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<p class=MsoPlainText><span style='font-size:11.0pt'>Hello everyone – Just
a quick reminder that the CO/WY 2010 Spring meeting will be held at the end of
this month. Details are included below. A job announcement is also
included below.<o:p></o:p></span></p>
<p class=MsoPlainText><span style='font-size:11.0pt'><o:p> </o:p></span></p>
<p class=MsoPlainText><span style='font-size:11.0pt'>##### REMINDER: SPRING
CO/WY CHAPTER MEETING (April 23, 2010) #####<o:p></o:p></span></p>
<p class=MsoPlainText><span style='font-size:11.0pt'><o:p> </o:p></span></p>
<p class=MsoNormal><span style='font-family:Consolas'>The spring ASA CO/WY
meeting will be held on Friday, April 23, 2010 at the NCAR facility in Boulder,
CO. There is no need to RSVP for this event. We will meet in the
ML-239 Damon Room located at<span style='color:black'> NCAR - 1850 Table Mesa
Dr., Boulder, CO 80305. Directions - Take U.S. Highway 36 to Boulder
Colorado • Exit at Table Mesa Drive • Head West on Table Mesa Drive
• The Mesa Lab and Fleischmann Building are located at 1850 Table Mesa
Drive where the road ends at the base of the Rocky Mountains<o:p></o:p></span></span></p>
<p class=MsoNormal><span style='font-family:Consolas;color:black'><o:p> </o:p></span></p>
<p class=MsoPlainText><span style='font-size:11.0pt'>Event: CO/WY Spring
Meeting 2010<o:p></o:p></span></p>
<p class=MsoPlainText><span style='font-size:11.0pt'>Date: April 23, 2010<o:p></o:p></span></p>
<p class=MsoPlainText><span style='font-size:11.0pt'>Location: NCAR, ML-239
Damon Room<o:p></o:p></span></p>
<p class=MsoPlainText><span style='font-size:11.0pt'>Time: 9:00am –
4:00pm<o:p></o:p></span></p>
<p class=MsoPlainText><span style='font-size:11.0pt'><o:p> </o:p></span></p>
<p class=MsoNormal><span style='font-family:Consolas;color:black'>AGENDA<o:p></o:p></span></p>
<p class=MsoNormal><span style='font-family:Consolas;color:black'>8am-9am:
Refreshments<o:p></o:p></span></p>
<p class=MsoNormal><span style='font-family:Consolas;color:black'>9am-11:30am
Welcome and Presentations<o:p></o:p></span></p>
<p class=MsoNormal><span style='font-family:Consolas;color:black'>11:30am-1:15pm:
Lunch on your own or $5 in the cafeteria<o:p></o:p></span></p>
<p class=MsoNormal><span style='font-family:Consolas'>1:15pm-4pm: Presentations<o:p></o:p></span></p>
<p class=MsoNormal><span style='font-family:Consolas'><o:p> </o:p></span></p>
<p class=MsoNormal><span style='font-family:Consolas'>Speakers<o:p></o:p></span></p>
<p class=MsoNormal><span style='font-family:Consolas'><o:p> </o:p></span></p>
<p class=MsoNormal><b><span style='font-size:10.0pt;font-family:Consolas;
color:black'>Myung-Hee Lee</span></b><span style='font-size:10.0pt;font-family:
Consolas;color:black'> [mhlee.csu@gmail.com]<o:p></o:p></span></p>
<p class=MsoNoSpacing><span style='font-size:10.0pt;font-family:Consolas'>Title:
Clustering High Dimensional, Low Sample Size Data using Maximal Data
Piling: We present new hierarchical clustering method for high
dimension, low sample size (HDLSS) data. The method utilizes the fact that each
individual data vector accounts for exactly one dimension in the subspace
generated by HDLSS data. The linkage that is used for measuring the distance
between clusters is the orthogonal distance between a ne subspaces generated by
each cluster. The ideal implementation would be to consider all possible binary
splits of data and choose the one that maximizes the distance in-between. Since
this is not computationally feasible in general, however, we use singular value
decomposition for its approximation. We provide theoretical justification of
the method by studying high dimensional asymptotics. Also we obtain the
probability distribution of the distance measure under the null hypothesis of
no split, which we use to propose a criterion for determining the number of
clusters. Simulation and real data analyses with microarray data show
competitive clustering performance of the proposed method. (This is a joint
work with Jeongyoun Ahn and Youngju Yoon) <o:p></o:p></span></p>
<p class=MsoNormal><span style='font-size:10.0pt;font-family:Consolas;
color:black'><o:p> </o:p></span></p>
<p class=MsoNormal><b><span style='font-size:10.0pt;font-family:Consolas;
color:black'>A.M. Santos</span></b><span style='font-size:10.0pt;font-family:
Consolas;color:black'> [pintyesantos@gmail.com]<o:p></o:p></span></p>
<p class=MsoPlainText><span style='font-size:10.0pt'>Robust estimation of
mixtures of heavy-tailed distributions A. M. Santos, IBM and UC-Denver Karen
Kafadar, Indiana University</span><span style='font-size:10.0pt'>. </span><span
style='font-size:10.0pt'>We examine the robustness and efficiency of a variant
of the EM algorithm for estimating parameters of mixtures of h-distributions.
