
Past Events 201112
The Department of Statistics hosts seminars throughout the year. There is no need to register for any of these and all are very welcome to attend. Please contact Events for further information about any of these seminars.
Statistics Seminars 201112
27 April 2012

14.00 Arthur Gretton (UCL)
Title: Hypothesis Testing and Bayesian Inference: New Applications of Kernel Methods
Abstract: In the early days of kernel machines research, the "kernel trick" was considered a useful way of constructing nonlinear learning algorithms from linear ones, by applying the linear algorithms to feature space mappings of the original data. More recently, it has become clear that a potentially more far reaching use of kernels is as a linear way of dealing with higher order statistics, by mapping probabilities to a suitable reproducing kernel Hilbert space (i.e., the feature space is an RKHS). I will describe how probabilities can be mapped to kernel feature spaces, and how to compute distances between these mappings. A measure of strength of dependence between two random variables follows naturally from this distance. Applications that make use of kernel probability embeddings include:
* Nonparametric twosample testing and independence testing in complex (high dimensional) domains. In the latter case, we test whether text in English is translated from the French, as opposed to being random extracts on the same topic.
* Inference on graphical models, in cases where the variable interactions are modelled nonparametrically (i.e., when parametric models are impractical or unknown).
15.15 XiaoLi Meng (Harvard University)
Title: Statistical Education and Educating Statisticians:
Producing wine connoisseurs and master winemakers
Abstract: The distinction between statistical education and educating statisticians is of particular importance at the pregraduate school level. In recent years we have taken a broader view of statistical education for Harvard's undergraduates, by shifting the focus from preparing a few to pursue Ph.D. level quantitative studies to helping many gain a basic appreciation of statistical argument and insight, as a part of their liberal arts critical thinking training and experience. Intriguingly, the journey, guided by the philosophy that one can become a wine connoisseur without ever knowing how to make wine, apparently has led us to produce many more future winemakers than when we focused only on producing a vintage. At the Ph.D. level, our focus has always been to produce the best winemakers, to take the wine analogy further, but true expert winemakers need to master far more than merely the chemical process of fermenting juice into alcohol, especially with ever increasing competition and demand. We therefore introduced a Professional Development Curriculum (PDC) parallel to the usual course curriculum, starting from "Stat 303: The Art and Practice of Teaching Statistics," a required oneyear course for all entering Ph.D.s, aiming at both producing well trained teaching fellows for undergraduate courses and effective statistical communicators in general. This talk shares a number of stories from our intoxicating journey and experiments, including a Riesling randomized trial conducted for "Stat 105: RealLife Statistics: Your Chance for Happiness (or Misery) " to assess the single most influential factor in students' ability to judge wine quality (once they are over 21).

16 March 2012

JaeKwang Kim (Iowa State University)
Title: An efficient method of estimation for longitudinal surveys with monotone missing data.
Abstract: Panel attrition is frequently encountered in panel sample surveys. When the panel attrition is related to the observed study variable, the classical approach of nonresponse adjustment using a covariatedependent dropout mechanism can be biased. We consider an efficient method of estimation with monotone panel attrition when the response probability depends on the previous values of study variable as well as other covariates. Because of the monotone structure of the missing pattern, the response mechanism is missing at random. The proposed estimator is asymptotically optimal in the sense that it minimizes the asymptotic variance of a class of estimators that can be written as a linear combination of the unbiased estimators of the panel estimates for each wave. The proposed estimator incorporates all available information using the idea of generalized least squares method. Variance estimation is discussed and results from a limited simulation study are also presented. This is a joint work with Dr. Ming Zhou.

9 March 2012

Subhra Sankar Dhar (Cambridge University)
Title: Comparison of Multivariate Distributions Using QuantileQuantile Plots and Related Tests
Abstract: The univariate quantilequantile (QQ) plot is a wellknown graphical tool for examining whether two data sets are generated from the same distribution or not. It is also used to determine how well a specified probability distribution fits a given sample. In this talk, we will develop and study a multivariate version of QQ plot based on spatial quantiles (see Chaudhuri (1996), JASA). The usefulness of the proposed graphical device will be illustrated on different real and simulated data, some of which have fairly large dimensions. We will also develop certain statistical tests that are related to the proposed multivariate QQ plots and study their asymptotic properties. The performance of those tests compared to some other wellknown tests for multivariate distributions will be discussed also.
This is a joint work with Biman Chakraborty and Probal Chaudhuri.

