Sample variance in the local measurements of the Hubble constant

The current $>3\sigma$ tension between the Hubble constant $H_0$ measured from local distance indicators and from cosmic microwave background is one of the most highly debated issues in cosmology, as it possibly indicates new physics or unknown systematics. In this work, we explore whether this tension can be alleviated by the sample variance in the local measurements, which use a small fraction of the Hubble volume. We use a large-volume cosmological $N$-body simulation to model the local measurements and to quantify the variance due to local density fluctuations and sample selection. We explicitly take into account the inhomogeneous spatial distribution of type Ia supernovae. Despite the faithful modelling of the observations, our results confirm previous findings that sample variance in the local Hubble constant $(H_0^{\rm loc})$ measurements is small; we find $\sigma(H_0^{\rm loc})=0.31\,{\rm km\ s^{-1}Mpc^{-1}}$, a nearly negligible fraction of the$\sim6\,{\rm km\ s^{-1}Mpc^{-1}}$necessary to explain the difference between the local and the global$H_0$measurements. While the$H_0$tension could in principle be explained by our local neighbourhood being a underdense region of radius$\sim 150 \,\rm Mpc$, the extreme required underdensity of such a void$(\delta\simeq -0.8)$makes it very unlikely in a$\Lambda$CDM universe, and it also violates existing observational constraints. Therefore, sample variance in a$\Lambda$CDM universe cannot appreciably alleviate the tension in$H_0$ measurements even after taking into account the inhomogeneous selection of type Ia supernovae.Comment: 10 pages, 6 figures, 1 table; main result in Figure 3; replaced to match published versio

Component selection and smoothing in multivariate nonparametric regression

We propose a new method for model selection and model fitting in multivariate nonparametric regression models, in the framework of smoothing spline ANOVA. The ``COSSO'' is a method of regularization with the penalty functional being the sum of component norms, instead of the squared norm employed in the traditional smoothing spline method. The COSSO provides a unified framework for several recent proposals for model selection in linear models and smoothing spline ANOVA models. Theoretical properties, such as the existence and the rate of convergence of the COSSO estimator, are studied. In the special case of a tensor product design with periodic functions, a detailed analysis reveals that the COSSO does model selection by applying a novel soft thresholding type operation to the function components. We give an equivalent formulation of the COSSO estimator which leads naturally to an iterative algorithm. We compare the COSSO with MARS, a popular method that builds functional ANOVA models, in simulations and real examples. The COSSO method can be extended to classification problems and we compare its performance with those of a number of machine learning algorithms on real datasets. The COSSO gives very competitive performance in these studies.Comment: Published at http://dx.doi.org/10.1214/009053606000000722 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org