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X-WR-CALDESC:Events for UCLA Center for Social Statistics
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DTSTART;TZID=America/Los_Angeles:20160329T143000
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SUMMARY:Betsy Sinclair\, Washington University in St Louis
DESCRIPTION:The Center for Social Statistics Presents:\n\n\nElectronic Homestyle: Tweeting Ideology\nAbstract: Ideal points are central to the study of political partisanship and an essential component to our understanding of legislative and electoral behavior. We employ automated text analysis on tweets from Members of Congress to estimate their ideal points using Naive Bayes classification and Support Vector Machine classification. We extend these tools to estimate the proportion of partisan speech used in each legislator’s tweets. We demonstrate an association between these measurements\, existing ideal point measurements\, and district ideology. \n 
URL:https://css.stat.ucla.edu/event/betsy-sinclair-washington-university-st-louis/
LOCATION:CA
CATEGORIES:css seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20151113T120000
DTEND;TZID=America/Los_Angeles:20151113T133000
DTSTAMP:20260502T080019
CREATED:20170324T180355Z
LAST-MODIFIED:20170501T204132Z
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SUMMARY:Reproducibility of Statistical Results
DESCRIPTION:The Center for Social Statistics Presents:\n\n\nMark S. Handcock \n(Professor\, Statistics) \nJeffrey B. Lewis \n(Professor\, Political Science) \nMarc A. Suchard \n(Professor\, Biomathematics\, Biostatistics and Human Genetics) \nAbstract: \nReproducibility is one of the main principles of the scientific method. This panel of scholars will discuss issues in the importance of replication of statistical results. Increasing attention is being paid to improve reporting and hence reproducibility in the social and medical sciences. This panel will discuss some key concerns in study replication\, initiatives for increasing replication\, and then open the floor to discussion of how we move forward as a scientific community.
URL:https://css.stat.ucla.edu/event/reproducibility-statistical-results/
LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:css seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20151015T120000
DTEND;TZID=America/Los_Angeles:20151015T133000
DTSTAMP:20260502T080019
CREATED:20170323T224804Z
LAST-MODIFIED:20170501T204012Z
UID:816-1444910400-1444915800@css.stat.ucla.edu
SUMMARY:Aude Hofleitner\, Facebook
DESCRIPTION:The Center for Social Statistics Presents:\nInferring and understanding travel and migration movements at a global scale\nAbstract: Despite extensive work on the dynamics and outcomes of large-scale migrations\, timely and accurate estimates of population movements do not exist. While censuses\, surveys\, and observational data have been used to measure migration\, estimates based on these data sources are constrained in their inability to detect unfolding migrations\, and lack temporal and demographic detail. In this study\, we present a novel approach for generating estimates of migration that can measure movements of particular demographic groups across country lines. \nSpecifically\, we model migration as a function of long-term moves across countries using aggregated Facebook data. We demonstrate that this methodological approach can be used to produce accurate measures of past and ongoing migrations – both short-term patterns and long-term changes in residence. Several case studies confirm the validity of our approach\, and highlight the tremendous potential of information obtained from online platforms to enable novel research on human migration events. \nIf you are interested in meeting with or joining the speaker for lunch\, please send email to Seminars@ccpr.ucla.edu
URL:https://css.stat.ucla.edu/event/aude-hofleitner-facebook/
LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:css seminar
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DTSTART;TZID=America/Los_Angeles:20150623T100000
DTEND;TZID=America/Los_Angeles:20150623T120000
DTSTAMP:20260502T080019
CREATED:20170324T180102Z
LAST-MODIFIED:20170501T204051Z
UID:854-1435053600-1435060800@css.stat.ucla.edu
SUMMARY:Bayesian Statistical Modeling Using Stan
DESCRIPTION:The Center for Social Statistics Presents:\n\nInstructor: Daniel Lee\, Columbia University\n\nAbstract:\nStan is an open-source\, Bayesian inference tool with interfaces in R\, Python\, Matlab\, Julia\, Stata\, and the command line. Users write statistical models in a high-level statistical language. The default Bayesian inference algorithm is the no-U-turn sampler (NUTS)\, an auto-tuned version of Hamiltonian Monte Carlo. Stan was developed to address the speed and scalability issues of existing Bayesian inference tools. The goal of the workshop is the practical application of Stan to different models starting with ordinary linear regression and ending with more complex models such as generalized linear mixed and hierarchical models. Experience in Bayesian statistical modeling is recommended\, but not required. Workshop participants should bring their own laptop installed with RStan (or some other Stan interface). See http://mc-stan.org/ for instructions on how to install it.\n\nThe workshop is open\, but UCLA community members are especially welcomed.  To enroll for the workshop\, please go to: http://goo.gl/forms/CYHSj8Vkcw\n\nThe workshop is co-sponsored by the UCLA Department of Statistics\, the UCLA Department of Political Science\, and the Department of Biostatistics at UCLA.
URL:https://css.stat.ucla.edu/event/bayesian-statistical-modeling-using-stan/
LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:ccpr workshop,css workshop
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