2 March 2012

Idris Eckley (Lancaster University)
Title: Alias detection and spectral correction for locally stationary time series
Abstract: Aliasing occurs when power exists in a signal at frequencies higher than the Nyquist rate (which is determined by the sampling rate). When it occurs, aliasing causes high frequency information to wrap round and mimic power at lower frequencies.
It is all too easy to overlook aliasing when conducting an analysis of a time series. Indeed it is rarely tested for, even though a bispectrumbased test of aliasing for (stationary) time series was proposed by Hinich and Wolinsky in 1988. For locally stationary series the situation is a bit different in that aliasing can be intermittent, depending on whether the spectrum locally contains frequencies higher than the Nyquist rate or not. This talk will introduce a waveletbased method to separate the spectral components of a locally stationary time series into two classes: (i) aliased or white noise components and (ii) lower frequency uncontaminated components. In particular we will consider the case of Shannon wavelets which can separate components even for signals that are not bandlimited. Finally, we show our test working on simulated data and an example provided by an industrial collaborator.
(Joint work with Guy Nason, University of Bristol)

17 February 2012

Kunnummal Muralidharan (University of Baroda)
Title: Theory of inliers: Modelling and Applications
Abstract: An inlier in a set of data is an observation or subset of observations not necessarily all zeroes, which appears to be inconsistent with the remaining data set. They are the resultant of instantaneous or early failures usually encountered in life testing, financial, Management, clinical trials and many other studies. Unlike in outlier theory, here inliers form a group of observations which are defined by the model itself. With the inclusion of inliers, the model will become either a nonstandard distribution or having more than two modes and hence usual method of statistical inference may not be appropriate to proceed with. We discuss some inliers prone models with some weak assumptions to study the estimation of inliers in exponential distribution. Various inlier prone models and estimation procedures are discussed. The detection of inliers and the problems associated with detections are presented. An illustration and a real life example are also discussed.

10 February 2012

Patrick J. Wolfe (UCL)
Title: Modelling Network Data
Abstract: Networks are fast becoming a primary object of interest in statistical data analysis, with important applications spanning the social, biological, and information sciences. A common aim across these ﬁelds is to test for and explain the presence of structure in network data.In this talk we show how characterizing the structural features of a network corresponds to estimating the parameters of various random network models, allowing us to obtain new results for likelihoodbased inference and uncertainty quantiﬁcation in this context. We discuss asymptotics for stochastic blockmodels with growing numbers of classes, the determination of conﬁdence sets for network structure, and a more general point process modelling for network data taking the form of repeated interactions between senders and receivers, where we show consistency and asymptotic normality of partiallikelihoodbased estimators related to the Cox proportional hazards model (arXiv:1011.1703, 1011.4644).

3 February 2012

Philip Dawid (Cambridge University)
Title: Proper Local Scoring Rules
Abstract:A scoring rule S(x, Q) measures the quality of a quoted distribution for an uncertain quantity X in the light of the realised value x of X. It is proper when it encourages honesty, i.e, when, if your uncertainty about X is represented by a distribution P, the choice Q = P minimises your expected loss. Traditionally, a scoring rule has been called local if it depends on Q only through q(x), the density of Q at x. The only proper local scoring rule is then the logscore, log q(x). For the continuous case, we can weaken the definition of locality to allow dependence on a finite number m of derivatives of q at x. A characterisation is given of such orderm local proper scoring rules, and their behaviour under transformations of the outcome space. In particular, any mlocal scoring rule with m 0 can be computed without knowledge of the normalising constant of the density. Parallel results for discrete sample spaces will be given.
Papers available at arXiv:1101.5011v1, arXiv:1104.2224v1
Joint work with Matthew Parry and Steffen Lauritzen

