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Activity Number:
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75
- Invited EPoster Session II
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Type:
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Invited
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Date/Time:
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Sunday, August 7, 2022 : 9:35 PM to 10:30 PM
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Sponsor:
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Section on Statistics in Imaging
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Abstract #323553
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Title:
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Density-on-Scalar Single-Index Quantile Regression Model
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Author(s):
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Shengxian Ding* and Rongjie Liu and Chao Huang
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Companies:
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Department of Statistics, Florida State University and Department of Statistics, Florida State University and Department of Statistics, Florida State University
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Keywords:
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Glioblastoma;
Density function;
Single-index;
Quantile regression;
Heterogeneity
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Abstract:
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This paper develops a density-on-scalar single-index quantile regression modeling framework to investigate the relationship between imaging responses and covariates of interest while tackling the imaging heterogeneity. Conventional association analysis methods typically assume that the imaging responses are well aligned after some preprocessing steps. However, this assumption is often violated in practice due to imaging heterogeneity, which is primarily caused by the different pathological patterns across subjects. Although some distribution-based approaches are developed to deal with this heterogeneity, major challenges have been posted due to the nonlinear subspace formed by the distributional responses and the unknown nonlinear association structure. Our method can successfully address all the challenges. We establish both estimation and inference procedures for the unknown functions in our model. The asymptotic properties of both estimation and inference procedures are systematically investigated. The finite-sample performance of our method is assessed by both Monte Carlo simulations and a real data example on brain cancer images from TCIA Glioblastoma Multiforme collection.
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Authors who are presenting talks have a * after their name.
