KLLR: A Scale-dependent, Multivariate Model Class for Regression Analysis

Arya Farahi

doi:10.3847/1538-4357/ac6ac7

2022-06-07

Arya Farahi *

Dhayaa Anbajagane

A. E. Evrard

DOI：10.3847/1538-4357/ac6ac7

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摘要

En 中文

The underlying physics of astronomical systems govern the relation between their measurable properties. Consequently, quantifying the statistical relationships between system-level observable properties of a population offers insights into the astrophysical drivers of that class of systems. While purely linear models capture behavior over a limited range of system scale, the fact that astrophysics is ultimately scale dependent implies the need for a more flexible approach to describing population statistics over a wide dynamic range. For such applications, we introduce and implement a class of kernel localized linear regression (KLLR) models. KLLR is a natural extension to the commonly used linear models that allows the parameters of the linear model-normalization, slope, and covariance matrix-to be scale dependent. KLLR performs inference in two steps: (1) it estimates the mean relation between a set of independent variables and a dependent variable and; (2) it estimates the conditional covariance of the dependent variables given a set of independent variables. We demonstrate the model's performance in a simulated setting and showcase an application of the proposed model in analyzing the baryonic content of dark matter halos. As a part of this work, we publicly release a Python implementation of the KLLR method.

Keyword:

GENERALIZED LINEAR-MODELS

DARK-MATTER HALOES

ILLUSTRISTNG SIMULATIONS

GALAXY CLUSTERS

HOT GAS

MASS

COSMOLOGY

EVOLUTION

SCATTER

ASTROPHYSICS

期刊

Astrophysical Journal

IF:

5.4

论文数:

8.3W

被引数:

32.0W

机构

university of chicago

学者数:

4.5W

论文数: 3.7W

被引数: 80

university of texas system

学者数:

18.5W

论文数: 15.6W

被引数: 210

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