Colorado State University
Hilton Fort Collins
Fort Collins, Colorado

June 11-13, 2006

Short Course
Maps & Websites
2003 - 2005
Cai, T. Tony Department of Statistics, The Wharton School, University of Pennsylvania

A Root-Unroot Transform and Block Thresholding Approach to
Density Estimation and Poisson Regression

Abstract: Block thresholding methods have demonstrated considerable successes in wavelet function estimation, especially in Gaussian nonparametric regression. Block thresholding increases estimation precision by utilizing information about neighboring wavelet coefficients. 
In this talk we will discuss a root-unroot method for density estimation and Poisson regression. This method turns these problems into a nonparametric regression problem and then block thresholding technique is used for estimation. The procedure is easily implementable. We show that the resulting estimators adaptively achieve the optimal rate of convergence over a range of Besov Spaces.

Biography: Tony Cai is  Associate Professor of  Statistics
at the Wharton School of the University of Pennsylvania. His research interests include nonparametric function estimation, wavelet applications, and functional data analysis. He earned his Ph.D. from Cornell University in 1996 and is currently Associate Editor for The Annals of Statistics, JASA, Statistica Sinca and Statistics Surveys.

Clyde, Merlise Institute of Statistics and Decision Sciences, Duke University

Bayesian Function Estimation using Continuous Overcomplete Dictionaries

Abstract: We consider the nonparametric regression problem of estimating an unknown function based on noisy data. One approach to this estimation problem is to represent the function in a series expansion using a linear combination of basis functions. Overcomplete dictionaries provide a larger, but redundant collection of generating elements than a basis, however, coefficients in the expansion are no longer unique. Despite the non-uniqueness, this has the potential to lead to sparser representations by using fewer non-zero coefficients. Compound Poisson random fields and their generalization to Levy random fields are ideally suited for construction of priors on functions using these overcomplete representations for the general nonparametric regression problem, and provide a natural limiting generalization of priors for the finite dimensional version of the regression problem. The price for the increased flexibility of the overcomplete representation is the computational challenge of exploring an infinite dimensional space. While expressions for posterior modes or posterior distributions of quantities of interest are not available in closed form, the prior construction using Levy random fields permits tractable posterior simulation via a reversible jump Markov chain Monte Carlo algorithm. Efficient computation is possible because updates based on adding/deleting or updating single dictionary elements bypass the need to invert large matrices. Furthermore, because dictionary elements are only computed as needed, memory requirements scale linearly with the sample size. In comparison with other methods, the Levy random field priors provide excellent performance in terms of both mean squared error. We discuss applications to protein identification and quantification using MALDI-TOF mass spectroscopy. 

Biography: Merlise Clyde is an Associate Professor of Statistics in the Institute of Statistics and Decision Sciences at Duke University. She received her PhD degree in 1993 from the School of Statistics at the University of Minnesota, Her primary research interest is in model selection and model uncertainty.

Donoho, David Statistics Department, Stanford

How Multiscale Thinking Creates New Challenges for Statisticians

Dukic, Vanja Biostatistics, University of Chicago

AIDS Reporting Delay in the US Cities: Analysis of the Centers for Disease Control (CDC) Data

Abstract:  Time delay between a new AIDS diagnosis and its report to the Centers for Disease Control (CDC), historically ranging between couple of weeks and couple of years, presents a significant problem when trying to predict future AIDS incidence and health care burden. The reporting delay needs to be correctly estimated and adjusted for in order to avoid potentially serious downward bias. We examine case reports from 39 large US cities, received by the CDC as of the end of December 2001 and published in the APIDS database. We employ Bayesian multi-resolution methodology to estimate city-specific hazards of reporting delay, adjusting for patient covariates and within-city correlation. We describe the ranking of the 39 US cities according to their reporting delay distributions based on the optimal survival curve ranking (OSCR) procedure. We discuss uncertainty in the reported delay estimates and in the resulting ranking, and present a graphical approach to visualize this uncertainty.

Biography:  Vanja Dukic got her PhD in Applied Mathematics at Brown University in 2001. Since then she has been at the University of Chicago as an assistant professor of biostatistics. Her research interests include Bayesian hierarchical models for epidemics, meta-analysis, and survival data, as well as model selection and statistical computing.