20 January 2012

Steffen Unkel (The Open University)
Title:Exploratory factor analysis of data matrices with more variables than observations
Abstract: The classical fitting problem in exploratory factor analysis (EFA) is to find estimates for the factor loadings matrix and the matrix of unique factor variances which give the best fit to the sample covariance or correlation matrix with respect to some goodnessoffit criterion. Predicted factor scores can be obtained as a function of these estimates and the data. In this talk, the EFA model is considered as a specific data matrix decomposition with fixed unknown matrix parameters. Fitting the EFA model directly to the data yields simultaneous solutions for both loadings and factor scores. Recently, new methods were introduced for the simultaneous least squares estimation of all EFA model unknowns. The algorithms are based on the singular value decomposition of data matrices, facilitate the estimation of both common and unique factor scores, and work equally well when the number of variables exceeds the number of observations. The methods are illustrated by means of Thurstone's 26variable box data and a real highdimensional data set.

20 December 2011

Rainer Dahlhaus (University of Heidelberg)
Title: Nonlinear Phase Estimation for Oscillatory Processes
Abstract: The estimation of nonlinear phases or instantaneous frequencies of nonstationary signals is currently a major issue indifferent areas. For example in physics phase synchronization has been a major topic for several years where phase estimation is a prerequisite. Another area is the HilbertHuang transform where phase estimation is a key step in EMD (empirical mode decomposition). In these areas phase estimation has been carried out by the Hilbert transformation, maximum periodogram methods or several adhoc methods.
In this talk we present a nonlinear, nonGaussian statespace model for phase estimation where the phase, amplitude and baseline are treated as latent Markov processes. For the estimation, we suggest a RaoBlackwellized particle smoother that combines the Kalman smoother and an efficient sequential Monte Carlo smoother. In addition we consider oscillation processes where noncosine type fluctuation patterns with an unobserved phase are modelled. For the estimation of the nonparametric fluctuation pattern a nonparametric EM algorithm is developed.
We also discuss phase synchronization of several oscillators.
The methods are demonstrated for noisy Rössler attractors and electrocardiogram recordings.
(Based on joint work with Jan Neddermeyer)

9 December 2011

Ingrid Van Keilegom (Université catholique de Louvain)
Title: Boundary estimation in the presence of measurement error with unknown variance
Abstract: Boundary estimation appears naturally in economics in the context of productivity analysis. The performance of a firm is measured by the distance between its achieved output level (quantity of goods produced) and an optimal production frontier which is the locus of the maximal achievable output given the level of the inputs (labor, energy, capital, etc.). Frontier estimation becomes difficult if the outputs are measured with noise and most approaches rely on restrictive parametric assumptions. This paper contributes to the direction of nonparametric approaches.
We start with a slightly simplified version of the general problem, which can be written as Y=X Z, where Y is the observable output, X is the unobserved variable of interest with support [0,T] and density f, and Z is the noise. Suppose that f(T)>0, and that Z is independent of X and is lognormally distributed with log Z ~ N(0,s^2) for some unknown variance s^2. The novelty of our approach consists in proposing a method for simultaneous estimation of T and s^2. In addition to this univariate problem, we also consider a model for the extension to the case with covariates, and propose estimators of the frontier function and the variance function under this model.
The asymptotic consistency and the rate of convergence of our estimators are established, and simulations are carried out to verify the performance of the estimators for small samples. We also apply our method on a dataset concerning the production output of American electricity utility companies.

18 November 2011

Dawei Huang (Chinese Academy of Science)
Title: Financial Time Series: Trend Classifications Based on Feature Transformation and Selection
Abstract: In this talk, we discuss how to classify a financial time series into up and down trends as well as to identify the tops and bottoms from statistical machine learning point of view. Firstly, we define the up and down trends with a parameter controlling the length of the trend. Secondly, we derive the optimal regressand in the regression for two class linear discrimination problem. Thirdly, we introduce socalled correlation booster to increase the linear relationship between the regressand and features. The possibility of classification is confirmed by Principal Component Analysis. Finally, LASSO algorithm with cross validation is used for selecting features and building models. These models can classify: 1. up and down trends; 2. tops from up trends; 3. bottoms from down trends. This method is applied to different financial time series, including gold and silver prices, stock indices and stock prices. Encouraging results are showed for real data.

