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Spring Semester 2012

Date / Time Speaker Title Location
16 March 2012
15:15-16:30 		Sander Greenland
University of California, Los Angeles
Event Details

Research Seminar in Statistics

Title Integrating Bayesian and frequentist statistics, or: Seeing both sides of the same biased coin.
Speaker, Affiliation Sander Greenland, University of California, Los Angeles
Date, Time 16 March 2012, 15:15-16:30
Location HG G 19.1
Abstract Outlines of a bayes-non-Bayes compromise or fusion have been emerging for decades. Nonetheless, basic teaching remains mired in conventional frequentist methods that are misunderstood and misrepresented by most users (including many statisticians) and that are highly misleading outside of ideal experimental conditions. Thus it is essential to revolutionize how we introduce elementary statistical inference in health and social science, by providing Bayesian concepts and methods in tandem with frequentist concepts and methods. Contrary to prevalent beliefs, basic Bayesian methods require no new computational formulas or software beyond familiar frequentist ones; they do not even require Bayes’ theorem. Those methods can help reveal untenably strong assumptions hidden in conventional methods, and allow relaxation of those assumptions into a more reasonable form. Background cite: Greenland, S. (2009). Relaxation penalties and priors for plausible modeling of non identified bias sources. Statistical Science, 24, 195-210
Integrating Bayesian and frequentist statistics, or: Seeing both sides of the same biased coin.read_more
HG G 19.1
* 20 March 2012
15:15-16:30 		Bin Yu
University of California, Berkeley
Event Details

Research Seminar in Statistics

Title Spectral clustering and high-dim stochastic block model for undirected and directed graphs
Speaker, Affiliation Bin Yu, University of California, Berkeley
Date, Time 20 March 2012, 15:15-16:30
Location HG G 19.1
Abstract In recent years network analysis have become the focus of much research in many fields including biology, communication studies, economics, information science, organizational studies, and social psychology. Communities or clusters of highly connected actors form an essential feature in the structure of several empirical networks. Spectral clustering is a popular and computationally feasible method to discover these communities. The Stochastic Block Model is a social network model with well defined communities. This talk will give conditions for spectral clustering to correctly estimate the community membership of nearly all nodes. These asymptotic results are the first clustering results that allow the number of clusters in the model to grow with the number of nodes, hence the name high-dimensional. Moreover, I will present on-going work on directed spectral clustering for networks whose edges are directed, including the enron data as an example.
Spectral clustering and high-dim stochastic block model for undirected and directed graphsread_more
HG G 19.1
23 March 2012
15:15-16:30 		Marc Hallin
Universität Brüssel
Event Details

Research Seminar in Statistics

Title One-Sided Representations of Generalized Dynamic Factor Models
Speaker, Affiliation Marc Hallin, Universität Brüssel
Date, Time 23 March 2012, 15:15-16:30
Location HG G 19.1
Abstract Factor model methods recently have become extremely popular in the theory and practice of large panels of time series data. Those methods rely on various models which all are particular cases of the Generalized Dynamic Factor Model (GDFM) introduced in Forni, Hallin, Lippi and Reichlin (2000). In that paper, however, estimation relies on Brillinger's concept of dynamic principal components, which produces filters that are in general two-sided and therefore yield poor performances at the end of the observation period and hardly can be used for prediction purposes. In this talk, we show how to remedy this problem, and how, based on recent results on singular stationary processes with rational spectra, one-sided estimators can be constructed for the parameters and the common shocks in the GDFM. Consistency is obtained, along with rates. An empirical example, based on US macroeconomic time series, compares estimates based on our model with those based on the usual static-representation restriction, and provides convincing evidence that the assumptions underlying the latter are not supported by the data.
One-Sided Representations of Generalized Dynamic Factor Modelsread_more
HG G 19.1
30 March 2012
15:15-16:30 		Claudia Czado
TU München
Alexander Bauer
TU München
Event Details