Fryzlewicz, Piotr. Department of Mathematics, University of Bristol

Unbalanced Haar(-Fisz) methodology for function estimation and variance stabilisation

Abstract:  The Discrete Unbalanced Haar (DUH) transform is a decomposition of 1D signals with respect to an orthonormal Haar-like basis where jumps in the basis vectors do not necessarily occur in the middle of their support. We introduce a procedure for estimation in Gaussian noise which consists of three steps: a DUH transform, thresholding of the decomposition coefficients, and the inverse DUH transform. We show that our estimator is mean-square consistent with near-optimal rates for a wide range of functions, uniformly over DUH bases which are not ``too unbalanced". An important ingredient of our approach is basis selection. We choose each basis vector so that it best matches the data at a specific scale and location, where the latter parameters are determined by the ``parent" basis vector. Our estimator performs well and is computable in O(n log n) operations. Modifications to the above procedure are needed for other types of noise. We consider Poisson intensity estimation, as well as a multiplicative regression set-up occurring in e.g. spectrum or volatility estimation. To account for the heterogeneity of the data, both the "basis selection" and "thresholding" steps of our algorithm use the so-called Fisz variance stabilising transform, whose main idea is to divide a given Unbalanced Haar coefficient by an appropriate function of the corresponding ``smooth" coefficient. With a small modification, the resulting Unbalanced Haar-Fisz estimation algorithm can also be used to stabilise the variance of heterogeneous data and bring their distribution closer to normality.

Biography:  Piotr Fryzlewicz obtained an M.Sc. in Mathematics from Wroclaw University of Technology, Poland (2000) and a Ph.D. in Statistics from the University of Bristol, UK (2003). In 2003-2005, he was a Chapman Research Fellow at Imperial College London. He is now a Lecturer in Statistics in the Department of Mathematics at the University of Bristol.
His research is in the areas of multiscale methods in statistics, nonparametric regression and time series, with applications to finance, astronomy and microarray data. His recent work on locally stationary financial time series models has been supported by a grant from the Nuffield Foundation.

Hannig, JanDepartment of Statistics Colorado State University

Extreme Value Theory for SiZer

Abstract:  SiZer is a powerful method for exploratory data analysis. In this paper approximation to the distributions underlying the statistical inference are investigated, and large improvements are made in the approximation using extreme value theory. This results in improved size, and also in an improved global inference version of SiZer. The main points are illustrated with real data and simulated examples.

Biography:  Jan Hannig received an MS. in mathematics from Charles University, Prague in 1996 and Ph.D. in statistics from Michigan State University in 2000. He is an Assistant Professor in the Department of Statistics at Colorado State University. His research interests include stochastic processes and theoretical statistics.

Hirakawa, Keigo Department of Statistics, Harvard University

Wavelet-Based Image Processing with Missing Data

Abstract: Suppose an image denoising problem is extended to simultaneously deal with problems with missing or incomplete pixel values, either because of mechanical designs (e.g. demosaicing) or because of distortion (e.g. picture impainting). In the context of wavelet-based image processing, missing or incomplete pixel poses a difficult problem because wavelet transform takes a linear combination of image signal, and thus many, or even all of the noisy wavelet coefficients are unobserved. In this work, a unified framework for coupling the EM algorithm with the Bayesian hierarchical modeling of neighboring wavelet coefficients of image signals is presented. Within this framework, problems with missing pixels or pixel components, and hence unobservable wavelet coefficients, are handled simultaneously with denoising. The hyperparameters of the model are estimated via the marginal likelihood by the EM algorithm, and a part of the output of its E-step automatically provides optimal estimates, given the specified Bayesian model, of the noise-free image. This unified empirical-Bayes based framework, therefore, offers a statistically principled and extremely flexible approach to a wide range of pixel estimation problems including image denoising, image interpolation, demosaicing, or any combinations of them. 

