The Department of Statistics hosts statistics seminars throughout the year. Seminars take place on Friday afternoons at 2pm in the Leverhulme Library (unless stated otherwise) with a lunch preceding at 1pm. All are very welcome to attend! Please contact Penelope Smith at statistics.events@lse.ac.uk for further information about any of these seminars below.

The Leverhulme Library (Room COL 6.15) is located on the sixth floor of Columbia Hourse. Please view the LSE maps and directions here.

7th October 2016 - 2-3pm in the Leverhulme Library

Satish Iyengar

University of Pittsburgh

Title - Diffusion Models in Neuroscience and Finance

Abstract - Stochastic models of neural activity are a well
developed application in biology. Diffusion models for integrate-and-fire (I-F) neurons hold a prominent place because of the many synaptic inputs to a neuron,
and because these models arise out of noisy versions of differential equations for the neural membrane's electrical properties. I will describe a leaky I-F model which leads to a reflecting Ornstein-Uhlenbeck process. I will then address the problem of maximum likelihood estimation of the parameters of this model when only the firing times corresponding to the first passage times are available. Then describe a two-dimensional diffusion model arising from a simple network and its use in finance. The coefficient of tail dependence is a quantity that measures how extreme events in one component of a bivariate distribution depend on extreme events in the other component. It is well-known that the Gaussian copula has zero tail dependence, a shortcoming for its application in credit risk modeling and quantitative risk management in general. We show that this property is shared by the joint distributions of hitting times of bivariate (uniformly elliptic) diffusion processes.

12th October 2016 - 4-5.30pm in Thai Theatre, NAB

Alan Agresti

University of Florida

Title - Some Issues in Generalized Linear Modeling

Abstract - This talk discusses several topics pertaining to generalized linear modeling. With focus on categorical data, the topics include (1) bias in using ordinary linear models with ordinal categorical response data, (2) interpreting effects with nonlinear link functions, (3) cautions in using Wald inference (tests and confidence intervals) when effects are large or near the boundary of the parameter space, and (4) the behavior and choice of residuals for GLMs. I will present few new research results, but these topics got my attention while I was writing the book "Foundations of Linear and Generalized Linear Models," recently published by Wiley.

14th October 2016 - 2-3pm in the Leverhulme Library

Simon Wood

Univeristy of Bristol

Title - Large additive models for large datasets: modelling 4 decades of daily pollution data over the UK

Abstract - The UK `black smoke' monitoring network has produced daily particulate air pollution data from a network of up to 2000 monitoring stations over several decades, resulting in >10^7 measurements in total. Spatio temporal modelling of the data is desirable in order to produce daily exposure estimates for cohort studies, for example. Generalized additive models/Latent Gaussian process models offer one way to do this if we can deal with the data volume and model size. This talk will discuss the development of methods for estimating generalized additive models having of order 10^4 coefficients, from of order 10^8 observations. The strategy combines 4 elements: (i) the use of rank reduced smoothers, (ii) fine scale discretization of covariates, (iii) an efficient approach to marginal likelihood optimization, that avoids computation of numerically awkward log determinant terms and (iv) marginal likelihood optimization algorithms that make good use of numerical linear algebra methods with reasonable scalability on modern multi-core processors. 600 fold speed ups can be achieved relative to the previous state of the art methods. This enables us to estimate spatio-temporal models for UK black smoke data over the last 4 decades at a daily resolution, where previously an annual resolution was challenging.

21st October 2016 - 2-3pm in the Leverhulme Library

Jeremie Bigot

University of Bordeaux

Title - Generalized SURE for optimal shrinkage of singular values in low-rank matrix denoising

Abstract - We consider the problem of estimating a low-rank signal matrix from noisy measurements under the assumption that the distribution of the data matrix belongs to an exponential family. In this setting, we derive generalized Stein's unbiased risk estimation (SURE) formulas that hold for any spectral estimators which shrink or threshold the singular values of the data matrix. This leads to new data-driven shrinkage rules, whose optimality is discussed using tools from random matrix theory and through numerical experiments. Under the spiked population model and in the asymptotic setting where the dimensions of the data matrix are let going to infinity, some theoretical properties of our approach are compared to recent results on asymptotically optimal shrinking rules for Gaussian noise. It also leads to new procedures for singular values shrinkage in finite-dimensional matrix denoising for Gaussian, Poisson or Gamma-distributed measurements.

28th October 2016 - 12-1pm in the Leverhulme Library

Flavio Ziegelmann

Federal University of Rio Grande do Sul

Title - Dynamic Copulas and Market Risk Forecasting

Abstract - In this talk we propose forecasting portfolio market risk measures, such as Value at Risk (VaR) and Expected Shortfall (ES), via dynamic copula modelling. For that we describe several dynamic copula models, from naive ones to complex factor copulas. The last are able to tackle the curse of dimensionality whereas simultaneously introducing a high level of complexity into the model. We start with bi-dimensional copulas, then go to vine copulas when increasing moderately the dimension and finally jump to factor copulas for high dimensional portfolios. In the factor copula case we allow for different levels of flexibility in the dynamics of the dependence parameters, which are driven by a GAS (Generalized Autorregressive Scores) model. Along the talk, we show some numerical analyses for both simulated and real data sets.