Research Seminar in Statistics

Title Model selection for pair-copula constructions of regular vine and non-Gaussian DAG models
Speaker, Affiliation Claudia Czado, TU München
Alexander Bauer, TU München
Date, Time 30 March 2012, 15:15-16:30
Location HG G 19.1
Abstract Pair-copula constructions (PCCs) allow to build very flexible multivariate statistical models based on a graphical representation called a regular vine (Kurowicka and Cooke, 2006) as well as models represented by directed acyclic graphs (DAGs). PCCs are very useful for modeling multivariate data in economics and finance, since they can capture non-symmetric and different tail dependences for different pairs of variables separately. Vine models are characterized by a sequence of linked trees called a vine-tree structure, bivariate copula families and families of marginal distributions. Two often studied subclasses are C- and D-vines. The multivariate normal and t distribution families are special cases. Moreover, PCCs can be used to construct non-Gaussian DAG models. First, research was focused on the development of efficient estimation methods. For regular vine models see for example Aas et. al. (2009) for likelihood based and Min and Czado (2010) for Bayesian estimation methods. For non-Gaussian DAGs model formulation and estimation methods are considered in Bauer et. al. (2012). Since the class of regular vine models is very large, model selection is vital. Dissmann et. al. (2011) provide a fast selection method in which trees are sampled sequentially using algorithms for weighted graphs. Bayesian alternatives are available. For non-Gaussian DAGs the model selection involves also a data-based selection of the DAG. We provide an alternative approach to the PC algorithm (Spirtes et. al., 2001) based on regular vines to allow for the detection of non-Gaussian dependency structures and compare its performance to the benchmark PC algorithm based on an independence test for zero partial correlation. We will discuss these PCC models and the associated selection methods as well illustrate them in an application to daily stock returns.
Model selection for pair-copula constructions of regular vine and non-Gaussian DAG modelsread_more
HG G 19.1
* 23 April 2012
11:15-12:30 		Michael Gutmann
Dept of Computer Science and Mathematics & Statistics, University of Helsinki
Event Details

Research Seminar in Statistics

Title On the Estimation of Unnormalized Statistical Models
Speaker, Affiliation Michael Gutmann, Dept of Computer Science and Mathematics & Statistics, University of Helsinki
Date, Time 23 April 2012, 11:15-12:30
Location HG G 19.2
Abstract The talk is on the basic problem of estimating, from observed data, a probabilistic model which is parameterized by a finite number of parameters. Focus of the talk is on the particular situation where the model probability density function is unnormalized. That is, the model will not integrate to one for all values of the parameters. Maximum likelihood estimation can then not be used without resorting to numerical approximations which are often computationally expensive. In the talk, I will first give some background on unnormalized models. Then, I will introduce you to a novel method to estimate them, explain some of its properties, and show how it is used in the modeling of images. The talk is based on the following publication: Michael U. Gutmann and Aapo Hyvärinen, Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics, Journal of Machine Learning Research, 13(Feb):307−361, 2012. http://jmlr.csail.mit.edu/papers/v13/gutmann12a.html
On the Estimation of Unnormalized Statistical Modelsread_more
HG G 19.2
1 June 2012
15:15-16:30 		Bodhisattva Sen
Columbia University New York, Cambridge University, UK
Event Details

Research Seminar in Statistics

Title Inference and estimation using nonprametric shape restricted functions
Speaker, Affiliation Bodhisattva Sen, Columbia University New York, Cambridge University, UK
Date, Time 1 June 2012, 15:15-16:30
Location HG G 19.1
Abstract The talk will introduce nonparametric function estimation under known shape constraints. In the fi rst part of the talk, I focus on construction of confidence intervals (CIs) for an unknown non-increasing density function on the positive real line based on the Grenander estimator, the nonparametric maximum likelihood estimator. This estimator, a prototypical example of a class of shape constrained estimators in one dimension, converges at rate cube-root n to a non-normal limit distribution. I investigate the consistency and performance of di fferent bootstrap schemes for constructing point-wise CIs in this setup. In the second part of the talk I consider the nonparametric least squares estimation of a convex regression function when the dimension of the covariate can be greater than one. I characterize and discuss the computation of such an estimator via the solution of certain quadratic and linear programs. I prove that under mild regularity conditions the estimator and its subdiff- erentials are consistent in both fixed and stochastic design regression settings.
Inference and estimation using nonprametric shape restricted functionsread_more
HG G 19.1
5 June 2012
15:15-16:30 		Caroline Uhler
ETH Zürich, SfS
Event Details