Biography: Keigo Hirakawa received B.S.E. in electrical engineering from Princeton University (Princeton, NJ) in 2000, M.S. and Ph.D in electrical and computer engineering from Cornell University (Ithaca, NY) in 2003 and 2005, respectively, where he was a recipient of the Lockheed Martin Fellowship. Since 2005, he has been a postdoctoral research associate in the Department of Statistics at Harvard University while simultaneously pursuing M.M. in jazz performance (piano) at New England Conservatory (Boston, MA). He has been an imaging consultant for Hewlett-Packard, Agilent Technologies, NEC Labs Japan, and Texas Instruments. Hirakawa's research focuses on color science and on model-based image processing, including image denoising, interpolation, model parameter estimation, and missing data problems.


Huo, Xiaming. School of Industrial and Systems Engineering, Georgia Institute of Technology

Multiscale Methodology and Detectability

Abstract: Multiscale methodology has been proven to be effective in determining the state-of-the-art boundaries in some detectability problems. We review some new results in this direction. More specifically, the existing results give the asymptotic rate of the boundaries. More precise distributional results can be derived, and they reveal more accurate properties of the detectability boundaries. Some of these results will be presented in my talk. Potential applications are described.

Biography:   Xiaoming Huo received the B.S. degree in mathematics from the University of Science and Technology, China, in 1993, and the M.S. degree in electrical engineering and the Ph.D. degree in statistics from Stanford University, Stanford, CA, in 1997 and 1999, respectively. He is an Associate Professor with the School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta. His research interests include statistics and multiscale methodology. Dr. Huo received first prize in the 30th International Mathematical Olympiad (IMO), which was held in Braunschweig, Germany.

Izem, Rima Statistics Department, Harvard

Analysis of Variation in Manifolds

Abstract:  Several statistical methods such as principal component analysis and analysis of variance are often effective in analyzing variation in high dimensional data when the space of variation is linear. However, describing variability is much more difficult when the data varies along nonlinear modes. Simple examples of nonlinear variation in functional data are horizontal shift of curves of common shape, frequency change of acoustic signals of common shape, or lighting change in images of the same object. 
This presentation shows novel data depth functions that would extend data depth concepts to describe variation of multivariate data when the space of variation is a manifold or the result of nonlinear variation in the data. We propose new ways of defining depth in manifolds and they both respect the geometry of the support of the distribution. We illustrate these new depth measures for manifolds of constant curvature or known atlas. 

 Biography:  Rima Izem received her PhD in statistics at the University of North Carolina at Chapel Hill in May 2004. She has spend the last two year as an assistant professor at the statistics department at Harvard. Her research interests are in developing statistical methodologies in Functional Data Analysis, Spatial Statistics, and Nonparametric statistics. She is particularly interested in applications to Biology and Economics.

Kolaczyk, Eric D. Department of Mathematics and Statistics, Boston University

Multiscale, Multigranular Statistical Image Segmentation

In the image segmentation problem, one seeks to determine and label homogeneous subregions in an image scene, based on pixel-wise measurements. Motivated by current challenges in the field of remote sensing land cover characterization, we introduce a framework that allows for adaptive choice of both the spatial resolution of subregions and the categorical granularity of labels. Our framework is based upon a class of models we call "mixlets," a blend of recursive dyadic partitions and finite mixture models. The first component allows for sparse representation of spatial structure at multiple resolutions, while the second enables us to capture the varying degrees of mixing of pure categories that accompany the use of different resolutions. A segmentation is produced in our framework by selecting an optimal mixlet model, through complexity-penalized maximum likelihood, and summarizing the information in that model with respect to a categorical hierarchy. Both theoretical and empirical evaluations of the proposed framework are presented. If time allows, we will also comment on current work towards Bayesian and spatio-temporal extensions.

Biography:  Professor Kolaczyk's research focuses on the statistical modeling and analysis of various types of temporal, spatial, and network data, with a particular emphasis on the development of methods exploiting inherent sparseness. His work has resulted in new methods for signal and image denoising, tomographic image reconstruction, disease mapping and clustering, high-level image analysis in land cover classification, and sampling and monitoring of computer network structure and traffic. Professor Kolaczyk's publications have appeared in the literatures on statistical theory and methods, engineering, astronomy, geography, and computer science. His work has been supported by various grants from the Office of Naval Research and the National Science Foundation.

