gov3009-l February 2012

gov3009-l@lists.fas.harvard.edu

1 participants
4 discussions

Applied Statistics Workshop: Hanspeter Pfister on Wed., February 29

by Konstantin Kashin

Dear all, Please join us for the Applied Statistics Workshop (Gov 3009) this Wednesday, February 29 from 12.00 - 1.30 pm in CGIS Knafel Room 354. Hanspeter Pfister <http://gvi.seas.harvard.edu/pfister>, Gordon McKay Professor of Computer Science at the School of Engineering and Applied Sciences at Harvard University, will give a presentation entitled "Visual Computing in Biology". As always, a light lunch will be provided. Abstract: > Many areas in science are experiencing a flood of data arising in part > from the development of instruments that acquire information on an > unprecedented scale. This is particularly true in biology, where huge > amounts of heterogeneous data are acquired from microarrays, scanners, > microscopes, and various other instruments. Visual computing tools are > essential to gain insights into this data by combining computational > analysis with the power of the human perceptual and cognitive system and > enabling data exploration through interactive visualizations. In this talk > I will present some of my group's work in visual computing and give an > overview of several successful visualization projects in the areas of > genomics and systems biology. I then will focus on our work on visual > computing in Connectomics, a new field in neuroscience that aims to apply > biology and computer science to the grand challenge of determining the > detailed neural circuitry of the brain. An up-to-date schedule for the workshop is available at http://www.iq.harvard.edu/events/node/1208. Best, Konstantin -- Konstantin Kashin Ph.D. Student in Government Harvard University Mobile: 978-844-0538 E-mail: kkashin(a)fas.harvard.edu Site: http://www.konstantinkashin.com/<http://people.fas.harvard.edu/%7Ekkashin/>

12 years, 2 months

Applied Statistics Workshop: Francesca Dominici on Wed., February 22

by Konstantin Kashin

Dear all, Please join us for the Applied Statistics Workshop (Gov 3009) this Wednesday, February 22 from 12.00 - 1.30 pm in CGIS Knafel Room 354. Francesca Dominici <http://www.hsph.harvard.edu/faculty/francesca-dominici/>, Professor of Biostatistics at the Harvard School of Public Health, will give a presentation entitled "Bayesian Effect Estimation Accounting for Adjustment Uncertainty". As always, a light lunch will be provided. Abstract: Model-based estimation of the effect of an exposure on an outcome is > generally sensitive to the choice of which confounding factors are included > in the model. We propose a new approach, which we call Bayesian Adjustment > for Confounding (BAC), to estimate the effect on the outcome associated > with an exposure of interest while accounting for the uncertainty in the > confounding adjustment. Our approach is based on specifying two models: 1) > the outcome as a function of the exposure and the potential confounders > (the outcome model); and 2) the exposure as a function of the potential > confounders (the exposure model). We consider Bayesian variable selection > on both models and link the two by introducing a dependence parameter ω > denoting the prior odds of including a predictor in the outcome model, > given that the same predictor is in the exposure model. In the absence of > dependence (ω = 1), BAC reduces to traditional Bayesian Model Averaging > (BMA). In simulation studies we show that BAC with ω > 1 estimates the > exposure effect with smaller bias than traditional BMA, and improved > coverage. We then compare BAC, a recent approach of Crainiceanu et al. > (2008), and traditional BMA in a time series data set of hospital > admissions, air pollution levels and weather variables in Nassau, NY for > the period 1999-2005. Using each approach, we estimate the short-term > effects of PM2.5 on emergency admissions for cardiovascular diseases, > accounting for confounding. This application illustrates the potentially > signiﬁcant pitfalls of misusing variable selection methods in the context > of adjustment uncertainty. An up-to-date schedule for the workshop is available at http://www.iq.harvard.edu/events/node/1208. Best, Konstantin -- Konstantin Kashin Ph.D. Student in Government Harvard University Mobile: 978-844-0538 E-mail: kkashin(a)fas.harvard.edu Site: http://www.konstantinkashin.com/<http://people.fas.harvard.edu/%7Ekkashin/>

12 years, 2 months

Applied Statistics Workshop: Tamar Sofer on Wed., February 15

by Konstantin Kashin

Dear all, Please join us for the Applied Statistics Workshop (Gov 3009) this Wednesday, February 15 from 12.00 - 1.30 pm in CGIS Knafel Room 354. Tamar Sofer, a Ph.D. student from the Department of Biostatistics at Harvard University, will give a presentation entitled "Sparse Joint Estimation of Covariates-Dependent Covariance Matrices". As always, a light lunch will be provided. Abstract: We propose an estimation method for the principal components/covariance > structures of a set of outcomes, while modeling the effect of covariates. > We assume a linear mixed model formulation on the outcomes as response to > covariates, a model corresponding to spiked covariance matrices. Since the > subject-specific covariance matrices and the effects of covariates are > believed to be sparse, we penalize coefficients using an oracle penalty > function. Under some assumptions on the parameters and the likelihood, we > show that the maximum likelihood estimator of the parameters is > asymptotically consistent and is uniformly sparse ("sparsistent"), even > when the number of parameters is small. We propose using the Bayesian > Information Criterion (BIC) for tuning parameter selection and show that it > is consistent for model selection. Using a simple iterated least squares > procedure we are able to recover the model parameters with high accuracy. > The method is implemented to study the effect of smoking on the covariances > of gene methylations in the asthma pathway in smokers and non-smokers US > veterans from the Normative Aging Study (NAS). An up-to-date schedule for the workshop is available at http://www.iq.harvard.edu/events/node/1208. Best, Konstantin -- Konstantin Kashin Ph.D. Student in Government Harvard University Mobile: 978-844-0538 E-mail: kkashin(a)fas.harvard.edu Site: http://people.fas.harvard.edu/~kkashin/

12 years, 2 months

Applied Statistics Workshop: Rocio Titiunik on Wed., February 8

by Konstantin Kashin

Dear all, Please join us for the Applied Statistics Workshop (Gov 3009) this Wednesday, February 8 from 12.00 - 1.30 pm in CGIS Knafel Room 354. Rocio Titiunik, Assistant Professor from the Department of Political Science at the University of Michigan, will give a presentation entitled "Using Regression Discontinuity to Uncover the Personal Incumbency Advantage". As always, a light lunch will be provided. Abstract: We study the conditions under which estimating the incumbency advantage > using a regression discontinuity (RD) design recovers the personal > incumbency advantage in a two-party system. Lee (2008) has introduced RD as > a method for estimating what is generally considered the "partisan" > incumbency advantage. We present a causal model with some simple but > plausible assumptions that allows RD to be used to estimate the "personal" > incumbency advantage, as an alternative to sophomore surge, retirement > slump, and other commonly used measures. We estimate the incumbency > advantage using our model with data from U.S. House elections between 1952 > and 2008. Using the assumptions of our model, we also explore the > estimation of the incumbency advantage beyond the limited RD conditions > where knife-edge electoral shifts create the leverage for causal inference. This is joint work with Robert Erikson. An up-to-date schedule for the workshop is available at http://www.iq.harvard.edu/events/node/1208. Best, Konstantin -- Konstantin Kashin Ph.D. Student in Government Harvard University Mobile: 978-844-0538 E-mail: kkashin(a)fas.harvard.edu Site: http://people.fas.harvard.edu/~kkashin/

12 years, 2 months

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gov3009-l February 2012