Varying coefficient models having different smoothing variables with randomly censored data

Yang, Seong J.; Ghouch, Anouar El; Keilegom, Ingrid Van

doi:10.1214/14-ejs882

Cited by 8 publications

(5 citation statements)

References 24 publications

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“…When the response variable Y is fully observed, model (2.18) has been studied in estimation (Zhang et al, 2002;Zhang and Li, 2007) and hypothesis testing (Ip et al, 2007). When the response variable Y is subject to randomly right censoring, Yang et al (2014) proposed an estimation method for (2.18) based on synthetic data obtained by the unbiased transformation given by Koul et al (1981) and the smooth back-fitting technique and studied the asymptotic normality of the resulting estimators of the coefficient functions. Our estimation method for model (2.18) is briefly described as follows.…”

Section: Weighted Local Linear Smoothing Methodsmentioning

confidence: 99%

“…The simulation results show that the proposed estimation method indeed has much better finite sample performance than the existing one. In addition to Luo et al (2006), another related paper by Yang et al (2014) studied the problem of estimating the coefficient functions in randomly right censored varying coefficient models where different coefficient functions have different onedimensional smoothing variable, which is more flexible than the model considered in this paper, in which the authors proposed an estimation method based on the synthetic data obtained by the unbiased transformation given by Koul et al (1981) and the smooth back-fitting technique and studied the asymptotic normality of the resulting estimators of the coefficient functions. Although both the proposed estimation method and the estimation method of Yang et al (2014) employed the same assumption on the censoring mechanism (see Remark 2.3 below), the starting point of constructing estimator of the coefficient functions is completely different.…”

Section: Introductionmentioning

confidence: 94%

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Weighted Local Linear Smoothing Method for Randomly Right Censored Varying Coefficient Models

Tizheng¹

2016

ALEA

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In this paper, we propose a new estimation method for randomly right censored varying coefficient models and establish the asymptotic normality of the resulting estimator. Compared to the existing estimation method, the proposed estimation method is computationally simple and practically feasible since it requires no transformation of data. Simulation studies are conducted to assess the finite sample performance of the proposed estimation method against that of the existing estimation method. The simulation results demonstrate that the proposed estimation method performs much better than the existing estimation method.

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Section: Weighted Local Linear Smoothing Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 94%

Weighted Local Linear Smoothing Method for Randomly Right Censored Varying Coefficient Models

Tizheng¹

2016

ALEA

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“…A detailed description of the study can be found in Hosmer et al (2008). The data set has been analyzed by several authors such as Yang et al (2014) and Geerdens et al ( 2020), among others. Moreover, the data are available in the R package quantreg under the name uis.…”

Section: Real Data Applicationmentioning

confidence: 99%

A new lack‐of‐fit test for quantile regression with censored data

Conde-Amboage

Keilegom

González–Manteiga

2021

Scandinavian J Statistics

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A new lack-of-fit test for quantile regression models will be presented for the case where the response variable is right-censored. The test is based on the cumulative sum of residuals, and it extends the ideas of He and Zhu (2003) to censored quantile regression. It will be shown that the empirical process associated with the test statistic converges to a Gaussian process under the null hypothesis and is consistent. To approximate the critical values of the test, a bootstrap mechanism will be used.A simulation study will be carried out to study the performance of the new test in comparison with other tests available in the literature. Finally, a real data application will be presented to show the good properties of the new lack-of-fit test in practice. K E Y W O R D Sbootstrap calibration, censored data, lack-of-fit tests, quantile regression INTRODUCTIONAlthough mean regression is still a traditional benchmark in regression studies, the quantile approach is receiving increasing attention, because it allows a more complete description of the conditional distribution of the response given the covariate, and it is more robust to deviations from error normality. That is, while classical regression gives only information on the conditional

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“…We applied to the dataset the gB-SBF method and the SBF method based on varying-coefficient models [44]. The latter models take the form of linear models but the coefficients are functions of continuous predictors.…”

Section: Randomly Right-censored Scalar Responsementioning

confidence: 99%

“…In our numerical study, we used a 10-fold cross-validation. For the gB-SBF and for the methods of [45] and of [44], we chose G x = dx j=1 {a j + 0.01 × k : k = 0, . .…”

Section: Css Algorithmmentioning

confidence: 99%

Additive regression for predictors of various natures and possibly incomplete Hilbertian responses

Jeon

Park

Keilegom

2021

Electron. J. Statist.

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In this paper we consider a fully nonparametric additive regression model for responses and predictors of various natures. This includes the case of Hilbertian and incomplete (like censored or missing) responses, and continuous, nominal discrete and ordinal discrete predictors. We propose a backfitting technique that estimates this additive model, and establish the existence of the estimator and the convergence of the associated backfitting algorithm under minimal conditions. We also develop a general asymptotic theory for the estimator such as the rates of convergence and asymptotic distribution. We verify the practical performance of the proposed estimator in a simulation study. We also apply the method to various real data sets, including those for a density-valued response regressed on a mixture of continuous and nominal discrete predictors, for a compositional response regressed on a mixture of continuous and ordinal discrete predictors, and for a censored scalar response regressed on a mixture of continuous and nominal discrete predictors.

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Varying coefficient models having different smoothing variables with randomly censored data

Cited by 8 publications

References 24 publications

Weighted Local Linear Smoothing Method for Randomly Right Censored Varying Coefficient Models

Weighted Local Linear Smoothing Method for Randomly Right Censored Varying Coefficient Models

A new lack‐of‐fit test for quantile regression with censored data

Additive regression for predictors of various natures and possibly incomplete Hilbertian responses

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