Seminar for Statistics

Search

Research Seminar

Would you like to be notified about these presentations via e-mail? Please subscribe here.

×

Modal title

Modal content

Autumn Semester 2017

Date / Time Speaker Title Location
20 October 2017
15:15-16:00 		Giuseppe Cavaliere
University of Bologna
Event Details

Research Seminar in Statistics

Title Bootstrap Inference under Random Distributional Limits
Speaker, Affiliation Giuseppe Cavaliere, University of Bologna
Date, Time 20 October 2017, 15:15-16:00
Location HG G 19.2
Abstract Asymptotic bootstrap validity is usually understood as consistency of the distribution of a bootstrap statistic, conditional on the data, for the unconditional limit distribution of a statistic of interest. From this perspective, randomness of the limit bootstrap measure is regarded as a failure of the bootstrap. Nevertheless, apart from an unconditional limit distribution, a statistic of interest may possess a host of (random) conditional limit distributions. This allows the understanding of bootstrap validity to be widened, while maintaining the requirement of asymptotic control over the frequency of correct inferences. First, we provide conditions for the bootstrap to be asymptotically valid as a tool for conditional inference, in cases where a bootstrap distribution estimates consistently, in a sense weaker than the standard weak convergence in probability, a conditional limit distribution of a statistic. Second, we prove asymptotic bootstrap validity in a more basic, on-average sense, in cases where the unconditional limit distribution of a statistic can be obtained by averaging a (random) limiting bootstrap distribution. As an application, we establish rigorously the validity of fixed-regressor bootstrap tests of parameter constancy in linear regression models.
Bootstrap Inference under Random Distributional Limitsread_more
HG G 19.2
3 November 2017
15:15-16:00 		Zoltan Szabo
Université Paris-Sarclay
Event Details

Research Seminar in Statistics

Title Tensor Product Kernels: Characteristic Property and Universality
Speaker, Affiliation Zoltan Szabo, Université Paris-Sarclay
Date, Time 3 November 2017, 15:15-16:00
Location HG G 19.1
Abstract Maximum mean discrepancy (MMD) and Hilbert-Schmidt independence criterion (HSIC) are among the most popular and successful approaches in applied mathematics to measure the difference and the independence of random variables, respectively. Thanks to their kernel-based foundations, MMD and HSIC are applicable on a large variety of domains such as documents, images, trees, graphs, time series, dynamical systems, sets or permutations. Despite their tremendous practical success, quite little is known about when HSIC characterizes independence and MMD with tensor kernel can discriminate probability distributions, in terms of the contributing kernel components. In this talk, I am going to provide a complete answer to this question, with conditions which are often easy to verify in practice. [Joint work with Bharath K. Sriperumbudur (PSU). Preprint: https://arxiv.org/abs/1708.08157, ITE toolbox (estimators): https://bitbucket.org/szzoli/ite-in-python/]
Tensor Product Kernels: Characteristic Property and Universalityread_more
HG G 19.1
14 November 2017
15:15-16:00 		Patrik Guggenberger
Penn State University
Event Details

Research Seminar in Statistics

Title A more powerful subvector Anderson-Rubin test in linear instrumental variable regressions
Speaker, Affiliation Patrik Guggenberger, Penn State University
Date, Time 14 November 2017, 15:15-16:00
Location HG G 19.2
Abstract We study subvector inference in the linear instrumental variables model assuming homoskedasticity but allowing for weak instruments. The subvector Anderson and Rubin (1949) test that uses chi square critical values with degrees of freedom reduced by the number of parameters not under test, proposed by Guggenberger et al (2012), controls size but is generally conservative. We propose a conditional subvector Anderson and Rubin test that uses data-dependent critical values that adapt to the strength of identification of the parameters not under test. This test has correct size and strictly higher power than the subvector Anderson and Rubin test by Guggenberger et al (2012). We provide tables with conditional critical values so that the new test is quick and easy to use.
A more powerful subvector Anderson-Rubin test in linear instrumental variable regressionsread_more
HG G 19.2
17 November 2017
15:15-16:00 		Peter Orbanz
Columbia University, New York
Event Details

