Research Seminar

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Autumn Semester 2010

Date / Time

Speaker

Title

Location

1 October 2010
15:15-16:15

Alex Lenkoski
University Heidelberg

Event Details

Research Seminar in Statistics

Title	Bayesian inference for general Gaussian graphical models with application to multivariate lattice data
Speaker, Affiliation	Alex Lenkoski, University Heidelberg
Date, Time	1 October 2010, 15:15-16:15
Location	HG G 19.1
Abstract	We introduce efficient Markov chain Monte Carlo methods for inference and model determination in multivariate and matrix-variate Gaussian graphical models. Our framework is based on the G-Wishart prior for the precision matrix associated with graphs that can be decomposable or non-decomposable. We extend our sampling algorithms to a novel class of conditionally autoregressive models for sparse estimation in multivariate lattice data, with a special emphasis on the analysis of spatial data. These models embed a great deal of flexibility in estimating both the correlation structure across outcomes and the spatial orrelation structure, thereby allowing for adaptive smoothing and spatial autocorrelation parameters. Our methods are illustrated using simulated and real-world examples, including an application to cancer mortality surveillance. Joint work with Adrian Dobra (University of Washington) and Abel Rodriguez (University of California, Santa Cruz).

Bayesian inference for general Gaussian graphical models with application to multivariate lattice data read_more

HG G 19.1

8 October 2010
15:15-16:15

Peter Hansen
Stanford University

Event Details

Research Seminar in Statistics

Title	A Winner's Curse for Econometric Models: On the Joint Distribution of In-Sample Fit and Out-of-Sample Fit and its Implications for Model Selection
Speaker, Affiliation	Peter Hansen, Stanford University
Date, Time	8 October 2010, 15:15-16:15
Location	HG G 19.1
Abstract	We consider the case where a parameter, theta, is estimated by maximizing a criterion function, Q(X;theta ). The estimate, thetaha t, is then used to evaluate the criterion function with the same data, X, as well as with an independent data set, Y. The in-sample fit and out-of-sample fit relative to that of the true, or quasi-true, parameter, theta, are denoted by eta = Q(X; thetahat) - Q(X; theta) and etatilde = Q(Y; thetahat) - Q(Y; theta* ), respectively. We derive the joint limit distribution of (eta, etatilde ) for a broad class of criterion functions and the joint distribution reveals that eta and etatilde are strongly negatively related. The implication is that good in-sample fit translates into poor out-of-sample fit, one-to-one. The result exposes a winner's curse problem when multiple models are compared in terms of their in-sample fit. The winner's curse has important implications for model selection by standard information criteria such as AIC and BIC.

A Winner's Curse for Econometric Models: On the Joint Distribution of In-Sample Fit and Out-of-Sample Fit and its Implications for Model Selectionread_more

HG G 19.1

19 November 2010
15:15-16:15

Fadoua Balabdaoui
Université Paris Dauphine

Event Details

Research Seminar in Statistics

Title	Distribution of the maximal difference between a Brownian bridge and its concave majorant
Speaker, Affiliation	Fadoua Balabdaoui, Université Paris Dauphine
Date, Time	19 November 2010, 15:15-16:15
Location	HG G 19.1
Abstract	We provide a representation of the maximal difference between a standard Brownian bridge and its concave majorant on the unit interval, from which we deduce expressions for the distribution and density functions and moments of this difference. This maximal difference has an application in nonparametric statistics where it arises in testing monotonicity of a density or regression curve.

Distribution of the maximal difference between a Brownian bridge and its concave majorantread_more

HG G 19.1

26 November 2010
15:15-16:15

Simon Wood
University of Bath

Event Details

Research Seminar in Statistics

Title	REML estimation of penalized GLMs
Speaker, Affiliation	Simon Wood, University of Bath
Date, Time	26 November 2010, 15:15-16:15
Location	HG G 19.1
Abstract	Many flexible regression models can be written as penalized Generalized Linear Models, either because the linear predictor depends on random effects or unknown smooth functions, or both. In this talk I discuss selection of the strength of penalization in such penalized GLMs. The two main alternatives are Marginal Likelihood (or REML) methods and prediction error methods (such as GCV or AIC). These are compared in the light of recent work by Reiss and Ogdon (JRSSB, 2009) suggesting that REML may be preferable to GCV in practice. Computational approaches to REML based smoothness selection are considered, and some numerical problems in computing the Marginal or restricted likelihood are identified and illustrated. A computationally robust strategy for dealing with these issues is then outlined, which is implemented in the R package `mgcv'. The methods are illustrated with examples of generalized additive models and signal regression models.

