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Detecting Monotone Bias in Galaxy Luminosities

Abstract Number:

1880 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Zixiang Xu (1), Jiayang Sun (2), Mary Meyer (3), Michael Woodroofe (4)

Institutions:

(1) N/A, N/A, (2) George Mason University, N/A, (3) Colorado State University, N/A, (4) Univ of Michigan, N/A

Co-Author(s):

Jiayang Sun  
George Mason University
Mary Meyer  
Colorado State University
Michael Woodroofe  
Univ of Michigan

First Author:

Zixiang Xu  
N/A

Presenting Author:

Zixiang Xu  
N/A

Abstract Text:

Luminosity function is a fundamental concept in astrophysics and cosmology, which describes the distribution of luminosities within a group of astronomical objects, such as galaxies. In reality, there could possibly exist a monotone selection bias when we collect the data on luminosities: given the same distance, the stars/galaxies with larger luminosity have a higher chance of being observed. This poses challenges for standard estimation procedures. Ignoring this bias can lead to failure. Conversely, procedures accounting for it may be inefficient when there is no selection bias. This poster introduces two semi-parametric procedures for detecting monotone selection bias in data with unknown parameters, with their application on a real dataset of galaxy luminosities.

Keywords:

selection bias|semiparametric|luminosity function| | |

Sponsors:

Survey Research Methods Section

Tracks:

Missing Data Methods/Non-response Bias Analysis

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I have read and understand that JSM participants must abide by the Participant Guidelines.

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