Specifically, we use the biweight in an EM-like algorithm to estimate location,
scale, and proportion of each component of a mixture of heavy-tailed
distributions. This family of long-tailed distributions, indexed by a
tail-length parameter (h=0 corresponds to the Gaussian), provides a measure by
which robustness and efficiency can be assessed. We compare our results
to those using the conventional EM-algorithm (that assumes Gaussian
distributions) and with Scott's L_2E (2001) approach. Finally, we
describe the application that motivated this research and our plans for future
work.<o:p></o:p></span></p>
<p class=MsoNormal><span style='font-size:10.0pt;font-family:Consolas;
color:black'><o:p> </o:p></span></p>
<p class=MsoNormal><b><span style='font-size:10.0pt;font-family:Consolas;
color:black'>Jun Zhu</span></b><span style='font-size:10.0pt;font-family:Consolas;
color:black'> [jun.zhu.e@gmail.com]: </span><span style='font-size:10.0pt;
font-family:Consolas'>Variable Selection in Spatial Linear Regression</span><b><span
style='font-size:10.0pt;font-family:Consolas;color:black'><o:p></o:p></span></b></p>
<p class=MsoNormal><span style='font-size:10.0pt;font-family:Consolas;
color:black'><o:p> </o:p></span></p>
<p class=MsoNormal><b><span style='font-size:10.0pt;font-family:Consolas;
color:black'>Bahr, Timothy</span></b><span style='font-size:10.0pt;font-family:
Consolas;color:black'> [TIMOTHY.BAHR@UCDENVER.EDU]<o:p></o:p></span></p>
<p class=MsoNoSpacing><span style='font-size:10.0pt;font-family:Consolas'>SLAM:
Gaussian Dynamic Linear Anaysis of MeDIP-chip Data<o:p></o:p></span></p>
<p class=MsoNoSpacing><span style='font-size:10.0pt;font-family:Consolas'>This
paper presents an efficient and powerful algorithm, GausSian Dynamic Linear
Anaysis of Methylated- chip data (SLAM), for proling DNA methylation patterns
from samples enriched for methylated DNA through immunoprecipitation (MeDIP)
and hybridized on genome tiling microarrays (MeDIP-chip). SLAM is distinguished
from other existing algorithms by its application of MeDIP-chip specific
normalization, dynamic linear smoothing and a Probit transformation in order to
accurately profile the percentage of methylated DNA bases across the genome.