Lee,Thomas C. M.  Department of Statistics Colorado State University

Fiducial Curvewise Confidence Intervals for Wavelet Regression

Abstract: We propose a method for constructing curvewise approximate confidence intervals for wavelet regression. Our method is based on a recent generalization of fiducial inference studied by Hannig (2006) and is an alternative to Bayesian based methods. Preliminary simulation results suggest good frequentist properties of the proposed method. Joint work with Jan Hannig.

Biography: Thomas Lee received his B.App.Sc. (Math) degree in 1992, and the B.Sc. (Hons) (Math) degree with University Medal in 1993, all from the University of Technology, Sydney, Australia. In 1997 he completed a Ph.D. degree jointly at Macquarie University and CSIRO Mathematical and Information Sciences, Sydney, Australia. Currently he is an Associate Professor at the Department of Statistics, Colorado State University, USA. His research interests include computational statistics, wavelet analysis, and digital signal and image processing.

Meng, Xiao-Li Department of Statistics, Harvard

A Crash Course in Wavelet Methods - Short Course, June 11


Xiao-Li Meng is Professor and Chairman of the Department of Statistics at Harvard University. He is also the co-editor of Statistica Sinica. He was the recipient of the 2001 COPSS Award, and the recipient of the 2003 Distinguished Achievement Award from ICSA. He was ranked (by Science Watch) among the world top 25 most cited authors for articles published and cited during 1991-2001 in mathematical sciences. His degrees include BS (Fudan Mathematics Department, 1982), Master of Science Diploma (Fudan Mathematics Institute, 1986), Master of Art (Harvard Statistics, 1987), and Ph.D. (Harvard Statistics, 1990). He taught at The University of Chicago from 1991-2001 before joining Harvard University. He has served on editorial boards for leading statistical journals such as The Annals of Statistics, Biometrika, Journal of The American Statistical Association, and Bernoulli. He has served on numerous national and international professional committees, including chairing the 2004 Joint Statistical Meetings. He is also an elected fellow of American Statistical Association and of Institute of Mathematical Statistics.

His current research interests include wavelet modelling for signal and image data, statistical issues in astronomy and astrophysics, modelling and imputation for mental health survey data, Bayesian ranking and mapping, statistical principles and foundational issues, and Markov chain Monte Carlo, especially perfect sampling.

Meyer, Francois Department of Electrical Engineering, University of Colorado 

Charting a functional atlas from an fMRI dataset

Abstract: The main challenge that we intend to address involves the charting of a functional atlas from a functional Magnetic Resonance Imaging (fMRI) dataset. A large number of internal microscopic variables in the brain and the scanner contribute to the fMRI signal. However, at a macroscopic scale many of these variables are coupled, and we can assume that the fMRI signal can be described by a small number of parameters, in comparison to the large number of degrees of freedom of the original dataset. We take advantage of the implicit low dimensionality of the dataset to construct, in an unsupervised way, a new parametrization of the dataset.  The new parametrization creates meaningful clusters allowing the separation of the dataset into: (1) activated voxels, (2) artefactual signals, and (3) a clutter formed by the background time series.  We have conducted several experiments with synthetic and in-vivo data that demonstrate the performance of our approach.
Francois Meyer is an Associate Professor of Electrical Engineering at the University of Colorado at Boulder. His research interests include signal and image processing and the analysis of biomedical datasets. He received a Ph.D. degree in electrical engineering from INRIA, France, in 1993, and graduated with Honors from Ecole Nationale Superieure d'Informatique et de Mathematiques Appliquees, Grenoble, in 1987, with a M.S. in computer science and applied mathematics.

Nason, GUY Department of Mathematics, University of Bristol

Multiscale adaptive lifting and some applications

Abstract: Lifting generalizes the multiscale method to a wide range of interesting scenarios. This talk describes the `one-coefficient-at-a-time' lifting method and explains how it can be applied to irregularly spaced data and functions on networks. We also mention adaptive lifting, selectively choosing the `best' basis element as the multiscale decomposition proceeds. We show how multiscale adaptive lifting can be used to improve prediction of hydrophobic segments along transmembrane proteins and also in estimating delay times in a transportation network.

