Efficient parameter estimation and variable selection in partial linear varying coefficient quantile regression model with longitudinal data

Wang, Kangning; Sun, Xiaofei

doi:10.1007/s00362-017-0970-0

Cited by 7 publications

(4 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fitting model ( 4) is equivalent to estimating parameters in (5). If q F,𝜏 (x i (t ij )) is correctly specified, a number of methodologies have been proposed to make related inference including parametric methods (e.g.…”

Section: Estimating 𝜏-Cqf With Censored History Processmentioning

confidence: 99%

“…Geraci and Bottai 26 proposed a best linear prediction (BLP) of u i in terms of linear fixed effect. For a given probability level, the predictor depends on the estimation of parameters in (5) with correct specification of q F,𝜏 (x i (t ij )) and the construction of u i , and satisfies that its mean squared error (MSE) reaches the minimum. In (4) we assume that random effects have a linear pattern z ⊤ i (t ij )u i , for the ith cluster, the BLP of u i is given as…”

Section: Eaw-based Prediction For Random Effectsmentioning

confidence: 99%

“…Lu and Fan 3 estimated parameters in weighted quantile regression. Tang et al 4 proposed a B-spline-based variable selection procedure for varying-coefficient quantile regression, and Wang and Sun 5 extended the approach to partial linear varying coefficient model with the within-subject correlation structure.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive aggregation for longitudinal quantile regression based on censored history process

Xiong

Deng

Wang

et al. 2023

Stat Methods Med Res

View full text Add to dashboard Cite

Most of the studies for longitudinal quantile regression are based on the correct specification. Nevertheless, one specific model can hardly perform precisely under different conditions and assessing which conditions are (approximately) satisfied to determine the optimal one is rather difficult. In the case of the mixed effect model, the misspecification of the fixed effect part will cause a lack of predicting accuracy of random effects, and affect the efficiency of the cumulative function estimator. On the other hand, limited research has focused on incorporating multiple candidate procedures in longitudinal data analysis, which is of current emergency. This paper proposes an exponential aggregation weighting algorithm for longitudinal quantile regression. Based on the secondary smoothing loss function, we establish oracle inequalities for aggregated estimator. The proposed method is applied to evaluate the cumulative [Formula: see text]th quantile function for additive mixed effect model with right-censored history process, and an aggregation-based best linear prediction for random effects is constructed as well. We show that the asymptotic properties are conveniently imposed owing to the smoothing scheme. Simulation studies are carried out to exhibit the rationality, and our method is illustrated to analyze the data set from a multicenter automatic defibrillator implantation trial.

show abstract

Section: Estimating 𝜏-Cqf With Censored History Processmentioning

confidence: 99%

Section: Eaw-based Prediction For Random Effectsmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive aggregation for longitudinal quantile regression based on censored history process

Xiong

Deng

Wang

et al. 2023

Stat Methods Med Res

View full text Add to dashboard Cite

show abstract

“…Fu and Wang, 15 Tang and Leng, 16 and Leng and Zhang 17 all indicated that the efficiency of the parameter estimation will be lost when a strong correlation exists. To capture the correlation and enhance the estimation efficiency, Lv et al 18 used the smooth‐threshold efficient QR‐based generalized estimating equations to select the covariates and estimate the parameters in the quantile partially linear additive model with longitudinal data; Wang and Sun 19 explored this kind of method for the quantile partial linear varying coefficient model with longitudinal data.…”

Section: Introductionmentioning

confidence: 99%

Penalized weighted smoothed quantile regression for high‐dimensional longitudinal data

Song,

Han,

et al. 2024

Statistics in Medicine

View full text Add to dashboard Cite

Quantile regression, known as a robust alternative to linear regression, has been widely used in statistical modeling and inference. In this paper, we propose a penalized weighted convolution‐type smoothed method for variable selection and robust parameter estimation of the quantile regression with high dimensional longitudinal data. The proposed method utilizes a twice‐differentiable and smoothed loss function instead of the check function in quantile regression without penalty, and can select the important covariates consistently using the efficient gradient‐based iterative algorithms when the dimension of covariates is larger than the sample size. Moreover, the proposed method can circumvent the influence of outliers in the response variable and/or the covariates. To incorporate the correlation within each subject and enhance the accuracy of the parameter estimation, a two‐step weighted estimation method is also established. Furthermore, we prove the oracle properties of the proposed method under some regularity conditions. Finally, the performance of the proposed method is demonstrated by simulation studies and two real examples.

show abstract

Variable selection in partially linear additive hazards model with grouped covariates and a diverging number of parameters

Afzal

Yang

2021

Comput Stat

View full text Add to dashboard Cite

Efficient parameter estimation and variable selection in partial linear varying coefficient quantile regression model with longitudinal data

Cited by 7 publications

References 37 publications

Adaptive aggregation for longitudinal quantile regression based on censored history process

Adaptive aggregation for longitudinal quantile regression based on censored history process

Penalized weighted smoothed quantile regression for high‐dimensional longitudinal data

Variable selection in partially linear additive hazards model with grouped covariates and a diverging number of parameters

Contact Info

Product

Resources

About