SLAM is the first MeDIP-chip analysis method that can directly compare the DNA
methylation levels of multiple tissue types, as well as being the rst method
appropriate for the analysis of tiling array data for other epigenetic marks,
such as histone methylation or acetlyation. We apply SLAM to several epigenetic
pro ling datasets. Results show that SLAM can provide more efficient, accurate and
consistent methylation estimates than other existing algorithms, implying that
SLAM has the greater potential for biological discovery. SLAM is available as a
command-line application or in a graphical user interface, and is freely
available for download at: http://statistics.byu.edu/johnson/SLAM/.<o:p></o:p></span></p>
<p class=MsoNormal><span style='font-size:10.0pt;font-family:Consolas'><o:p> </o:p></span></p>
<p class=MsoNormal><b><span style='font-size:10.0pt;font-family:Consolas;
color:black'>Amber Hackstadt </span></b><span style='font-size:10.0pt;
font-family:Consolas;color:black'>[ahacksta@lamar.colostate.edu]</span><span
style='font-size:10.0pt;font-family:Consolas'><o:p></o:p></span></p>
<p class=MsoNoSpacing><span style='font-size:10.0pt;font-family:Consolas'>A
Bayesian Approach to Fitting Mixed Models Using Shape Restricted Regression
Splines, Amber Hackstadt, Mary Meyer, and Jennifer Hoeting: We propose a
Bayesian approach to fit shape-restricted regression splines for mixed models.
Linear mixed-effects models are often used to analyze repeated measure and
longitudinal data but often the only thing that is known about the data is the
shape (monotone increasing/decreasing or concave/convex) and smoothness of the
regression functions. Shape-restricted splines are used to model regression
functions in mixed-effects models. <o:p></o:p></span></p>
<p class=MsoNormal><span style='font-size:10.0pt;font-family:Consolas;
color:black'><o:p> </o:p></span></p>
<p class=MsoNormal><b><span style='font-size:10.0pt;font-family:Consolas;
color:black'>Yuan Wang </span></b><span style='font-size:10.0pt;font-family:
Consolas;color:black'>[ywang.csu@gmail.com]</span><span style='font-size:10.0pt;
font-family:Consolas'><o:p></o:p></span></p>
<p class=MsoNormal><span style='font-size:10.0pt;font-family:Consolas'>Carbon
Flux in the Mid-Continent Region Project: The net exchange of CO2 between the
terrestrial biosphere and atmosphere remains a key uncertainty in the carbon
cycle. An important campaign has been launched in the Mid-Continent
region of USA, and our main goal is to compare, diagnose and reconcile two
different estimates of CO2 exchange over the study region.<o:p></o:p></span></p>
<p class=MsoNormal><span style='font-size:10.0pt;font-family:Consolas'><o:p> </o:p></span></p>
<p class=MsoNormal><b><span style='font-size:10.0pt;font-family:Consolas;
color:black'>Bruce Bugbee </span></b><span style='font-size:10.0pt;font-family:
Consolas;color:black'>[brucebugbee@gmail.com]<o:p></o:p></span></p>
<p class=MsoNormal><span style='font-size:10.0pt;font-family:Consolas'>A
popular topic in e commerce research is the observation and quantifying of
bidder behavior in online auction systems. A functional methodology and cluster
analysis was used to quantify bidder behaviors across two distinct item
types--collectibles (1968 Camaros) and commodities (generic digital camera).
Similar behaviors were observed with varying proportions of occurrence
and levels of auction success."</span><span style='font-size:10.0pt;
font-family:Consolas'><o:p></o:p></span></p>
<p class=MsoNormal><span style='font-size:10.0pt'><o:p> </o:p></span></p>
<p class=MsoNormal><span style='font-size:10.0pt'><o:p> </o:p></span></p>
<p class=MsoPlainText><span style='font-size:11.0pt'>##### JOB ANNOUCEMENT
#####<o:p></o:p></span></p>
<p class=MsoPlainText><span style='font-size:11.0pt'><o:p> </o:p></span></p>
<p class=MsoPlainText>Job Posting: Instructor in the Business School at the
University of Colorado Denver.<o:p></o:p></p>
<p class=MsoPlainText>We're seeking a full-time instructor to teach
undergraduate and graduate (master's level) statistics courses. More
information about the position is available at <a
href="https://www.jobsatcu.com/applicants/jsp/shared/position/JobDetails_css.jsp?postingId=221403">https://www.jobsatcu.com/applicants/jsp/shared/position/JobDetails_css.jsp?postingId=221403</a>.
The job posting number is 809565.<o:p></o:p></p>
<p class=MsoPlainText><span style='font-size:11.0pt'><o:p> </o:p></span></p>
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