Research Seminar in Statistics

Title Geometry of the Faithfulness assumption in causal inference
Speaker, Affiliation Caroline Uhler, ETH Zürich, SfS
Date, Time 5 June 2012, 15:15-16:30
Location HG G 19.1
Abstract Algorithms for inferring causality are heavily based on the Faithfulness assumption. The unfaithful distributions have measure zero and can be seen as a collection of hypersurfaces in a hypercube. The Faithfulness condition alone is not sufficient to guarantee uniform consistency and Strong-Faithfulness has been proposed to overcome this problem. In contrast to the original Faithfulness assumption, the set of distributions satisfying Strong-Faithfulness does not have measure one. We study the (Strong) Faithfulness condition from the point of view of real algebraic geometry and give upper and lower bounds on the proportion of unfaithful distributions for various classes of graphs.
Geometry of the Faithfulness assumption in causal inferenceread_more
HG G 19.1
* 12 June 2012
11:00-12:00 		Volkan Cevher
EPFL
Event Details

Research Seminar in Statistics

Title Learning non-parametric basis independent models from point queries via low-rank methods
Speaker, Affiliation Volkan Cevher, EPFL
Date, Time 12 June 2012, 11:00-12:00
Location CAB H 52
Abstract We consider the problem of actively learning multi-index functions of the form f(x) = g(Ax)= \sum_{i=1}^k g_i(a_i^Tx) from point evaluations of f. We assume that the function f is defined on an l2-ball in R^d, g is twice continuously differentiable almost everywhere, and A \in {R}^{k \times d} is a rank k matrix, where k << d. We propose a randomized, active sampling scheme for estimating such functions with uniform approximation guarantees. Our theoretical developments leverage recent techniques from low rank matrix recovery, which enables us to derive an estimator of the function f along with sample complexity bounds. We also characterize the noise robustness of the scheme, and provide empirical evidence that the high-dimensional scaling of our sample complexity bounds is quite accurate.
Learning non-parametric basis independent models from point queries via low-rank methodsread_more
CAB H 52
22 June 2012
15:15-16:30 		Richard Nickl
Cambridge University
Event Details

Research Seminar in Statistics

Title Donsker's central limit theorem for Estimating Lévy Measures
Speaker, Affiliation Richard Nickl, Cambridge University
Date, Time 22 June 2012, 15:15-16:30
Location HG G 19.1
Abstract see attachment below.
Assets Donsker's central limit theorem for Estimating Lévy Measuresfile_download
Donsker's central limit theorem for Estimating Lévy Measuresread_more
HG G 19.1
* 25 June 2012
10:00-12:00 		Thomas Richardson
University of Washington
Event Details

Research Seminar in Statistics

Title FIM Minicourse on Causal Graphical Models and Counterfactuals
Speaker, Affiliation Thomas Richardson, University of Washington
Date, Time 25 June 2012, 10:00-12:00
Location HG G 19.1
Abstract In this series of lectures we will first introduce the potential outcome approach to causal models. We will then introduce multivariate statistical models based on Directed Acyclic Graphs (DAGs), reviewing their basic Markov properties. We will then provide a causal interpretation for DAGs in terms of potential outcomes, relating this to the back-door formula and more generally, the do-calculus of Pearl. We will then consider two problems that arise in the context of a DAG model when we only observe a marginal distribution. First we consider the non-parametric dentification of causal effects, and describe a simple complete algorithm for this problem. Second, we will describe a general class of non-parametric constraints that are implied to hold in the observed marginal distribution, which we call the nested Markov property. We will describe parameterizations of some of the related statistical models. Finally, time permitting, we will describe approaches that have been proposed for dropping the assumption of acyclicity, describing some of the practical and theoretical obstacles. Prerequisites: Some basic familiarity with statistical problems.
FIM Minicourse on Causal Graphical Models and Counterfactualsread_more
HG G 19.1
* 28 June 2012
10:00-12:00 		Thomas Richardson
University of Washington
Event Details

Research Seminar in Statistics

Title FIM Minicourse on Causal Graphical Models and Counterfactuals
Speaker, Affiliation Thomas Richardson, University of Washington
Date, Time 28 June 2012, 10:00-12:00
Location HG G 19.1
Abstract In this series of lectures we will first introduce the potential outcome approach to causal models. We will then introduce multivariate statistical models based on Directed Acyclic Graphs (DAGs), reviewing their basic Markov properties. We will then provide a causal interpretation for DAGs in terms of potential outcomes, relating this to the back-door formula and more generally, the do-calculus of Pearl. We will then consider two problems that arise in the context of a DAG model when we only observe a marginal distribution. First we consider the non-parametric dentification of causal effects, and describe a simple complete algorithm for this problem. Second, we will describe a general class of non-parametric constraints that are implied to hold in the observed marginal distribution, which we call the nested Markov property. We will describe parameterizations of some of the related statistical models. Finally, time permitting, we will describe approaches that have been proposed for dropping the assumption of acyclicity, describing some of the practical and theoretical obstacles. Prerequisites: Some basic familiarity with statistical problems.
FIM Minicourse on Causal Graphical Models and Counterfactualsread_more
HG G 19.1
29 June 2012
15:15-16:30 		Joseph Salmon
Duke University, Durham, NC, USA
Event Details

