AstroStat Summer School [Announcement]

From Jogesh Babu:

First Announcement

Summer School in Statistics for Astronomers VI
June 7-12, 2010
with a supplement on Statistics and Computation for Astronomical Surveys
June 12-14, 2010
Registration Deadline: May 3, 2010 or when the enrollment limit reaches.
Penn State University

The sixth annual Penn State Summer School in Statistics for Astronomers will be held at Penn State. The main part of the School is a 6-day course (June 7-12, 2010) in fundamental statistical inference designed to provide researchers and graduate students in the physical sciences with a strong conceptual foundation in modern statistics. We develop a repertoire of well-established techniques applicable to observational astronomy and physics. Classroom instruction is interspersed with hands-on analysis of astronomical data using the open-source R software package. The course is taught by a team of statistics and astronomy professors with opportunity for discussion of methodological issues. The program starts on Monday morning (June 7, 2010), and ends on Saturday June 12, 2010 at noon. The topics covered include:

* Exploratory data analysis
* Hypothesis testing and parameter estimation
* Regression
* Bootstrap resampling
* Model selection & goodness-of-fit
* Maximum likelihood methods & Bayes’ Theorem
* Non-parametric methods
* Monte Carlo methods
* Poisson processes
* Time series

The 2010 Summer School will be modeled on the last four Penn State Summer Schools and the two Indian Institute of Astrophysics-Penn State Summer School; see 2005, 2006, 2007, 2008 and 2009 lecture notes for the Penn State Summer Schools.

This is immediately followed by a supplementary program (June 12-14, 2010) on Statistics and Computation for Astronomical Surveys. This program starts on Saturday June 12 immediately following the main school and ends on Monday June 14 at noon. Statistical topics covered will include:

* Number count distributions (“logN-logS”) and the fundamental equation of stellar statistics
* Selection effects: truncation and censoring (Lynden-Bell, Kaplan-Meier product limit estimators)
* Classical survey biases (Eddington, Malmquist, Lutz-Kelker)
* Population modeling with hierarchical models
* Statistical cross-matching between surveys
* Introduction to Virtual Observatory software tools for querying and analyzing survey data

Participants may register for one or both programs. There is limited financial support for the program on astronomical surveys; requests for support should be sent to Tom Loredo (loredo, at by May 3.

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