Hi everyone!
This week at the Applied Statistics Workshop we will be welcoming *Nan
Laird*, Harvey V. Fineberg Research Professor of Public Health in the
Department of Biostatistics at the Harvard University School of Public
Health. She will be presenting work entitled *Multivariate Problems in
Genetic Analysis**.* Please find the abstract below and on the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations…>
.
As usual, we will meet in CGIS Knafel Room 354 from noon to 1:30pm, and
lunch will be provided. See you all there! To view previous Applied
Statistics presentations, please visit the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/videos>.
-- Aaron Kaufman
Title: Multivariate Problems in Genetic Analysis
Abstract:Complex diseases have multiple underlying contributing factors,
both genetic and environmental. In addition, the disease syndrome is often
characterized by numerous clinical traits that may be analyzed for
association with genes along with the disease status. Genome Wide
Association Analysis (GWAS) has been highly successful in identifying some
genetic loci associated with many disease syndromes and/or selected traits.
The purpose of the analysis of multiple traits may be to show consistency
and thereby strengthen the evidence, or to identify different loci for
different traits, or to gain additional power for new loci. In this talk we
describe an approach to integrating multiple phenotypes based on the
concepts of heritability and co-heritability. Our approach is designed for
GWAS and uses the genetic data both for the estimation of heritability and
using samples of cases and controls and for testing association.
--
Aaron R Kaufman
PhD Candidate, Harvard University
Department of Government
818.263.5583
Hi everyone!
This week at the Applied Statistics Workshop we will be welcoming *Laura
Balzer*, Post-Doctoral Fellow in the Department of Biostatistics at the
Harvard University School of Public Health. She will be presenting work
entitled *Targeted Learning in the SEARCH trial and HIV prevention in East
Africa.* Please find the abstract below and on the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations…>
.
As usual, we will meet in CGIS Knafel Room 354 from noon to 1:30pm, and
lunch will be provided. See you all there! To view previous Applied
Statistics presentations, please visit the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/videos>.
-- Aaron Kaufman
Title: Targeted Learning in the SEARCH trial and HIV prevention in East
Africa
Abstract: Evaluation of community-based interventions presents significant
methodological challenges. In this talk, we describe the design and
analysis of the SEARCH trial, an ongoing community randomized trial to
evaluate the impact of early HIV diagnosis and immediate treatment with
streamlined care in rural East Africa. We focus on 3 choices to optimize
study power: adaptive pair-matching over complete randomization, targeting
the sample average treatment effect instead of a population average
parameter, and data-adaptive adjustment through a pre-specified targeted
maximum likelihood estimator (TMLE). These choices are compared
theoretically and with finite sample simulations. We demonstrate each
choice improves efficiency relative to standard practice, while maintaining
nominal confidence interval coverage.
Hi all,
The Applied Statistics Workshop is on break this week, and will resume on
March 23 with Laura Balzer, Post-Doc in the Department of Biostatistics at
the Harvard TH Chan School of Public Health. See you then!
-Aaron Kaufman
Hi everyone!
This week at the Applied Statistics Workshop we will be welcoming *Stefanie
Jegelka*, Assistant Professor of Electrical Engineering and Computer
Science at MIT. She will be presenting work entitled *Algorithms and new
applications for determinantal point processes**.* Please find the
abstract below and on the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations…>
.
As usual, we will meet in CGIS Knafel Room 354 from noon to 1:30pm, and
lunch will be provided. See you all there! To view previous Applied
Statistics presentations, please visit the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/videos>.
-- Aaron Kaufman
Title: Algorithms and new applications for determinantal point processes
Abstract: Many real-world inference problems are, at their core, subset
selection problems. Probabilistic models for such scenarios rely on having
distributions over discrete sets that are sufficiently accurate yet
computationally efficient to work with. We focus on sub-families of such
distributions whose special mathematical properties are the basis for fast
algorithms. As a specific example, Determinantal Point Processes (DPPs)
have recently become popular in machine learning, as elegant and tractable
probabilistic models of diversity. We explore new applications of DPPs for
variational inference over combinatorial objects, such as coupled cascades
in a collection of networks, where we are able to leverage combinatorial
and convex structure in the problem. In the second part of the talk, I will
outline ideas for speeding up sampling from DPPs. These ideas build on new
insights for algorithms that compute bilinear inverse forms. These results
have applications beyond DPPs, including sensing with Gaussian Processes
and submodular maximization.