11 November 2011

Maria Alejandra Molina (LSE)
What drives the survival and growth of new firms in Brazil? A learning and capability perspective

28 October 2011

Philipp Rode (LSE Cities)
Title: Statistical projects of LSE Cities
Abstract: LSE Cities's research activities are characterised by handling wideranging collection and processing of global, regional and local data that have helped foster and communicate a better understanding of cities from various perspectives, such as economics, sociology, environment or governance. Data on urbanisation, cities and space is by its very nature more rudimentary, fragmented and heterogeneous than in many other disciplines; it often requires novel and innovative approaches to analysis, while ensuring validity and significance of the research. LSE Cities is inviting M.Res. students from the Statistics Department to join us in thinking about paths towards robust analysis of urban data on our various research fronts. Options for dissertations, types of data and relevant research projects will be introduced at the seminar on October 28, 2011.

14 October 2011

Alexey Sorokin (MAN Investments)
Title: Noninvertibility in herteroscedastic time series models
Abstract: In order to calculate the unobserved volatility in conditional heteroscedastic time series models such as GARCH, the natutral recursive approximation is very often used. A model is called invertible if this recursive approximation converges to the unobserved volatility in probability. It turns out that a stationary GARCH (p, q) model is always invertible, but some other wellknown heteroscedastic models, in particular some asymmetric ones, are not. For such models, a pair (true volatility, approximation) has a nondegenearte stationary distribution. As a result, the volatility forecast given by the recursive approximation is inconsistent even if the true parameter vector is know. In the talk, I will present an "almost" criterion of invertibility for 2 particular models, present numerical examples and discuss challenges in obtaining a general condition for noninvertibility.

Risk and Stochastics Seminars 201112
The Risk and Stochastics Seminar aims to promote communication and discussion of research in the mathematics of insurance and finance and their interface, to encourage interaction between practice and theory in these areas, and to support academically students in related programmes at postgraduate level. All are welcome to attend. Sessions run regularly during LSE terms, and will place on Thursdays at 6.05 pm in Room V312, 3rd floor of Tower 2.
The current uptodate schedule is given below. Please contact Events for further information about any of these seminars. All are very welcome to attend.
10 May 2012

Brenda Lopez Cabrera (HumboldtUniversität zu Berlin)
17.00, Rm OLD 3.28
Details can be found here.

3 May 2012

Larbi Alili (University of Warwick)  17.0018.00 in CON 1.04
Title: On some involutive inversions of one dimensional diffusions
Abstract: Given a regular one dimensional diffusion on an interval, where regular boundaries are killing, we show that, in some sense, this has a unique inverse with respect to a fixed point in the state space. This inverse process when appropriately timechanged is the dual process which law is obtained by Doob $h$transforming the law of the original diffusion. Conversely, given a positive harmonic function $h$ on that interval (or a scale function) , satisfying some reasonable conditions, we show that the Doob $h$transformed process can be obtained explicitly in terms of inversion of the original diffusion with a time change. The construction involves some involutions and interesting random clocks. This is joint work with P. Graczyk and T. Zak.

19 April 2012

Luciano Campi (Paris 13)  17.0018.00 in V312
Title: On existence of shadow prices
Abstract: For utility maximization problems under proportional transaction costs, it has been observed that the original market with transaction costs can sometimes be replaced by a frictionless shadow market that yields the same optimal strategy and utility. However, the question of whether or not this indeed holds in generality has remained elusive so far. In this paper we present a counterexample that shows that shadow prices may fail to exist. On the other hand, we prove that short selling constraints are a sufficient condition to warrant their existence, even in very general multicurrency market models with possibly discontinuous bidaskspreads.
This talk is based on a joint work with G. Benedetti, J. Kallsen and J. MuhleKarbe.