28th October 2016 - 2-3pm in the Leverhulme Library

Min Xu

University of Pennsylvania

Title - Faithful Variable Screening for High-Dimensional Convex Regression

Abstract - We study the problem of variable selection in convex nonparametric regression. Under the assumption that the true regression function is convex and sparse, we develop a screening procedure to select a subset of variables that contains the relevant variables. Our approach is a two-stage quadratic programming method that estimates a sum of one-dimensional convex functions, followed by one-dimensional concave regression fits on the residuals. In contrast to previous methods for sparse additive models, the optimization is finite dimensional and requires no tuning parameters for smoothness. Under appropriate assumptions, we prove that the procedure is faithful in the population setting, yielding no false negatives. We give a finite sample statistical analysis, and introduce algorithms for efficiently carrying out the required quadratic programs. The approach leads to computational and statistical advantages over fitting a full model, and provides an effective, practical approach to variable screening in convex regression. Joint work with Minhua Chen and John Lafferty.

4th November 2016 - 2-3pm in the Leverhulme Library

Nikos Tzavidis

University of Southampton

Title - Domain Prediction of Complex Indicators: Model-based Methods, Transformations and Robust Alternatives

Abstract - Small Area (Domain) prediction of complex indicators for example, deprivation and inequality indicators typically relies on micro-simulation/model-based methods that use regression models with domain-specific random effects. When the Gaussian assumptions for the model error terms are met, Empirical Best Prediction (EBP) for domains is possible and should be preferred. In this talk we will present current research on alternative methodologies when the model assumptions are misspecified. To start with, we will discuss the use of transformations- focusing mainly on power and scaled transformationsfor trying to ensure the validity of the EBP ssumptions. Transformations can help improve estimation but even small departures from the model assumptions can adversely impact upon estimation of parameters closer to the tails of the distribution and on estimation of the Mean Squared Error. We will then outline alternative, possibly more robust model-based methodologies. These methods are based on the use of a random effects model for the quantiles of the empirical distribution unction that exploits the link between maximum likelihood estimation and the use of the Asymmetric Laplace Distribution as a working assumption. The talk will also briefly outline work on the use of this latter method with discrete outcomes in particular, count outcomes.

11th November 2016 - 2-3pm in the Leverhulme Library

Yulia Gel

University of Texas at Dallas

Title - Bootstrap of Degree Distribution in Large Sparse Networks

Abstract - We propose a new method of nonparametric bootstrap to quantify estimation uncertainties in functions of network degree distribution in large ultra sparse networks. Both network degree distribution and network order are assumed to be unknown. The key idea is based on adaptation of the ``blocking'' argument, developed for bootstrapping of time series and re-tiling of spatial data, to random networks. We first sample a set of multiple ego networks of varying orders that form a patch, or a network block analogue, and then resample the data within patches. To select an optimal patch size, we develop a new computationally efficient and data-driven cross-validation algorithm. In our simulation study, we show that the new fast patchwork bootstrap (FPB) outperforms competing approaches by providing sharper and better calibrated confidence intervals for functions of a network degree distribution, including the cases of networks in an ultra sparse regime. In addition, the FPB is substantially less computationally expensive, requires less information on a graph, and is free from nuisance parameters. We illustrate the FPB in application to collaboration networks in statistics and computer science and to Wikipedia networks.

18th November 2016 - 2-3pm in the Leverhulme Library

Chenlei Leng

University of Warwick

Title - DECOrrelated feature space partitioning for distributed sparse regression

Abstract - Fitting statistical models is computationally challenging when the sample size or the dimension of the dataset is huge. An attractive approach for down-scaling the problem size is to first partition the dataset into subsets and then fit using distributed algorithms. The dataset can be partitioned either horizontally (in the sample space) or vertically (in the feature space). While the majority of the literature focuses on sample space partitioning, feature space partitioning is more effective when p≫n. Existing methods for partitioning features, however, are either vulnerable to high correlations or inefficient in reducing the model dimension. In this paper, we solve these problems through a new embarrassingly parallel framework named DECO for distributed variable selection and parameter estimation. In DECO, variables are first partitioned and allocated to m distributed workers. The decorrelated subset data within each worker are then fitted via any algorithm designed for high-dimensional problems. We show that by incorporating the decorrelation step, DECO can achieve consistent variable selection and parameter estimation on each subset with (almost) no assumptions. In addition, the convergence rate is nearly minimax optimal for both sparse and weakly sparse models and does NOT depend on the partition number m. Extensive numerical experiments are provided to illustrate the performance of the new framework.

2nd December 2016 - 2-3pm in the Leverhulme Library

Joshua Loftus

University of Cambridge and Alan Turing Institute

Title - TBC

Abstract - TBC

6th December 2016 - 3-4.30pm in the Leverhulme Library

Hira Koul

Michigan State University

Title - Residual empirical processes

Abstract - Residual empirical processes are known to play a central role in the development of statistical inference in numerous additive models. This talk will discuss some history and some recent advances in the asymptotic uniform linearity of parametric and nonparametric residual empirical processes. We shall also discuss their usefulness in developing asymptotically distribution free goodness-of-fit tests for fitting an error distribution functions in nonparametric ARCH(1) models.

20th January 2017 - 2-3pm in the Leverhulme Library