490 citations · 3.5k across the 123 of their papers we have counts for
10 papers · 1 filter
Inference for the dark energy equation of state using Type IA supernova data
Christopher Genovese, Peter Freeman, Larry Wasserman +2
The surprising discovery of an accelerating universe led cosmologists to posit the existence of "dark energy"--a mysterious energy field that permeates the universe. Understanding…
Mapping the Cosmological Confidence Ball Surface
Brent Bryan, Jeff Schneider, Christopher J. Miller +3
We present a new technique to compute simultaneously valid confidence intervals for a set of model parameters. We apply our method to the Wilkinson Microwave Anisotropy Probe's (WM…
Statistical Computations with AstroGrid and the Grid
Robert C Nichol, Garry Smith, Christopher J Miller +6
We outline our first steps towards marrying two new and emerging technologies; the Virtual Observatory (e.g, AstroGrid) and the computational grid. We discuss the construction of V…
Massive Science with VO and Grids
Robert Nichol, Garry Smith, Christopher Miller +7
There is a growing need for massive computational resources for the analysis of new astronomical datasets. To tackle this problem, we present here our first steps towards marrying…
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…
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…
Controlling the False Discovery Rate in Astrophysical Data Analysis
Christopher J. Miller, Christopher Genovese, Robert C. Nichol +6
The False Discovery Rate (FDR) is a new statistical procedure to control the number of mistakes made when performing multiple hypothesis tests, i.e. when comparing many data agains…
Fast Algorithms and Efficient Statistics: N-point Correlation Functions
Andrew Moore, Andy Connolly, Chris Genovese +8
We present here a new algorithm for the fast computation of N-point correlation functions in large astronomical data sets. The algorithm is based on kdtrees which are decorated wit…
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…