REML estimation of penalized GLMs read_more

HG G 19.1

3 December 2010
15:15-16:15

Mohamed Hebiri
Seminar für Statistik

Event Details

Research Seminar in Statistics

Title	Goodbye Talk!
Speaker, Affiliation	Mohamed Hebiri, Seminar für Statistik
Date, Time	3 December 2010, 15:15-16:15
Location	HG G 19.1
Abstract	In this talk, I will present some works I have realized during my postdoc in the ETH around the variable selection topic. The main focus is Lasso-type methods. In a first part, we deal with the Lasso with an additional quadratic penalty. Then we present some results obtained on a transductive version of the Lasso. Finally, I will discuss some current works on the graphical model.

Goodbye Talk! read_more

HG G 19.1

10 December 2010
15:15-16:15

Jan Beran
Universität Konstanz

Event Details

Research Seminar in Statistics

Title	On bees, splines and fractional integration - an approach to the analysis of calcium imaging data
Speaker, Affiliation	Jan Beran, Universität Konstanz
Date, Time	10 December 2010, 15:15-16:15
Location	HG G 19.1
Abstract	Olfactory coding is an interesting branch of neurobiology with challenging statistical problems. In this talk, calcium imaging data measuring reactions to olfactory stimuli in the antennal lobe of honeybees is considered. Specific biological questions lead to a combination of regression splines, fractional processes and errors-in-variables regression. Asymptotic results for spline estimators are based on fractional calculus and integration with respect to fractional Brownian motion or Hermite processes. Test statistics for examining the effect of neurotransmitters are derived taking into account errors-in-variables and previously derived limit theorems. This is joint work with Arno Weiershäuser (Department of Mathematics and Statistics, University of Konstanz) and several colleagues from neurobiology (Giovanni Galizia, Julia Rein, Martin Strauch, Brian H. Smith).

On bees, splines and fractional integration - an approach to the analysis of calcium imaging dataread_more

HG G 19.1

* 17 December 2010
14:15-15:00

Caroline Uhler
University of California Berkeley

Event Details

Research Seminar in Statistics

Title	Geometry of maximum likelihood estimation in Gaussian graphical models
Speaker, Affiliation	Caroline Uhler, University of California Berkeley
Date, Time	17 December 2010, 14:15-15:00
Location	HG G 19.1
Abstract	We study maximum likelihood estimation in Gaussian graphical models from the perspective of convex algebraic geometry. It is well-known that the maximum likelihood estimator (MLE) exists with probability one if the number of observations is at least as large as the number of variables. We examine the situation with fewer samples for bipartite graphs and grids. Taking an algebraic approach we find the ﬁrst example of a graph for which the MLE exists with probability one even when the number of observations equals the treewidth of the underlying graph.

Geometry of maximum likelihood estimation in Gaussian graphical modelsread_more

HG G 19.1

* 17 December 2010
15:15-16:15

Victor Panaretos
EPFL Lausanne

Event Details

Research Seminar in Statistics

Title	Second-Order Comparison of Random Functions and DNA Geometry
Speaker, Affiliation	Victor Panaretos, EPFL Lausanne
Date, Time	17 December 2010, 15:15-16:15
Location	HG G 19.1
Abstract	Given two samples of continuous zero-mean iid Gaussian random functions on [0,1], we consider the problem of testing whether they share the same covariance operator. Our study is motivated by the problem of determining whether the mechanical properties of short strands of DNA are significantly affected by their base-pair sequence; though expected to be true, this effect had so far not been observed in three-dimensional electron microscopy data. The testing problem is seen to involve aspects of ill-posed inverse problems and a test based on a Karhunen–Loève approximation of the Hilbert–Schmidt distance of the empirical covariance operators is proposed and investigated. When applied to a dataset of DNA minicircles obtained through the electron microscope, our test seems to suggest potential sequence effects on DNA shape.

Second-Order Comparison of Random Functions and DNA Geometryread_more

HG G 19.1

Notes: events marked with an asterisk (*) indicate that the time and/or location are different from the usual time and/or location and if you want you can subscribe to the iCal/ics Calender.

Organizers: Peter Bühlmann, Reinhard Furrer, Leonhard Held, Hans-Rudolf Künsch, Marloes Maathuis, Werner Stahel, Sara van de Geer

Archive: SS 24 AS 23 SS 23 AS 22 SS 22 AS 21 SS 20 AS 19 SS 19 AS 18 SS 18 AS 17 SS 17 AS 16 SS 16 AS 15 SS 15 AS 14 SS 14 AS 13 SS 13 AS 12 SS 12 AS 11 SS 11 AS 10 SS 10 AS 09