# Social Science Statistics Blog

February 2012
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27 February 2012

### App Stats: Pfister on "Visual Computing in Biology"

We hope you can join us this Wednesday, February 29, 2012 for the Applied Statistics Workshop. Hanspeter 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". A light lunch will be served at 12 pm and the talk will begin at 12.15.

"Visual Computing in Biology"
Hanspeter Pfister
School of Engineering and Applied Sciences, Harvard University
CGIS K354 (1737 Cambridge St.)
Wednesday, February 29th, 2012 12.00 pm

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.

Posted by Konstantin Kashin at 1:19 AM

20 February 2012

### App Stats: Dominici on "Bayesian Effect Estimation Accounting for Adjustment Uncertainty"

We hope you can join us this Wednesday, February 22, 2012 for the Applied Statistics Workshop. Francesca Dominici, Professor of Biostatistics from the Department of Biostatistics at the Harvard School of Public Health, will give a presentation entitled "Bayesian Effect Estimation Accounting for Adjustment Uncertainty". A light lunch will be served at 12 pm and the talk will begin at 12.15.

"Bayesian Effect Estimation Accounting for Adjustment Uncertainty"
Francesca Dominici
Department of Biostatistics, Harvard School of Public Health
CGIS K354 (1737 Cambridge St.)
Wednesday, February 22nd, 2012 12.00 pm

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.

Posted by Konstantin Kashin at 2:03 AM

13 February 2012

### App Stats: Sofer on "Sparse Joint Estimation of Covariates-Dependent Covariance Matrices"

We hope you can join us this Wednesday, February 15, 2012 for the Applied Statistics Workshop. 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". A light lunch will be served at 12 pm and the talk will begin at 12.15.

"Sparse Joint Estimation of Covariates-Dependent Covariance Matrices"
Tamar Sofer
Department of Biostatistics, Harvard University
CGIS K354 (1737 Cambridge St.)
Wednesday, February 15th, 2012 12.00 pm

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).

Posted by Konstantin Kashin at 2:15 AM

6 February 2012

### App Stats: Titiunik on "Using Regression Discontinuity to Uncover the Personal Incumbency Advantage"

We hope you can join us this Wednesday, February 8, 2012 for the Applied Statistics Workshop. 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". A light lunch will be served at 12 pm and the talk will begin at 12.15.

"Using Regression Discontinuity to Uncover the Personal Incumbency Advantage"
Rocio Titiunik
Department of Political Science, University of Michigan
CGIS K354 (1737 Cambridge St.)
Wednesday, February 8th, 2012 12.00 pm

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.

Posted by Konstantin Kashin at 1:21 AM