Biography: Guy P Nason is Professor of Statistics, Department of Mathematics, University of Bristol, U.K. His research interests are: multiscale methods in statistics, non-stationary time series, variance stabilization, political science and statistics, statistical methods for defence and security, statistics on networks, bioinformatics and data and image fusion. He is the lead author and maintainer of the free WaveThresh package. He was EPSRC Advanced Research Fellow from 2000-5 and was awarded the 2001 Guy Medal in Bronze by the Royal Statistical Society (RSS) for work in wavelet methods in statistics. He was recently Secretary of RSS's Research Section and is currently a member of RSS Council. He also is a member of the IMS, ISI, the Bernoulli Society and the IASC. He is a big fan of Wallace and Gromit and Shrek2.


Ombao, Hernando Department of Statistics, University of Illinois at Urbana-Champaign

Localized Feature Selection for Discrimination and Classification of Non-Stationary Signals

Abstract:  In this talk, we develop an automatic procedure for selecting features for discriminating and classifiying in non-stationary signals. The feature of interest is the spectrum, which is the decomposition of variance across frequency. In practice, signals are non-stationary, that is, the distribution of the variance of signals across frequency changes over time. Thus, for analyzing such signals, we use the SLEX (smooth localized complex exponentials) library. The SLEX library consists of many bases; each basis is composed of localized orthogonal Fourier-like waveforms. The SLEX provides a natural built-in mechanism for extracting localized spectral features. In our procedure, we use a training data set that consists of signals whose group memberships are known. The best basis from the SLEX library is that which minimizes prediction error. We show via some simulation studies that our method is able to consistently identify the most discriminant coefficients and is able to classify signals to the correct groups at a high rate. Finally, we apply our method to magnetoencephalograms, recorded from a standard auditory paired-click paradigm, for classifying subjects in the control and the schizophrenic groups.

Biography:  Hernando Ombao is Associate Professor in the Department of Statistics at the University of Illinois at Urbana-Champaign. He also holds adjunct appointments in Psychology, Psychiatry and Cognitive Neuroscience. He received in Ph.D. degree in Biostatistics at the University of Michigan in 1999. He is actively developing theory and methods for non-stationary signals and images and is keenly interested in the applications to neuroscience and seismology. He currently serves as an Associate Editor for JASA, Theory and Methods.
Park, Cheolwoo Department of Statistics, University of Georgia

Multiscale Analysis on Internet Traffic Data

Abstract: It is important to characterize burstiness of Internet traffic and find the causes for building models that can mimic real traffic. To achieve this goal, exploratory analysis tools and statistical tests are needed, along with new models for aggregated traffic. This talk introduces statistical tools based on wavelets and SiZer (SIgnificance of ZERo crossings of the derivative). The intricate fluctuations of Internet traffic are explored in various respects and lessons on long range dependence and nonstationarities from real data analyses are summarized.

Biography: Cheolwoo Park obtained his Ph.D. in Statistics from Seoul 
National University, Korea, in 2002. He was Postdoc at the  University of North Carolina, Chapel Hill and the  Statistical and Applied Mathematical Sciences Institute, and  Visiting Assistant Professor at the University of Florida.  He is now Assistant Professor in the Department of  Statistics at the University of Georgia.  His research area covers nonparametric function estimation,  Internet traffic data analysis, machine learning, and fMRI  data analysis.

Steele, J. Michael  Wharton School, University of Pennsylvannia 

Banquet Speech: The Kolmogorov membrane and Tukey's Statistical Analog

Biography:  J. Michael Steele has served as the C.F. Koo Professor of Statistics since joining the Wharton School faculty in 1990. Before coming to Wharton, he taught at the University of British Columbia, Stanford University, and Princeton University. He received his B.A. in mathematics from Cornell in 1971 and his Ph.D. in mathematics from Stanford in 1975. Steele’s main area of research is the statistical modeling of asset returns, and he is the author of Stochastic Calculus and Financial Applications, a widely used graduate text. Steele’s most recent book, The Cauchy-Schwarz Master Class, was published by Cambridge University Press in the spring of 2004.

