We present nonparametric techniques for constructing and verifying density estimates from high-dimensional data whose irregular dependence structure cannot be modelled by parametric multivariateâ€¦ (More)

To further our knowledge of the complex physical process of galaxy formation, it is essential that we characterize the formation and evolution of large databases of galaxies. The spectral synthesisâ€¦ (More)

Given observed data and a collection of parameterized candidate models, a 1 -- Î± confidence region in parameter space provides useful insight as to those models which are a good fit to the data, allâ€¦ (More)

This article presents a Monte Carlo method for approximating the minimax expected size (MES) confidence set for a parameter known to lie in a compact set. The algorithm is motivated by problems inâ€¦ (More)

Two common sources of DNA for whole exome sequencing (WES) are whole blood (WB) and immortalized lymphoblastoid cell line (LCL). However, it is possible that LCLs have a substantially higher rate ofâ€¦ (More)

Statistical inference of cosmological quantities of interest is complicated by significant observational limitations, including heteroscedastic measurement error and irregular selection effects.â€¦ (More)

Frequently physical scientists seek a confidence set for a parameter whose precise value is unknown, but constrained by theory or previous experiments. The confidence set should exclude parameterâ€¦ (More)

Parameter estimation in astrophysics often requires the use of complex physical models. In this paper we study the problem of estimating the parameters that describe star formation history (SFH) inâ€¦ (More)

The observational limitations of astronomical surveys lead to significant statistical inference challenges. One such challenge is the estimation of luminosity functions given redshift (z) andâ€¦ (More)

Dimension-reduction techniques can greatly improve statistical inference in astronomy. A standard approach is to use Principal Components Analysis (PCA). In this letter we apply a recently-developedâ€¦ (More)