Smoothed partially linear varying coefficient quantile regression with nonignorable missing response
Metrika, ISSN: 1435-926X
2024
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
In this paper, we propose a smoothed quantile regression estimator and variable selection procedure for partially linear varying coefficient models with nonignorable nonresponse. To avoid the computational problem caused by the non-smooth quantile loss function, we employ the kernel smoothing method. To address the identifiability issue, we use an instrument and estimate the parametric propensity function based on the generalized method of moments. Once the propensity is estimated, we construct the bias-corrected estimating equations utilizing the inverse probability weighting approach. Then, we apply the empirical likelihood method to obtain an unbiased estimator. The asymptotic properties of the proposed estimators are established for both the parametric and nonparametric parts. Meanwhile, variable selection is considered by using the SCAD penalty. The finite-sample performance of the estimators is studied through simulations, and a real-data example is also presented.
Bibliographic Details
Springer Science and Business Media LLC
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