Nonparametric instrument model averaging

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Nonparametric Statistics Pub Date : 2023-05-22 DOI:10.1080/10485252.2023.2215339

Jianan Chen, Binyan Jiang, Jialiang Li

引用次数: 0

Abstract

We present a new nonparametric model averaging approach to the instrumental variable (IV) regression where the effects of multiple instruments on the endogenous variable are modelled as nonparametric functions in the reduced form equations. Even if individual IVs may have weak and nonlinear relevance to the exposure, our proposed model averaging is able to ensemble their effects with optimal weights to produce valid inference. Our analysis covers both the case in which the number of IV is fixed and the case in which the dimension of IV is diverging with sample size. This novel framework can be especially beneficial to the practical situations involving weak IVs since in many recent observational studies we may encounter a large number of instruments and their quality could range from poor to strong. Numerical studies are carried out and comparisons are made between our proposed method and a wide range of existing alternative methods.

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来源期刊

Journal of Nonparametric Statistics 数学-统计学与概率论

CiteScore

1.50

自引率

8.30%

发文量

审稿时长

6-12 weeks

期刊介绍： Journal of Nonparametric Statistics provides a medium for the publication of research and survey work in nonparametric statistics and related areas. The scope includes, but is not limited to the following topics: Nonparametric modeling, Nonparametric function estimation, Rank and other robust and distribution-free procedures, Resampling methods, Lack-of-fit testing, Multivariate analysis, Inference with high-dimensional data, Dimension reduction and variable selection, Methods for errors in variables, missing, censored, and other incomplete data structures, Inference of stochastic processes, Sample surveys, Time series analysis, Longitudinal and functional data analysis, Nonparametric Bayes methods and decision procedures, Semiparametric models and procedures, Statistical methods for imaging and tomography, Statistical inverse problems, Financial statistics and econometrics, Bioinformatics and comparative genomics, Statistical algorithms and machine learning. Both the theory and applications of nonparametric statistics are covered in the journal. Research applying nonparametric methods to medicine, engineering, technology, science and humanities is welcomed, provided the novelty and quality level are of the highest order. Authors are encouraged to submit supplementary technical arguments, computer code, data analysed in the paper or any additional information for online publication along with the published paper.