Research Seminar in Statistics

Title Subsampling large graphs and symmetry in networks
Speaker, Affiliation Peter Orbanz, Columbia University, New York
Date, Time 17 November 2017, 15:15-16:00
Location HG G 19.1
Abstract Consider a very large graph---say, the link graph of a large social network. Now invent a randomized algorithm that extracts a smaller subgraph. If we use the subgraph as sample data and perform statistical analysis on this sample, what can we learn about the underlying network? Clearly, that should depend on the algorithm. We approach the problem by considering what distributional symmetries are satisfied by the algorithm. There is a specific algorithm for which the induced symmetry is precisely exchangeability. In this case, the appropriate statistical models are so-called graphon models, but things change drastically if seemingly minor modifications are made to the subsampler. I will discuss two types of results: (1) How symmetry properties explain what we can learn from a single sample. (2) Convergence properties of symmetric random variables: Laws of large numbers, central limit theorems and Berry-Esseen type bounds, which hold whether or not the symmetry property is derived from subsampling.
Subsampling large graphs and symmetry in networksread_more
HG G 19.1
15 December 2017
11:15-12:00 		Preetam Nandy
University of Pennsylvania
Event Details

Research Seminar in Statistics

Title Estimating and testing individual mediation effects in high-dimensional settings
Speaker, Affiliation Preetam Nandy, University of Pennsylvania
Date, Time 15 December 2017, 11:15-12:00
Location HG G 19.1
Abstract We consider the problem of identifying intermediate variables (or mediators) that regulate the effect of a treatment on an outcome. While there has been significant research on this topic, little work has been done when the set of potential mediators is high-dimensional. A further complication arises when the potential mediators are interrelated. In particular, we assume that the causal structure of the treatment, potential mediators and outcome is a directed acyclic graph. In this setting, we propose novel methods for estimating and testing the influence of a mediator on the outcome for high-dimensional linear structural equation models (linear SEMs). For the estimation of individual mediation effect, we propose a modification of the IDA algorithm that was developed for estimating causal effects from observational data. While most of the approaches for estimating the influence of potential mediators ignore the causal relationship among the mediators, our IDA-based approach estimates the underlying causal graph from data. We derive a high-dimensional consistency result for the IDA-based estimators when the data are generated from a linear SEM with sub-Gaussian errors. Further, we propose a first asymptotically valid testing framework in such a setting, leading to a principled FDR control approach for the identification of the set of true mediators. Finally, we empirically demonstrate the importance of using an estimated causal graph in high-dimensional mediation analysis.
Estimating and testing individual mediation effects in high-dimensional settingsread_more
HG G 19.1
19 December 2017
15:15-16:00 		Sören Künzel
University of California, Berkeley
Event Details

Research Seminar in Statistics

Title Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning
Speaker, Affiliation Sören Künzel, University of California, Berkeley
Date, Time 19 December 2017, 15:15-16:00
Location HG G 19.2
Abstract There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies. We describe a number of meta-algorithms that can take advantage of any machine learning or regression method to estimate the conditional average treatment effect (CATE) function. Meta-algorithms build on base algorithms --- such as OLS, the Nadaraya-Watson estimator, Random Forests (RF), Bayesian Additive Regression Trees (BART) or neural networks --- to estimate the CATE, a function that the base algorithms are not designed to estimate directly. We introduce a new meta-algorithm, the X-learner, that is provably efficient when the number of units in one treatment group is much larger than another, and it can exploit structural properties of the CATE function. For example, if the CATE function is parametrically linear and the response functions in treatment and control are Lipschitz continuous, the X-learner can still achieve the parametric rate under regularity conditions. We then introduce versions of the X-learner that use RF and BART as base learners. In our extensive simulation studies, the X-learner performs favorably, although none of the meta-learners is uniformly the best. We also analyze two real data applications and provide a software package that implements our methods.
Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning read_more
HG G 19.2

Note: if you want you can subscribe to the iCal/ics Calender.

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

JavaScript has been disabled in your browser