Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Regression Analyses of Distributions using Quantile Functional
  Regression   Radiomics involves the study of tumor images to identify quantitative markers
explaining cancer heterogeneity. The predominant approach is to extract
hundreds to thousands of image features, including histogram features comprised
of summaries of the marginal distribution of pixel intensities, which leads to
multiple testing problems and can miss out on insights not contained in the
selected features. In this paper, we present methods to model the entire
marginal distribution of pixel intensities via the quantile function as
functional data, regressed on a set of demographic, clinical, and genetic
predictors. We call this approach quantile functional regression, regressing
subject-specific marginal distributions across repeated measurements on a set
of covariates, allowing us to assess which covariates are associated with the
distribution in a global sense, as well as to identify distributional features
characterizing these differences, including mean, variance, skewness, and
various upper and lower quantiles. To account for smoothness in the quantile
functions, we introduce custom basis functions we call quantlets that are
sparse, regularized, near-lossless, and empirically defined, adapting to the
features of a given data set. We fit this model using a Bayesian framework that
uses nonlinear shrinkage of quantlet coefficients to regularize the functional
regression coefficients and provides fully Bayesian inference after fitting a
Markov chain Monte Carlo. We demonstrate the benefit of the basis space
modeling through simulation studies, and apply the method to Magnetic resonance
imaging (MRI) based radiomic dataset from Glioblastoma Multiforme to relate
imaging-based quantile functions to demographic, clinical, and genetic
predictors, finding specific differences in tumor pixel intensity distribution
between males and females and between tumors with and without DDIT3 mutations.

12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-lat
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The categories of this paper are: patt-sol
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The categories of this paper are: quant-ph
1
The categories of this paper are: cs