Presentations 

Fall/Winter 20042005 
David van Dyk (UC Irvine) 20 Sep 2005  Introduction 
Michael Ratner (CfA) Liu Jing Chen (Harvard U) 04 Oct 2005  On locating IM Peg for Gravity Probe B 
Jin Jia Shun (Purdue U) 11 Oct 2005 
 Higher Criticism Statistic: Theory and Applications in Cosmology and Astronomy [.pdf]
 Abstract [.pdf]

 Of interest:
 Higher Criticism for Detecting Sparse Heterogeneous Mixtures
 Donoho, D., & Jin, J., Ann. Statist., Vol 32, 3, 962994
 Cosmological nonGaussian Signature Detection: Comparing Performance of Different Statistical Tests
 Jin et al. 2005, astroph/0503374
 [.pdf]

Park Tae Young (Harvard U) 25 Oct 2005 
 Fitting Narrow Emission Lines in Xray Spectra [.pdf]
 Abstract:
Spectral emission lines are local features that represent
extra emissions of photons in a narrow band of energy. In
a statistical model, it is often appropriate to model the
emission lines with a narrow Gaussian function or a delta
function. In this article, we show how to identify the
location of the narrow line profiles using a modelbased
Bayesian statistical perspective. Such Bayesian methods are
ideally suited to handling the complexity of highresolution
highenergy spectral data such as that obtained with the
Chandra Xray Observatory. van Dyk et al (2001) show how
Bayesian methods can account for these complexities of the
data generation mechanism as well as the Poisson nature of
photon count data. The multimodal nature of the likelihood
function poses difficulties for these methods, however, when
the location and width of a spectral line are simultaneously
fitted or when delta functions are used to model spectral
lines. These difficulties necessitate more sophisticated,
stateoftheart statistical computation. We thus develop
such methods and illustrate how to detect narrow spectral
lines in Xray spectra using Chandra data sets for the energy
spectrum of the high redshift quasar PG 1634+706.

Chandra Calibration Workshop 01 Nov 2005 1:30pm4:30pm  Special Session on
Incorporating Calibration Uncertainties into Data Analysis http://cxc.harvard.edu/ccw/ 
Andreas Zezas (SAO) 29 Nov 2005 
 Xray data analysis techniques
 Presentation [.ppt]

Hong Jae Sub (SAO) 7 Feb 2006 12 Noon  1 PM 
 New spectral classification technique for faint Xray sources:
Quantile Analysis
 [.ppt]
 Abstract:
 We describe a new spectral classification technique called quantile
analysis for Xray sources with limited statistics. The quantile
analysis is superior to the conventional approaches such as Xray
hardness ratios or Xray color analysis. The median is considered to
be an improved substitute for the conventional Xray hardness ratio
and the quantilebased phase diagram is more evenly sensitive over
various spectral shapes than the conventional colorcolor diagrams. We
demonstrate the new technique by simulations using Chandra ACIS detector
response function and the analysis results from the deep observations
at the galactic center.
 Links:
 astroph/0406463
 QCCD code
 ChaMPlane

Aneta Siemiginowska (SAO) & Vinay Kashyap (SAO) 8 Feb 2006 12:30 Noon  1:30 PM HEAD Lunch Talk 
 Xray Astrostatistics: Bayesian Methods in Data Analysis
 Abstract:
 We will describe the CaliforniaHarvard AstroStatistics Collaboration,
CHASC. We will provide an introduction to Bayesian methods in the
context of some basic Xray astrophysics problems, such as determining
the source strength in the presence of background, and hardness ratios
in the regime of (very) low counts. We will also discuss posterior
predictive pvalues (PPP), which are the preferred alternatives to
the often abused Ftests used for model comparisons.
 AS's slides:
 [.ppt] ; [.pdf]
 VK's slides:
 [.ppt] ; [.pdf]

Meng XiaoLi (Harvard U) 25 Apr 2006 11amNoon 
 A Brief Tutorial of Markov Chain Monte Carlo:
A Workhorse for Modern Scientific Computation
 Abstract:
 The Markov chain Monte Carlo (MCMC) methods, originating
in computational physics about half a century ago, have
seen an enormous range of applications in recent statistical
literature, due to their ability to simulate from very complex
distributions such as the ones needed in realistic statistical models.
This talk provides an introductory tutorial of
the two most frequently used MCMC algorithms:
the Gibbs sampler and the MetropolisHastings algorithm.
Using simple yet nontrivial examples, we show,
step by step, how to implement these two algorithms. The examples
involve a family of bivariate distributions whose full conditional
distributions are all normal but whose joint densities are
not only nonnormal, but also bimodal.
 Presentation:
 [.ppt] ; [.pdf]
 Movies:
 symmetric, Gibbs [.avi]
 asymmetric, Gibbs, bad implementation [.avi]
 asymmetric, Gibbs, better implementation [.avi]

Hyunsook Lee (Penn State) 7 Sep 2006 
 A Convex Hull Peeling Depth Approach to Nonparametric
Massive Multivariate Data Analysis with Applications
 Abstract:
We explore the convex hull peeling process to develop
empirical tools for statistical inferences on multivariate massive data.
Convex hull and its peeling process has intuitive appeals for robust
location estimation. We define the convex hull peeling depth,
which enables to order multivariate data. This ordering process
provides ways to obtain multivariate quantiles including
median. Based on the generalized quantile process, we define
a convex hull peeling central region, a convex hull level set, and
a volume functional, which lead us to invent one dimensional mappings,
describing shapes of multivariate distributions along data depth.
We define empirical skewness and kurtosis measures
based on the convex hull peeling process.
In addition to these empirical descriptive statistics, we find
a few methodologies to separate multivariate outliers in massive data sets.
Those outlier detection algorithms
are (1) estimating multivariate quantiles up to the level $\alpha$,
(2) detecting changes in a measure sequence of convex hull level sets,
and (3) constructing a balloon to exclude outliers. The convex hull peeling
depth is a robust estimator so that
the existence of outliers do not affect properties of inner convex hull
level sets. Overall, we illustrate all these characteristics and algorithms
of the convex hull peeling process through bivariate synthetic data sets.
We show that these empirical procedures are applicable to
real massive data set by employing Quasars and galaxies from the Sloan
Digital Sky Survey.
 Presentation [.pdf]
