Do Bars Trigger Activity in Galactic Nuclei?

We investigate the connection between the presence of bars and AGN activity, using a volume-limited sample of $\sim$9,000 late-type galaxies with axis ratio $b/a>0.6$ and $M_{r} < -19.5+5{\rm log}h$ at low redshift ($0.02\le z\lesssim 0.055$), selected from Sloan Digital Sky Survey Data Release 7. We find that the bar fraction in AGN-host galaxies (42.6%) is $\sim$2.5 times higher than in non-AGN galaxies (15.6%), and that the AGN fraction is a factor of two higher in strong-barred galaxies (34.5%) than in non-barred galaxies (15.0%). However, these trends are simply caused by the fact that AGN-host galaxies are on average more massive and redder than non-AGN galaxies because the fraction of strong-barred galaxies (\bfrsbo) increases with $u-r$ color and stellar velocity dispersion. When $u-r$ color and velocity dispersion (or stellar mass) are fixed, both the excess of \bfrsbo in AGN-host galaxies and the enhanced AGN fraction in strong-barred galaxies disappears. Among AGN-host galaxies we find no strong difference of the Eddington ratio distributions between barred and non-barred systems. These results indicate that AGN activity is not dominated by the presence of bars, and that AGN power is not enhanced by bars. In conclusion we do not find a clear evidence that bars trigger AGN activity.Comment: 13 pages, 11 figures, accepted for publication in Ap

Doubly Flexible Estimation under Label Shift

In studies ranging from clinical medicine to policy research, complete data are usually available from a population $\mathscr{P}$, but the quantity of interest is often sought for a related but different population $\mathscr{Q}$ which only has partial data. In this paper, we consider the setting that both outcome $Y$ and covariate ${\bf X}$ are available from $\mathscr{P}$ whereas only ${\bf X}$ is available from $\mathscr{Q}$, under the so-called label shift assumption, i.e., the conditional distribution of ${\bf X}$ given $Y$ remains the same across the two populations. To estimate the parameter of interest in $\mathscr{Q}$ via leveraging the information from $\mathscr{P}$, the following three ingredients are essential: (a) the common conditional distribution of ${\bf X}$ given $Y$, (b) the regression model of $Y$ given ${\bf X}$ in $\mathscr{P}$, and (c) the density ratio of $Y$ between the two populations. We propose an estimation procedure that only needs standard nonparametric technique to approximate the conditional expectations with respect to (a), while by no means needs an estimate or model for (b) or (c); i.e., doubly flexible to the possible model misspecifications of both (b) and (c). This is conceptually different from the well-known doubly robust estimation in that, double robustness allows at most one model to be misspecified whereas our proposal can allow both (b) and (c) to be misspecified. This is of particular interest in our setting because estimating (c) is difficult, if not impossible, by virtue of the absence of the $Y$-data in $\mathscr{Q}$. Furthermore, even though the estimation of (b) is sometimes off-the-shelf, it can face curse of dimensionality or computational challenges. We develop the large sample theory for the proposed estimator, and examine its finite-sample performance through simulation studies as well as an application to the MIMIC-III database