Research Seminar in Statistics

Title "Poisson noise reduction with non-local PCA"
Speaker, Affiliation Joseph Salmon, Duke University, Durham, NC, USA
Date, Time 29 June 2012, 15:15-16:30
Location HG G 19.1
Abstract Photon-limited imaging, which arises in applications such as spectral imaging, night vision, nuclear medicine and astronomy, occurs when the number of photons collected by a sensor is small relative to the desired image resolution. Typically a Poisson distribution is used to model these observations, and the inherent heteroscedasticity of the data combined with standard noise removal methods yields significant artifacts. This paper introduces a novel denoising algorithm for photon-limited images which combines elements of dictionary learning and sparse epresentations for image patches. The method employs both an adaptation of Principal Component Analysis (PCA) for Poisson noise. A comprehensive empirical evaluation of the performance of the proposed method helps characterize the performance of this approach in very low light regimes relative to other state-of-the-art denoising methods. The results reveal that, despite its implicity, PCA-flavored denoising appears to be highly competitive in the presence of significant Poisson noise.
"Poisson noise reduction with non-local PCA"read_more
HG G 19.1
* 2 July 2012
10:00-12:00 		Thomas Richardson
University of Washington
Event Details

Research Seminar in Statistics

Title FIM Minicourse on Causal Graphical Models and Counterfactuals
Speaker, Affiliation Thomas Richardson, University of Washington
Date, Time 2 July 2012, 10:00-12:00
Location HG G 19.1
Abstract In this series of lectures we will first introduce the potential outcome approach to causal models. We will then introduce multivariate statistical models based on Directed Acyclic Graphs (DAGs), reviewing their basic Markov properties. We will then provide a causal interpretation for DAGs in terms of potential outcomes, relating this to the back-door formula and more generally, the do-calculus of Pearl. We will then consider two problems that arise in the context of a DAG model when we only observe a marginal distribution. First we consider the non-parametric dentification of causal effects, and describe a simple complete algorithm for this problem. Second, we will describe a general class of non-parametric constraints that are implied to hold in the observed marginal distribution, which we call the nested Markov property. We will describe parameterizations of some of the related statistical models. Finally, time permitting, we will describe approaches that have been proposed for dropping the assumption of acyclicity, describing some of the practical and theoretical obstacles. Prerequisites: Some basic familiarity with statistical problems.
FIM Minicourse on Causal Graphical Models and Counterfactualsread_more
HG G 19.1
* 4 July 2012
10:00-12:00 		Thomas Richardson
University of Washington
Event Details

Research Seminar in Statistics

Title FIM Minicourse on Causal Graphical Models and Counterfactuals
Speaker, Affiliation Thomas Richardson, University of Washington
Date, Time 4 July 2012, 10:00-12:00
Location HG G 19.1
Abstract In this series of lectures we will first introduce the potential outcome approach to causal models. We will then introduce multivariate statistical models based on Directed Acyclic Graphs (DAGs), reviewing their basic Markov properties. We will then provide a causal interpretation for DAGs in terms of potential outcomes, relating this to the back-door formula and more generally, the do-calculus of Pearl. We will then consider two problems that arise in the context of a DAG model when we only observe a marginal distribution. First we consider the non-parametric dentification of causal effects, and describe a simple complete algorithm for this problem. Second, we will describe a general class of non-parametric constraints that are implied to hold in the observed marginal distribution, which we call the nested Markov property. We will describe parameterizations of some of the related statistical models. Finally, time permitting, we will describe approaches that have been proposed for dropping the assumption of acyclicity, describing some of the practical and theoretical obstacles. Prerequisites: Some basic familiarity with statistical problems.
FIM Minicourse on Causal Graphical Models and Counterfactualsread_more
HG G 19.1

Notes: red marked events are important, 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, Leonhard Held, Hans-Rudolf Künsch, Marloes Maathuis, Sara van de Geer, Michael Wolf

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

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