Simultaneous Functional Quantile Regression

Abstract

The conventional method for functional quantile regression (FQR) is to fit the regression model for each quantile of interest separately. Therefore, the slope function of the regression, as a bivariate function of time and quantile, is estimated as a univariate function of time for each fixed quantile. There are several limitations to this conventional strategy. For example, the monotonicity of conditional quantiles can not be guaranteed, and the smoothness of the slope estimator as a bivariate function can not be controlled. In this paper, we develop a new framework for functional quantile regression. We propose to simultaneously fit the functional quantile regression model for multiple quantiles with the help of a bivariate basis under some constraints so that the estimated quantiles satisfy the monotonicity conditions. Meanwhile, the smoothness of the slope estimator is controlled.The proposed estimator for the slope function is shown to be asymptotically consistent. In addition, we also establish its asymptotic normality. Simulation studies are implemented to evaluate the finite sample performance of the proposed method in comparison with the

A chromosome-level genome of the striated frogfish (Antennarius striatus)

“…The DNA and RNA sequencing data have been deposited in the NCBI Sequence Read Archive (SRA) database, including SRR28026916 51 , SRR28026917 52 , SRR28026918 53 , SRR28038112 54 , SRR28038113 55 , SRR28038114 56 , SRR28038115 57 , SRR28038116 58 , SRR28038117 59 , SRR28038118 60 , SRR28038119 61 , SRR28038120 62 , SRR28038121 63 , SRR28038122 53 , SRR28038123 64 , SRR28038124 65 , which is associated with the BioProject accession number PRJNA1078610. The draft genome assembly of the striated frogfish has been submitted to the NCBI GeneBank with the accession number JBANDW000000000 66 . The draft genome assembly and genome annotation were deposited in the Figshare database (10.6084/m9.figshare.25340587) 67 .…”