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Chad M. Schafer, Ph.D.

Affiliations: 
2004 University of California, Berkeley, Berkeley, CA, United States 
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Philip B. Stark grad student 2004 UC Berkeley
 (Constructing confidence regions of optimal expected size: Theory and application to cosmic microwave inference.)

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Cisewski-Kehe J, Weller G, Schafer C. (2019) A preferential attachment model for the stellar initial mass function Electronic Journal of Statistics. 13: 1580-1607
Wang K, Mao Y, Zentner AR, et al. (2019) How to optimally constrain galaxy assembly bias: supplement projected correlation functions with count-in-cells statistics Monthly Notices of the Royal Astronomical Society. 488: 3541-3567
Metcalf RB, Meneghetti M, Avestruz C, et al. (2019) The Strong Gravitational Lens Finding Challenge Astronomy and Astrophysics. 625
Schafer CM. (2015) A Framework for Statistical Inference in Astrophysics Annual Review of Statistics and Its Application. 2: 141-162
Izbicki R, Lee AB, Schafer CM. (2014) High-dimensional density ratio estimation with extensions to approximate likelihood computation Journal of Machine Learning Research. 33: 420-429
Weyant A, Schafer C, Wood-Vasey WM. (2013) Likelihood-free cosmological inference with type ia supernovae: Approximate bayesian computation for a complete treatment of uncertainty Astrophysical Journal. 764
Schafer CM, Freeman PE. (2013) Likelihood-free inference in cosmology: Potential for the estimation of luminosity functions Information Systems Development: Reflections, Challenges and New Directions. 3-19
Richards JW, Homrighausen D, Freeman PE, et al. (2012) Semi-supervised learning for photometric supernova classification Monthly Notices of the Royal Astronomical Society. 419: 1121-1135
Buchman SM, Lee AB, Schafer CM. (2011) High-dimensional density estimation via SCA: An example in the modelling of hurricane tracks Statistical Methodology. 8: 18-30
Richards JW, Lee AB, Schafer CM, et al. (2010) Prototype selection for parameter estimation in complex models Annals of Applied Statistics. 4: 383-408
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