Quantile Regression Model
Lasso Factorial Design Quantile Regression Model with Application
... in regression models. One of these models is the quantile regression model (QR), which was introduced by Koenker and Bassett ...of regression model is not required to satisfy ...
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Efficient Estimation of an Additive Quantile Regression Model
... additive quantile regression ...polynomial quantile smoothing for each additive component with the other additive components replaced by the corresponding estimates from the first ...additive ...
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Measuring the Mutual Fund Industry Risk Management and Performance Sustainability - Quantile Regression Model
... the Quantile Regression Model to observe the rank correlation between bond fund performance and asset, volatility, management fee, Sharpe index and show that fund performance between volatility as a ...
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Estimation of critical streamflow discharge level using nonparametric quantile regression model
... nonparametric quantile function is consistent and asymptotically normally distributed, and under suitable conditions, the estimator converges uniformly with an appropriate ...
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Efficient semiparametric estimation of a partially linear quantile regression model
... nonparametric quantile regression models + 1 For example , see Chaudhuri ~1991a , 1991b!, Fan , Hu , and Truong ~1994!, and Welsh ~1996! for local polynomial quantile regression ; see ...
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Quantile Regression in Statistical Downscaling to Estimate Extreme Monthly Rainfall
... developing model of statistical downscaling using quantile regression to estimate extreme monthly ...Circulation Model output in the same ...developing quantile regression ...
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A quantile regression approach for modelling a Health-Related Quality of Life Measure
... a Quantile Regression ...linear regression or logistic regression to model the measures of ...a quantile regression approach was used to evaluate such a relationship ...
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Quantile Regression for Repeated Responses Measured with Error.
... ditional quantile curves of usual sodium intake as a function of age for two domains in the ...These quantile curves clearly offer a broader perspective about the usual sodium intake patterns across ...a ...
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Bayesian spatial quantile regression
... spatial model to predict ozone under different meteorological conditions, and use this model to study spatial and temporal trends and to forecast ozone concentrations under different climate ...spatial ...
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Semiparametric quantile regression with random censoring
... This paper considers estimation and inference in semiparametric quantile regression models when the response variable is subject to random censoring. The paper consid- ers both the cases of independent and ...
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Quantile forecasts of inflation under model uncertainty
... regression model for in‡ation. My ultimate aim is to produce quantile forecasts for in‡ation using several potential explanatory ...Bayesian model aver- aging (BMA) and selection (BMS) methods ...
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Penalized Flexible Bayesian Quantile Regression
... to reconcile these differences. Our motivating example is an analysis of body fat data which is previously analyzed by Johnson [32] and available in the package “mfp”. This study had a total body measurements of 252 men. ...
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Comments and Suggestions on Defining and Using P-Values and Null Hypothesis Tests
... ratio quantile used in this paper because of Iraq's dependence on oil export revenue source foundation to finance the development of sectors of Iraq's economy, as it depends on the prosperity of Iraq's energy ...
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Are there asymmetries in the effects of training on the conditional male wage distribution?
... linear regression framework (see inter alia references in Ashenfelter and Lalonde, 1996; and Arulampalam and Booth, ...conditional quantile regression model was first introduced by Koenker and ...
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A nonparametric approach for quantile regression
... Quantile regression estimates conditional quantiles and has wide applications in the real ...regular quantile regression (QR) method often designs a linear or non-linear model, then ...
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Innovation and market value: A quantile regression analysis
... conventional regression estimators show that the stock market does recognise efforts at innovation, quantile regression analysis adds a new dimension to the literature, suggesting that the influence ...
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Demand for International Reserves: A Quantile Regression Approach
... stock’ model, developed in the 1960s and 1970s ...The model posits that central banks choose an optimal level of reserves to balance the costs of the macroeconomic adjustment incurred in the absence of ...
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Weighted Type of Quantile Regression and its Application
... We will address the issue of choosing weights when using the weighted CQR. Since the weights in the weighted CQR are allowed to be negative, the proposed weighted CQR is different from the common QR and the CQR (see also ...
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Poverty and Intelligence: Evidence Using Quantile Regression
... The strength of the empirical analysis of Lynn and Vanhanen (2006) has never really been studied. For more recently, Mani et al. (2013) showed that poverty also affects cognitive Capacities . This result is in line with ...
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Pricing Ability of Four Factor Model using Quantile Regression: Evidences from India
... the quantile regression suggested by Koenker and Bassett (1978) to test the relationship in their study ...the quantile regression while Basset and Chen (2001) have used the same for portfolio ...
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