Vannucci, Marina Department of Statistics, Texas A&M University

Bayesian inference for wavelet-based modelling of functional data

Abstract: In this talk I will describe methodologies for Bayesian modelling of functional data that incorporate feature extraction. Practical applications will be classification and clustering problems that involve functional predictors. Wavelet methods will be used for dimension reduction. Wavelets are orthogonal transformations that allow the decomposition of a signal into a set of components, each associated to a particular scale (or resolution). Wavelets have been successfully employed in various ways in the analysis of functional data. In the practical contexts of this talk, curves will be transformed to wavelet coefficients and Bayesian methods will be used to simultaneously estimate group structures among observations (curves) while identifying discriminating local features of the curves via the  selection of  the corresponding wavelet coefficients.  I will present applications to real data. In mass spectrometry, for example, the identification of peaks related to a specific outcome, i.e. peaks that discriminate samples or that predict a clinical response,  is of interest. Another example will look at analysing data from a study involving high-dimensional,  high-frequency tidal volume traces measured during an induced panic model in normal humans. 

Biography: Dr. Vannucci received a Ph.D. in Statistics in 1996 from the
University of Florence, Italy. In 1998 she joined the Department of Statistics at Texas A&M University where she is currently a Full Professor. Her research has focused on the theory and practice of Bayesian variable selection techniques and on the development of wavelet-based statistical models and their applications. Her work is often motivated by real problems that need to be addressed with suitable statistical methods.

Vidakovic, Brani  Department of Biomedical Engineering,  Georgia Institute of
Technology and Emory University

Wavelets in Bioinformatics: Protein and DNA Random Walks and their
Multiscale Analysis

Abstract:  In this talk we overview several applications of wavelets in biomedical research. The emphasis is placed on scaling measures of bio data and their statistical use. In particular, we discuss in more detail so called DNA random walks.
Functional segmentation is one of the many ways to study DNA and proteins.  For example, a genomic sequence under consideration is partitioned into segments and these are identified as a particular functional type of DNA (such as coding or regulatory regions) if some relevant  statistical descriptors match with  of experimentally verified counterparts. DNA and protein random walks and wavelet based measures of their (multi)fractality are tools for classifying segments of DNA and proteins to functional types.
In discussing this application we overview results by C-K. Peng and coauthors, Allan Arneodo and coauthors, Jonas Almeida, and some others, on simple 1-D DNA random walks generated by PP (purine/pyramidine) and WS (weak/strong bonds) polarities. Then we discus multidimensional and marginal protein random walks and their application in protein classification.

Biography:  Brani Vidakovic is Professor of Statistics at The Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University. He has BS and MS in Mathematics from University of Belgrade (1978, 1981) and PhD in Statistics from Purdue University (1992). He was an Assistant and Associate Professor of Statistics and Decision Sciences at Duke University prior to joining Georgia Institute of Technology in 2000. Dr Vidakovic is currently the President of Georgia Chapter of American Statistical Association. He is an Editor-in-Chief of Wiley's Encyclopedia of Statistical Sciences and Associate Editor of several statistical journals. His research interests include wavelets, Bayesian and computational statistics, nonparametrics, and biostatistics.

von Sachs, Rainer  Institut de statistique, Université catholique de Louvain

A multiscale approach for statistical characterization of functional brain images

Abstract: In this talk we present an approach of spatial multiscales for an improved characterization of functional pixel intensities of (medical) images. Examples are numerous such as temporal dependence of brain response intensities measured by fMRI or frequency dependence of NMR spectra measured at each pixel. The overall goal is to improve the misclassification rate in (unsupervised) clustering of the functional image content into a finite but unknown number of classes. Hereby we adopt a non-parametric point of view to reduce the functional dimensionality of the observed pixel intensities,  by statistical aggregation of non-linear  wavelet threshold estimators of these intensity curves. As we model  them by a very general functional form, this is opposed to commonly used parametric feature extraction based on a priori knowledge on the nature of the functional response.
The same paradigm applies to our spatial multiscale approach used to improve upon the low degree of information aggregation of monoscale statistical models used to extract structure in the underlying noisy measurements on the pixel scale. Instead of modelling correlation between neighboring pixels we use an approach based on  Recursive Dyadic Partitioning of the image. Complexity-penalised maximum likelihood estimation based on Gaussian mixture models (in the domain of the discrete wavelet transform of the pixel intensity curves), which we estimate via EM, will allow us to choose the locally best scales for clustering the image content into a finite but adaptively chosen number of cluster classes.
In this talk we present results both on the theoretical treatment of the encountered estimation steps and on the numerical performance of our algorithm on simulated and real data examples.
This is joint work with Anestis Antoniadis (Université Joseph Fourier, Grenoble) and Jérémie Bigot (Université Paul Sabbatier, Toulouse).

