Optimal smoothing in nonparametric conditional quantile derivative function estimation

Wang, Lin; Cai, Zucong; Li, Zheng; Su, Li

doi:10.1016/j.jeconom.2015.03.014

Cited by 7 publications

(8 citation statements)

References 38 publications

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“…Untuk mengatasi masalah tersebut, maka dikembangkanlah metode regresi quantil. Regresi quantil pertama kali diperkenalkan oleh Koenker dan Basset (1978), Metode ini merupakan perluasan dari model regresi pada quantil bersyarat [12]. Untuk mendapatkan estimasi parameter model regresi quantil diperoleh dengan menggunakan metode pemorograman linier diantarnya algoritma simpleks, interior-point, dan smoothing [11].…”

Section: Y Matdoan B W Otok R M Atokunclassified

Modeling of Quantile Regression to Know the Factors Affecting the High Spread Api Malaria in Indonesia

Matdoan

2020

JMSK

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The OLS method estimation is based on a normal distribution, so it is not appropriate to analyze a number of data that are not symmetrical or contain outliers. Therefore, quantile regression was developed which was not affected by outliers. This study compares quantile regression with OLS in the case of factors affecting malaria in Indonesia. The results show that the value of the Quantil Regression model is 0,832 and the MSE value is 0,182. In addition, the OLS model obtained a value of 0,681 and an MSE value of 0,231. So we get the conclusion that the best model is a quantile regression model. Further results were obtained that the main factors causing the spread of malaria in Indonesia were the factor of livable houses, poor population factors and physician factors.

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Section: Y Matdoan B W Otok R M Atokunclassified

Modeling of Quantile Regression to Know the Factors Affecting the High Spread Api Malaria in Indonesia

Matdoan

2020

JMSK

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“…In the context of the local linear LS, Henderson et al (2015) considered a method of bandwidth selection based on cross-validation for gradient estimation. After that this method is extended to the framework of the local linear QR by Lin et al (2015). Their methods are to select the optimal bandwidth for the gradient estimation based on the local linear approximation which is easy to give a large bias in many situations.…”

Section: Bandwidth Selectionmentioning

confidence: 99%

“…Through the local least squares regression, Henderson et al (2015) put forward a minimum cross-validation approach for choosing bandwidth of gradient estimation. Whereafter, this work is further promoted by Lin et al (2015) to discuss the selection of bandwidth for quantile derivative estimation. Kai et al (2010) suggested a short cut strategy using the relationship of optimal bandwidths between the least squares regression and the composite quantile regression.…”

Section: Introductionmentioning

confidence: 99%

“…(b) A full automatic and data-driven bandwidth selection method is raised for the gradient estimation. The approach of bandwidth selection enriches the works of Henderson et al (2015) and Lin et al (2015) to estimate gradient making use of the local linear LS and the local linear QR methods, respectively. We consider to replace the unknown true gradient with local cubic derivative estimator in the oracle least squares cross-validation (CV) which was constructed by Müller et al (1987) with local estimator.…”

Section: Introductionmentioning

confidence: 99%

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Local weighted composite quantile estimation and smoothing parameter selection for nonparametric derivative function

Xie

Sun

Liu

2019

Econometric Reviews

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Estimating derivatives is of primary interest as it quantitatively measures the rate of change of the relationship between response and explanatory variables. We propose a local weighted composite quantile method to estimate the gradient of an unknown regression function. Because of the use of weights, a data-driven weighting scheme is discussed for maximizing the asymptotic efficiency of the estimators. We derive the leading bias, variance and normality of the estimator proposed. The asymptotic relative efficiency is investigated and reveals that the new approach provides a highly efficient alternative to the local least squares, the local quantile regression and the local composite quantile regression methods. In addition, a fully automatic bandwidth selection method is considered and is shown to deliver the bandwidth with oracle property meaning that it is asymptotically equivalent to the optimal bandwidth if the true gradient were known. Simulations are conducted to compare different estimators and an example is used to illustrate their performance. Both simulation and empirical results are consistent with our theoretical findings.

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“…Using nonparametric techniques to estimate econometric models has received increasing attention among econometricians in recent decades (see, for example, Pagan and Ullah (1999); Hall et al (2007); Belloni et al (2016); Lin et al (2015); Li et al (2013); Firpo et al (2009) and Firpo et al (2018) for the literature of nonparametric methods and applications). The most popular nonparametric model is the conditional mean regression model.…”

Section: Introductionmentioning

confidence: 99%

Nonparametric Estimation of a Conditional Quantile Function in a Fixed Effects Panel Data Model

Yan

2018

JRFM

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Optimal smoothing in nonparametric conditional quantile derivative function estimation

Cited by 7 publications

References 38 publications

Modeling of Quantile Regression to Know the Factors Affecting the High Spread Api Malaria in Indonesia

Modeling of Quantile Regression to Know the Factors Affecting the High Spread Api Malaria in Indonesia

Local weighted composite quantile estimation and smoothing parameter selection for nonparametric derivative function

Nonparametric Estimation of a Conditional Quantile Function in a Fixed Effects Panel Data Model

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