6 citations · 6 across the 1 of their papers we have counts for
6 papers
Multi-Tree Methods for Statistics on Very Large Datasets in Astronomy
Alexander G. Gray, Andrew W. Moore, Robert C. Nichol +3
Many fundamental statistical methods have become critical tools for scientific data analysis yet do not scale tractably to modern large datasets. This paper will describe very rece…
Non-Parametric Inference in Astrophysics
Larry Wasserman, Christopher J. Miller, Robert C. Nichol +6
We discuss non-parametric density estimation and regression for astrophysics problems. In particular, we show how to compute non-parametric confidence intervals for the location an…
A Non-parametric Analysis of the CMB Power Spectrum
Christopher J. Miller, Robert C. Nichol, Christopher Genovese +1
We examine Cosmic Microwave Background (CMB) temperature power spectra from the BOOMERANG, MAXIMA, and DASI experiments. We non-parametrically estimate the true power spectrum with…
Computational AstroStatistics: Fast and Efficient Tools for Analysing Huge Astronomical Data Sources
R. C. Nichol, S. Chong, A. J. Connolly +10
I present here a review of past and present multi-disciplinary research of the Pittsburgh Computational AstroStatistics (PiCA) group. This group is dedicated to developing fast and…
Fast Algorithms and Efficient Statistics: Density Estimation in Large Astronomical Datasets
A. J. Connolly, C. Genovese, A. W. Moore +3
In this paper, we outline the use of Mixture Models in density estimation of large astronomical databases. This method of density estimation has been known in Statistics for some t…
Computational AstroStatistics: Fast Algorithms and Efficient Statistics for Density Estimation in Large Astronomical Datasets
R. C. Nichol, A. J. Connolly, A. W. Moore +3
We present initial results on the use of Mixture Models for density estimation in large astronomical databases. We provide herein both the theoretical and experimental background f…