this conditional distribution. In this paper, we present an approach based on quantile and M-quantile regression to achieve this goal. We applied the methodologies to a prospective, randomized, multi-centre clinical trial. In order to take into account the hierarchical nature of the data we extended the M-quantile regression model to a three-level random effects specification and estimated it by maximum likelihood.
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Shim, Joo-Yong, and Chang-Ha Hwang. "M-quantile kernel regression for small area estimation." Journal of the Korean Data and Information Science Society 23, no. 4 (2012): 749–56. http://dx.doi.org/10.7465/jkdi.2012.23.4.749.

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Merlo, Luca, Lea Petrella, Nicola Salvati, and Nikos Tzavidis. "Marginal M-quantile regression for multivariate dependent data." Computational Statistics & Data Analysis 173 (September 2022): 107500. http://dx.doi.org/10.1016/j.csda.2022.107500.

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Moreno, Justo De Jorge, and Oscar Rojas Carrasco. "EVOLUTION OF EFFICIENCY AND ITS DETERMINANTS IN THE RETAIL SECTOR IN SPAIN: NEW EVIDENCE." Journal of Business Economics and Management 16, no. 1 (2014): 244–60. http://dx.doi.org/10.3846/16111699.2012.732958.

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The purpose of this work is twofold: on the one hand, recent methodologies will be used to estimate technical efficiency and its determinants factors in Spain's retail sector. In particular, the order-m approach, which is based on the concept of expected minimum input function and quantile regression, for the analysis of the factors determinants of efficiency is used. On the other hand, the results obtained applying the methods mentioned in the Spanish retail sector can contribute to opening up a new field of analysis since the results may be compared by means of the methodologies proposed as well as those which already exist in the literature. The paper used data envelopment analysis stochastic (order-m) to measure efficiency and quantile regression analysis for the second stage in Spanish retail. For the second stage of analysis relative of the factors determinants of efficiency, we use quantile regression. We take account of heterogeneity between the different characteristics of firms, using quantile regression techniques. We find that firm size, age and market concentration are positively related to the efficiency along the quantiles considered in the analysis. The relationship between intensity of capital and better trained employees in the efficiency shows a curvilinear behavior. Also, there are significant differences by region to which the firm belongs. The main contribution of this paper is to provide an efficiency analysis for Spanish retail sector using a non parametric approach with a robust estimator and quantile regression analysis for second stage. This methodology allows for a more careful analysis of what happens at firm level.
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Vinciotti, Veronica, and Keming Yu. "M-quantile Regression Analysis of Temporal Gene Expression Data." Statistical Applications in Genetics and Molecular Biology 8, no. 1 (2009): 1–20. http://dx.doi.org/10.2202/1544-6115.1452.

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Salvati, N., N. Tzavidis, M. Pratesi, and R. Chambers. "Small area estimation via M-quantile geographically weighted regression." TEST 21, no. 1 (2010): 1–28. http://dx.doi.org/10.1007/s11749-010-0231-1.

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Hall, Peter, and Joel L. Horowitz. "Bandwidth Selection in Semiparametric Estimation of Censored Linear Regression Models." Econometric Theory 6, no. 2 (1990): 123–50. http://dx.doi.org/10.1017/s0266466600005089.

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Quantile and semiparametric M estimation are methods for estimating a censored linear regression model without assuming that the distribution of the random component of the model belongs to a known parametric family. Both methods require estimating derivatives of the unknown cumulative distribution function of the random component. The derivatives can be estimated consistently using kernel estimators in the case of quantile estimation and finite difference quotients in the case of semiparametric M estimation. However, the resulting estimates of derivatives, as well as parameter estimates and inferences that depend on the derivatives, can be highly sensitive to the choice of the kernel and finite difference bandwidths. This paper discusses the theory of asymptotically optimal bandwidths for kernel and difference quotient estimation of the derivatives required for quantile and semiparametric M estimation, respectively. We do not present a fully automatic method for bandwidth selection.
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Dissertations / Theses on the topic "M-quantile regression"

