C. Schafer

发表

Accurate parameter estimation for star formation history in galaxies using SDSS spectra

Ann B. Lee, J. Richards, P. Freeman, 2009, 0905.4683.

Likelihood-Free Inference in Cosmology: Potential for the Estimation of Luminosity Functions

P. Freeman, C. Schafer, 2012 .

Efficiently computing minimax expected-size confidence regions

Jeff G. Schneider, Chad M. Schafer, H. Brendan McMahan, 2007, ICML '07.

LIKELIHOOD-FREE COSMOLOGICAL INFERENCE WITH TYPE Ia SUPERNOVAE: APPROXIMATE BAYESIAN COMPUTATION FOR A COMPLETE TREATMENT OF UNCERTAINTY

W. M. Wood-Vasey, W. Wood-Vasey, C. Schafer, 2012, 1206.2563.

EXPLOITING LOW-DIMENSIONAL STRUCTURE IN ASTRONOMICAL SPECTRA

Ann B. Lee, J. Richards, P. Freeman, 2008, 0807.2900.

Synaptic, transcriptional, and chromatin genes disrupted in autism

Christopher S. Poultney, Stephan J Sanders, Stephen J. Guter, 2014, Nature.

Constructing Confidence Regions of Optimal Expected Size

Philip B. Stark, Chad M. Schafer, P. Stark, 2009 .

How to optimally constrain galaxy assembly bias: supplement projected correlation functions with count-in-cells statistics

Andrew P. Hearin, C. Schafer, A. Zentner, 2019, Monthly Notices of the Royal Astronomical Society.

FOAM : Expanding the Horizons of Climate

C. Schafer, R. Jacob, John R. Anderson, 2008 .

Powerful Choices: Tuning Parameter Selection Based on Power

K. Doksum, C. Schafer, 2006 .

A Framework for Statistical Inference in Astrophysics

C. Schafer, 2015 .

Semi-supervised learning for photometric supernova classification★

Chad M. Schafer, Dovi Poznanski, Peter E. Freeman, 2011, 1103.6034.

Realizing the potential of astrostatistics and astroinformatics

C. A. Young, E. Feigelson, T. Loredo, 2019, 1909.11714.

Selecting local models in multiple regression by maximizing power

K. Doksum, C. Schafer, 2006, math/0612248.

A Flexible Pipeline for Prediction of Tropical Cyclone Paths

C. Schafer, Robin Dunn, Niccolò Dalmasso, 2019, 1906.08832.

FOBOS: A Next-Generation Spectroscopic Facility at the W. M. Keck Observatory

J. Rhodes, D. Schlegel, M. White, 2019, 1907.07195.

High-Dimensional Density Ratio Estimation with Extensions to Approximate Likelihood Computation

Rafael Izbicki, Chad Schafer, Ann B. Lee, 2014, AISTATS.

Whole exome sequencing reveals minimal differences between cell line and whole blood derived DNA.

K. Roeder, M. Daly, Seungtai Yoon, 2013, Genomics.

Exome Analyses Reveal New Autism Genes in Synaptic, Transcriptional, and Chromatin Networks Supplementary Methods

Christopher S. Poultney, Stephan J Sanders, Evan T. Geller, 2022 .

Prototype selection for parameter estimation in complex models

Chad M. Schafer, Ann B. Lee, Peter E. Freeman, 2011, 1105.6344.

A preferential attachment model for the stellar initial mass function

Chad Schafer, Jessi Cisewski-Kehe, Grant B. Weller, 2019, Electronic Journal of Statistics.

Using what we know: Inference with Physical Constraints

Philip B. Stark, Chad M. Schafer, P. Stark, 2003 .

A Statistical Method for Estimating Luminosity Functions Using Truncated Data

C. Schafer, 2007, astro-ph/0702401.

Maximizing Science in the Era of LSST: A Community-Based Study of Needed US Capabilities

Puragra Guhathakurta, Elise Jennings, David Trilling, 2016, 1610.01661.

Programmed Biologically Inspired Synthetic Templating of Multifunctional Nanoarchitectures for Small-Scale Reactions

Jonathan E. Didier, C. Schafer, P. Leduc, 2012 .

FOBOS: A Next-Generation Spectroscopic Facility

J. Rhodes, D. Schlegel, M. White, 2019 .

High-Dimensional Density Estimation via SCA: An Example in the Modelling of Hurricane Tracks ✩

Chad M. Schafer, Ann B. Lee, C. Schafer, 2009, 0907.0199.

Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era pdf

Lior Shamir, Ricardo Vilalta, Yen-Chi Chen, 2019, ArXiv.

Better support for collaborations preparing for large-scale projects: the case study of the LSST Science Collaborations Astro2020 APC White Paper.

Federica B. Bianco, Manda Banerji, John Bochanski, 2019, 1907.09027.

Constructing Confidence Regions of Optimal Expected Size Chad

P. Stark, C. Schafer, Cmu, 2015 .

FOAM: Expanding the Horizons of Climate Modeling

M. Tobis, C. Schafer, I. Foster, 1997, ACM/IEEE SC 1997 Conference (SC'97).

Constraining Stellar Multiplicity with Approximate Bayesian Computation

H. Eric Alpert, C. Schafer, C. Badenes, 2016 .

Computational Design and Performance of the Fast Ocean Atmosphere Model, Version One

John Anderson, Ian T. Foster, Robert L. Jacob, 2001, International Conference on Computational Science.

Semiparametric Bivariate Density Estimation with Irregularly Truncated Data

Chad M. Schafer, C. Schafer, 2006 .

Exploring Coupled Atmosphere-Ocean Models Using Vis5D

John Anderson, Ian T. Foster, Robert L. Jacob, 1996, Int. J. High Perform. Comput. Appl..