29 March 2012

Samuel Cohen (Oxford University)
Title: Uncertainty and nonlinear expectations
Abstract: Decision making in the presence of uncertainty is a mathematically delicate topic. In this talk, we consider coherent sublinear expectations on a measurable space, without assuming the existence of a dominating probability measure. By considering discretetime `martingale' processes, we show that the classical results of martingale convergence and the up/downcrossing inqualities hold in a `quasisure' sense. We also give conditions, for a general filtration, under which an `aggregation' property holds, generalising an approach of Soner, Touzi and Zhang (2011). From this, we extend various results on the representation of conditional sublinear expectations to general filtrations under uncertainty.

15 March 2012

Josef Teichmann (ETH Zurich)
Details can be found here.

8 March 2012

Johan Tysk (Uppsala University)
Title:Can timehomogeneous diffusions produce any distribution?
Abstract: Given a centred distribution, can one find a timehomogeneous martingale diffusion starting at zero which has the given law at time 1? We answer the question affirmatively if generalized diffusions are allowed.
This talk is based on a joint work with Erik Ekström, David Hobson and Svante Janson.

1 March 2012

Suleyman Basak (London Business School)
Title: Strategic Asset Allocation in Money Management
Abstract: Money managers behave strategically when competing for fund flows within relatively small groups. We study strategic interaction between two riskaverse managers in continuous time, characterizing analytically their unique equilibrium dynamic investments. Driven by chasing and contrarian mechanisms when one is well ahead, they gamble in the opposite direction when their performances are close. We also discuss multiple and mixedstrategy equilibria. Equilibrium policy of each crucially depends on the opponent's risk attitude. Hence, client investors, concerned about how a strategic manager may trade on their behalf, should also learn competitors' characteristics  as against nonstrategic settings, where knowing a manager's own characteristics suffices to determine behavior.

23 February 2012

Vicky Henderson (Oxford MAN Institute)
Details can be found here.

16 February 2012

Mike Tehranchi (University of Cambridge)
Details can be found here. 

13 Feb 2012
Start time 4pm
Venue:Room COL 6.15, sixth floor, Columbia House.

Albert Shiryaev (Steklov Mathematical Institute)
Title: The concept of randomness: evolution of noyions
Abstract: We analyze different notions of randomness for infinite sequences and their relations starting with the notion of "Kollective" on von Mises. His definition of "randomness" provoked serious criticisms, however it also stimulated the investigation of the problem "which infinite sequences meet our idea of randomness?" We give an overview of different algorithmic approaches, including:

Frequencystability randomness after MisesWaldChurch

Frequencystability randomness after Kolmogorov

Typical randomness after MartinLoef

Chaotic randomness or Complex structure after KolmorgorovLevinSchnorr
We shall also discuss the relation between these algorithmic approaches and the probabilistic ones.
Albert Shiryaev is a distinguished Visiting Professor in the Deaprtment of Statistics.

9 February 2012

Markus Riedle (King's College)
Title: Stochastic models in infinite dimensions
Abstract: Starting from the HeathJarrowMorton framework and the heat equation we motivate examples of noise for models in an infinite dimensional setting such as Wiener processes, Levy processes and fractional Brownian motions.In more detail we consider the recently introduced cylindrical Levy process as a generalisation of cylindrical Wiener processes, which are the most often applied model of noise in infinite dimensions.The perturbation of dynamics by noise results in stochastic differential equations and requires a theory of stochastic integration in the underlying space which is in this setting infinite dimensional. We explain the difficulties of stochastic integration in infinite dimensional spaces and indicate a possible approach.
(part of this talk is based on joint works with D. Applebaum, O. van Gaans, E. Issoglio)

2 February 2012

Curdin Ott (University of Bath)
Title: Capped Optimal Stopping problems for the Maximum Process
Abstract: This talk concerns optimal stopping problems driven by a spectrally negative Levy process X. More precisely, we will consider capped versions of the Russian and American lookback optimal stopping problem and provide semiexplicit solutions in terms of scale functions. The optimal stopping boundary is characterised by an ordinary firstorder differential equation involving scale functions and, in particular, changes according to the path variation of X. Furthermore, we will see that the solutions exhibit a pattern suggested by Peskir's maximality principle.

