Dimension reduction estimation for central mean subspace with missing multivariate response

Fan, Guoguang; Xu, Hong-Xia; Liang, Han-Ying

doi:10.1016/j.jmva.2019.104542

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Some Extensions Of Multivariate Sir1

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Cited by 3 publications

(1 citation statement)

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“…For instance, [67] provides asymptotic results for the pooled marginal slicing estimator based on SIR α approach, while moment-based dimension reduction methods for a multivariate response Y were developed by [83]. The case of missing values in Y has been explored by [34] since multivariate SIR approaches cannot be used directly. The specific case of a bivariate response was studied by [78], especially when Y is a mix of continuous and categorical responses.…”

Section: Some Extensions Of Multivariate Sirmentioning

confidence: 99%

Advanced topics in Sliced Inverse Regression

Girard

Lorenzo

Saracco

2022

Journal of Multivariate Analysis

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Section: Some Extensions Of Multivariate Sirmentioning

confidence: 99%

Advanced topics in Sliced Inverse Regression

Girard

Lorenzo

Saracco

2022

Journal of Multivariate Analysis

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Sufficient dimension reduction in the presence of controlling variable and missing multivariate response

Ling

et al. 2023

Communications in Statistics - Simulation and Computation

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Nonlinear interaction detection through partial dimension reduction with missing response data

Fan

2022

Random Matrices: Theory Appl.

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In this paper, we are concerned with nonlinear interaction detection based on partial dimension reduction with missing response data. The covariates are grouped through linear combinations in a general class of semi-parametric models to detect their joint interaction effects. The joint interaction effects are estimated by a profile least squares approach with the help of the inverse probability weighted technique. The asymptotic properties of the resulting estimate for the central partial mean subspace are established. In addition, a Wald type test is proposed to detect the interactions between the covariates. A BIC-type criterion is applied to determine the structural dimension of the central partial mean subspace and its consistency is also obtained. Simulations are conducted to examine the finite sample performances of our proposed method and a real data set is analyzed for illustration.

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Dimension reduction estimation for central mean subspace with missing multivariate response

Cited by 3 publications

References 23 publications

Advanced topics in Sliced Inverse Regression

Advanced topics in Sliced Inverse Regression

Sufficient dimension reduction in the presence of controlling variable and missing multivariate response

Nonlinear interaction detection through partial dimension reduction with missing response data

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