Related papers: Biases from Non-Simultaneous Regression with Corre…
We present 2603 spectra of 462 nearby Type Ia supernovae (SN Ia) obtained during 1993-2008 through the Center for Astrophysics Supernova Program. Most of the spectra were obtained with the FAST spectrograph at the FLWO 1.5m telescope and…
We present examples of non-Gaussian statistics that can induce bispectra matching local and non-local (including equilateral) templates in biased sub-volumes. We find cases where the biasing from coupling to long wavelength modes affects…
Missing values in datasets are common in applied statistics. For regression problems, theoretical work thus far has largely considered the issue of missing covariates as distinct from missing responses. However, in practice, many datasets…
We use type Ia supernovae (SNe Ia) data obtained by the Sloan Digital Sky Survey-II Supernova Survey (SDSS-II/SNe) in combination with the publicly available SDSS DR16 fiber spectroscopy of their host galaxies to correlate SNe Ia…
Detailed radiative transfer simulations of kilonovae are difficult to apply directly to observations; they only sparsely cover simulation parameters, such as the mass, velocity, morphology, and composition of the ejecta. On the other hand,…
Past analyses of Type Ia Supernovae (SNe Ia) have identified an irreducible scatter of 5-10% in distance widely attributed to an intrinsic dispersion in luminosity. Another, equally valid, source of this scatter is intrinsic dispersion in…
Randomization is a basis for the statistical inference of treatment effects without strong assumptions on the outcome-generating process. Appropriately using covariates further yields more precise estimators in randomized experiments. R. A.…
Concerning bivariate least squares linear regression, the classical results obtained for extreme structural models in earlier attempts are reviewed using a new formalism in terms of deviation (matrix) traces which, for homoscedastic data,…
In this paper we present and discuss a new Monte Carlo approach aimed at correcting the observed Supernova (SN) rates for the effects of host galaxy dust extinction. The problem is addressed in a general way and the model includes SN…
The design and analysis of time-domain sky surveys requires the ability to simulate accurately realistic populations of core collapse supernova (SN) events. We present a set of spectral time-series templates designed for this purpose, for…
In this work, we show that when supernova Ia (SN Ia) data sets are used to put constraints on the free parameters of inhomogeneous models, certain extra information regarding the light-curve fitter used in the supernovae Ia luminosity…
We study whether the bias factors of galaxies can be unbiasedly recovered from their power spectra and bispectra. We use a set of numerical N-body simulations and construct large mock galaxy catalogs based upon the semi-analytical model of…
Intrinsic alignments (IA) of galaxies is one of the key secondary signals to cosmic shear measurements, and must be modeled to interpret weak lensing data and infer the correct cosmology. There are large uncertainties in the physical…
Type Ia supernovae (SNe Ia) are useful distance indicators in cosmology, provided their luminosity is standardized by applying empirical corrections based on light-curve properties. One factor behind these corrections is dust extinction,…
Linear regression is a data analysis technique, which is categorized as supervised learning. By utilizing known data, we can predict unknown data. Recently, researchers have explored the use of quantum annealing (QA) to perform linear…
We present an analytic model for bolometric light curves which are powered by the interaction between supernova ejecta and a dense circumstellar medium. This model is aimed at modeling Type IIn supernovae to determine the properties of…
We study the biasing relation between dark-matter halos or galaxies and the underlying mass distribution, using cosmological $N$-body simulations in which galaxies are modelled via semi-analytic recipes. The nonlinear, stochastic biasing is…
Inhomogeneous universe models have been proposed as an alternative explanation for the apparent acceleration of the cosmic expansion that does not require dark energy. In the simplest class of inhomogeneous models, we live within a large,…
Observed data is often contaminated by undiscovered interlopers, leading to biased parameter estimation. Here we present BEAMS (Bayesian Estimation Applied to Multiple Species) which significantly improves on the standard maximum likelihood…
The objective of this work is to propose an asymptotic correction method for the estimators of parameters from regression models with covariates subject to classification errors. A correction was developed based on the least squares…