19 January 2012

Damiano Brigo
(King's College)
Alex Mijatovic (University of Warwick)
Title: On the drawdown of completely asymmetric Levy processes
This is joint work with M. Pistorius.

24 November 2011

Kees van Schaik (University of Manchester)
Title: Meromorphic Levy processes: a WienerHopf Monte Carlo simulation method and American option pricing
Abstract: Recently some new, large families of Levy processes (processes with stationary independent increments and cadlag paths) have been introduced that have the rare property that both the parameters determining their law and their WienerHopf factors are quite explicitly known. The latter firsly allows for using such processes in a newly established socalled WienerHopf Monte Carlo (WHMC) simulation method. We will discuss this simulation method and point out some advantages over 'plain' Monte Carlo, we will also discuss some work in progress concerning extending the WHMC simulation method to path functionals such as first hitting times and over/undershoots. Secondly we will discuss some work in progress concerning a numerical scheme for finite expiry optimal stopping problems (i.e. finite horizon American options in a financial context) driven by such Levy processes, extending Peter Carr's socalled Canadisation algorithm.

17 November 2011

Mete Soner (ETH Zürich)
Title: Choquet Capacity, Nonlinear PDE's and hedging
Abstract: As it is well known backward stochastic differential equations (BSDE's) are naturally connected to semilinear parabolic partial differential equations. Moreover there are many applications of BSDE's in mathematical finance. The semilinear part of the equation, determines the quadratic variation and allows us to work with one probability measure. To obtain a similar theory for fully nonlinear parabolic equations, one needs to extend the theory and require the equations to hold under a large class nondominated measures. In this talk, I will survey this new theory of 2BSDE's, G expectations of Peng, uncertain volatility model as developed by Denis & Martini. I will also provide a hedging or equivalently a martingale representation theorem in this context.

10 November 2011

Kevin Warner (Tower Research Capital)
Title: Neural Networks for Systematic Trading
Abstract: Starting with a background on artificial neural networks, an intuitive explanation of the basic perceptron and a review of the process for estimation, training and evaluation, focusing on where nuance is particularly needed, I'll then cover the thinking behind various architectures and topologies and will compare the computing overhead and forecasting accuracy of assorted models by working though examples on simulated and actual time series data, concluding with some recommendations for real life implementation.

3 November 2011

Jordan Stoyanov (University of Newcastle)
Details can be foundhere.

27 October 2011

Huyên Pham (University Paris Diderot)
Details can be found here.

20 October 2011

Ragnar Norberg (LSE and University of Lyon)
Title: On optimal quadratic hedging of payment streams and optimal design of derivatives.
Abstract: An investment strategy consists of a portfolio process and a cost process, the latter representing deposits into and withdrawals from the portfolio account. The investment strategy is a hedge of a contractual payment stream (e.g. defined by an insurance contract) if the payments are currently deposited on or withdrawn from the portfolio account as they are due. The purpose of the hedge is given by an optimization criterion for the investment strategy. In the martingale case certain quadractic criteria lead to the same optimal portfolio but different optimal cost functions. The theory works when constraints are imposed on the portfolio value, a case in point being solvency requirements in insurance (e.g. Solvency II). So far the theory has dealt with optimal hedging with a given set of available traded assets. I shall finish by discussing optimal design of the very assets (e.g. insrance derivatives), the purpose being to minimise the average hedging error across a population of hedgers pursuing optimal individual hedging strategies.

13 October 2011

Almut Veraart (Imperial College London)
Details can be found here.

6 Oct 2011

Roman Muraviev (ETH)
Title: Natural selection with habits and learning in heterogeneous economics
Abstract: We study natural selection in complete financuial markets, populated by heterogeneous agents. We allow for a rich structure of heterogeneity: individuals may differ in their beliefs concerning the economy, information and learning mechanism, risk aversion, impatience (time preference rate) and degree of habits. We develop new techniques for studying long run behaviour of such economics, based on the Strassen's functional law of iterated logatrithm. In particular, we explicitly detrmine an agent's survival index and show how the latter depends on the aent's characteristics. We use these results to study the long run behaviour of the equilibrium interest rate and the market price of risk.


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