Biography: Rainer von Sachs obtained his Ph.D. in Mathematics 1991 from the University of Heidelberg, Germany. Until 1998 he was with the Mathematics Department of the University of Kaiserslautern, Germany. Since then he is Professor of Statistics at the Institut de statistique, Université catholique de Louvain, Louvain-la-Neuve, Belgium. His main research interests are nonparametric curve estimation, time series analysis, wavelets and multiscale methods. Rainer von Sachs is an elected member of ISI and member of the Bernoulli Society and the IMS. He currently serves as Associate Editor of the Journal of the Royal Statistical Society, Series B.

Wang,Yazhen   Statistics, University of Connecticut

Multiscale Jump and Volatility Analysis for High-Frequency Financial Data
Abstract:  Volatilities of asset returns are pivotal for many issues in financial economics. The availability of high frequency intraday data should allow us to estimate volatility more accurately. Asset prices often contain jumps, and high-frequency financial data are inevitably contaminated with market microstructure noise. Existing methods can deal with noisy data for the continuous diffusion price model or handle the jump-diffusion price model without noise. This talk will present estimation of integrated volatility and jump variation for noisy high-frequency financial data with jumps.  The proposed wavelet based multi-scale methodology can cope with both jumps in the price and market microstructure noise in the data, and estimate both integrated volatility and jump variation from the noisy data. We establish convergence rates for the proposed estimators of integrated volatility and jump variation. In particular, we show that the integrated volatility can be estimated asymptotically under the jump-diffusion price model as well as under the continuous diffusion price model. Simulations are conducted to assess the performance of the proposed estimators and to compare them with existing ones. Theoretical and numerical analysis show that the proposed estimators outperform existing methods for noisy high-frequency data under the jump-diffusion model, and have comparable performance for the continuous diffusion model and noiseless jump-diffusion model. The methods are illustrated by applications to two high-frequency exchange rate data sets. This talk is based on a joint work with Jianqing Fan.

Biography Yazhen Wang is Professor of Statistics at the University of Connecticut. He obtained his Ph.D in statistics from University of California at Berkeley in 1992. His research interests are financial econometrics, nonparametric curve estimation, change points, long-memory process and self-similar process, wavelets and multiscale methods, and order restricted statistical inference. Yazhen Wang is an IMS fellow and an elected member of ISI. He currently serves as an Associate Editor of Statistica Sinica.

Wolf, Patrick Statistics Department, Harvard

A Crash Course in Wavelet Methods - Short Course, June 11

Abstract: Patrick J. Wolfe received a B.S. in Electrical Engineering and a B.Mus. concurrently from the University of Illinois <http://www.uiuc.edu> at Urbana-Champaign, both with honors. He earned his Ph.D. in Engineering from the University of Cambridge <http://www.cam.ac.uk> as a US National Science Foundation Graduate Research Fellow, working on the application of perceptual criteria to statistical audio signal processing.

Prior to joining Harvard in 2004, Professor Wolfe held a Fellowship and College Lectureship jointly in Engineering and Computer Science at New Hall <http://www.newhall.cam.ac.uk>, a Cambridge College where he also served as Dean. He has also taught in the Department of Statistical Science <http://www.ucl.ac.uk/Stats/> at University College, London, and continues to act as a consultant to the professional audio community.

In addition to his diverse teaching activities, Professor Wolfe has published in the literatures of engineering, computer science, and statistics, and has received honors from the Acoustical Society of America and the International Society for Bayesian Analysis. His research interests lie at the intersection of statistical signal processing and numerical harmonic analysis, and encompass general as well as audio-related applications.Fwd shortcourse urgent


Graybill Conference
June 11-13, 2006
Colorado State University
Hilton Fort Collins

Fort Collins, CO 80526

2006 Graybill Conference Poster
Last Updated: Friday, April 7, 2006