C
Christopher R. Genovese
Researcher at Carnegie Mellon University
Publications - 108
Citations - 11774
Christopher R. Genovese is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: False discovery rate & Multiple comparisons problem. The author has an hindex of 38, co-authored 108 publications receiving 11196 citations. Previous affiliations of Christopher R. Genovese include Battelle Memorial Institute & National Science Foundation.
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A Non-parametric Analysis of the CMB Power Spectrum
TL;DR: In this paper, a non-parametric estimate of the CMB temperature power spectrum from the BOOMERANG, MAXIMA, and DASI experiments is presented, which is practically indistinguishable from the best fit cosmological model.
Posted Content
Computational AstroStatistics: Fast Algorithms and Efficient Statistics for Density Estimation in Large Astronomical Datasets
Robert C. Nichol,A. J. Connolly,Andrew W. Moore,Jeff Schneider,Christopher R. Genovese,Larry Wasserman +5 more
TL;DR: It is found that EM can accurately recover the underlying density distribution from point processes thus providing an efficient adaptive smoothing method for astronomical source catalogs and a means of identifying outliers in multi-dimensional color-color space.
Book ChapterDOI
Estimation of Correlations in Large Samples
István Szapudi,J. R. Bond,Stéphane Colombi,A. J. Connolly,Christopher R. Genovese,Andrew W. Moore,Robert C. Nichol,Simon Prunet,Dmitry Pogosyan,Jeff Schneider,Alexander S. Szalay,Larry Wasserman +11 more
Book ChapterDOI
Automated Classification Techniques for Large Spectroscopic Surveys
A. J. Connolly,Francisco J. Castander,Christopher R. Genovese,Eric J. Hilton,A. Merrelli,Andrew W. Moore,Robert C. Nichol,Jeff Schneider,Yehuda Snir,Alexander S. Szalay,István Szapudi,Larry Wasserman,Ching-Wa Yip +12 more
TL;DR: In this paper, a series of statistical techniques, ranging from Karhunen-Lo'eve transform to wavelet transforms, are applied to the spectra from the Sloan Digital Sky Survey in order to define a statistically robust and objective spectral classification scheme.
Journal ArticleDOI
Expanding the scope of statistical computing: Training statisticians to be software engineers
TL;DR: In this paper, the syntax of a particular programming language or specific statistical computation methods are discussed. But the focus is on the syntax and semantics of the programming language, not the methods.