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For: Liang HY, Wang BH, Shen Y. Quantile regression of partially linear single-index model with missing observations. STATISTICS-ABINGDON 2021. [DOI: 10.1080/02331888.2021.1883613] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Number Cited by Other Article(s)
1
Wang BH, Liang HY. Empirical likelihood in single-index quantile regression with high dimensional and missing observations. J Stat Plan Inference 2023. [DOI: 10.1016/j.jspi.2023.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
2
Hu YP, Liang HY. Empirical likelihood in single-index partially functional linear model with missing observations. COMMUN STAT-THEOR M 2022. [DOI: 10.1080/03610926.2022.2094413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
3
Liu CS, Liang HY. Bayesian analysis in single-index quantile regression with missing observation. COMMUN STAT-THEOR M 2022. [DOI: 10.1080/03610926.2022.2042027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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