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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.<br>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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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 tracts, municipalities, regions) in a geographical area. Green and Richardson (2002) proposed a general approach to modelling spatial data based on finite mixtures with spatial constraints, where the prior probabilities are modelled through a Markov Random Field (MRF) via a Potts representation (Kindermann and Snell, 1999, Strauss, 1977). This model was defined in a Bayesian context, assuming that the interaction parameter for the Potts model is fixed over the entire analyzed region. Geman and Geman (1984) have shown that this class process can be modelled by a Markov Random Field (MRF). As proved by the Hammersley-Clifford theorem, modelling the process through a MRF is equivalent to using a Gibbs distribution for the membership vector. In other words, the spatial dependence between component indicators is captured by a Gibbs distribution, using a representation similar to the Potts model discussed by Strauss (1977). In this work, a Gibbs distribution, with a component specific intercept and a constant interaction parameter, as in Green and Richardson (2002), is proposed to model effect of neighboring areas. This formulation allows to have a parameter specific to each component and a constant spatial dependence in the whole area, extending to quantile and m-quantile regression the proposed by Alfò et al. (2009) who suggested to have both intercept and interaction parameters depending on the mixture component, allowing for different prior probability and varying strength of spatial dependence. We propose, in the current dissertation to adopt this prior distribution to define a Finite mixture of quantile regression model (FMQRSP) and a Finite mixture of M-quantile regression model (FMMQSP), for spatial data.
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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 dissertation is divided into four chapters, each of them focusing on various aspects of multivariate analysis and different data types and structures. The methodologies we propose are supported by theoretical results and illustrated using simulation studies and real-world data.
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Book chapters on the topic "M-quantile regression"

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Elsner, James B., and Thomas H. Jagger. "Intensity Models." In Hurricane Climatology. Oxford University Press, 2013. http://dx.doi.org/10.1093/oso/9780199827633.003.0012.

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Strong hurricanes, such as Camille in 1969, Andrew in 1992, and Katrina in 2005, cause catastrophic damage. It is important to have an estimate of when the next big one will occur. You also want to know what influences the strongest hurricanes and whether they are getting stronger as the earth warms. This chapter shows you how to model hurricane intensity. The data are basinwide lifetime highest intensities for individual tropical cyclones over the North Atlantic and county-level hurricane wind intervals. We begin by considering trends using the method of quantile regression and then examine extreme-value models for estimating return periods. We also look at modeling cyclone winds when the values are given by category, and use Miami-Dade County as an example. Here you consider cyclones above tropical storm intensity (≥ 17 m s−1) during the period 1967–2010, inclusive. The period is long enough to see changes but not too long that it includes intensity estimates before satellite observations. We use “intensity” and “strength” synonymously to mean the fastest wind inside the cyclone. Consider the set of events defined by the location and wind speed at which a tropical cyclone first reaches its lifetime maximum intensity (see Chapter 5). The data are in the file LMI.txt. Import and list the values in 10 columns of the first 6 rows of the data frame by typing . . . &gt; LMI.df = read.table("LMI.txt", header=TRUE) &gt; round(head(LMI.df)[c(1, 5:9, 12, 16)], 1). . . The data set is described in Chapter 6. Here your interest is the smoothed intensity estimate at the time of lifetime maximum (WmaxS). First, convert the wind speeds from the operational units of knots to the SI units of meter per second. . . . &gt; LMI.df$WmaxS = LMI.df$WmaxS * .5144 . . . Next, determine the quartiles (0.25 and 0.75 quantiles) of the wind speed distribution. The quartiles divide the cumulative distribution function (CDF) into three equal-sized subsets. . . . &gt; quantile(LMI.df$WmaxS, c(.25, .75)) 25% 75% 25.5 46.0 . . . You find that 25 percent of the cyclones have a lifetime maximum wind speed less than 26 m s−1 and 75 percent have a maximum wind speed less than 46ms−1, so that 50 percent of all cyclones have a maximum wind speed between 26 and 46 m s−1 (interquartile range–IQR).
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Mitchell, James, Aubrey Poon, and Gian Luigi Mazzi. "Nowcasting Euro Area GDP Growth Using Bayesian Quantile Regression." In Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling. Emerald Publishing Limited, 2022. http://dx.doi.org/10.1108/s0731-90532021000043a004.

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