Events

Sep 17 - Sep 23, 2023

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  Predictive Performance Test based on the Exhaustive Nested Cross-Validation for High-dimensional data

  Iris Ivy Gauran, Postdoctoral Research Fellow, Biostatistics Group

  Sep 21, 12:00 - 13:00

  B9 L2 H2

  Cross-validation is an algorithmic technique extensively used for estimating the prediction error, tuning the regularization parameter, and choosing between competing predictive rules.

Apr 2 - Apr 8, 2023

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  Modeling and Inference for Multivariate Time Series, with Applications to Integer-Valued Processes and Nonstationary Extreme Data

  Matheus B. Guerrero, Ph.D. Student, Statistics

  Apr 4, 16:00 - 19:00

  B4 L5 R5220

  statistical methods integer-valued data autoregressive processes multivariate nonstationary extreme data

  This Ph.D. research focuses on proposing new statistical methods for two types of time series data: integer-valued data and multivariate nonstationary extreme data. For the former, the researcher proposes a novel approach to building an integer-valued autoregressive (INAR) model that offers the flexibility to specify both marginal and innovation distributions, leading to several new INAR processes. For the latter, the researcher proposes new extreme value theory methods for analyzing multivariate nonstationary extreme data, specifically EEG recordings from patients with epilepsy. Two extreme-value methods, Conex-Connect and Club Exco, are proposed to study alterations in the brain network during extreme events such as epileptic seizures.

Feb 12 - Feb 18, 2023

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  Conditional Kernel Density Estimation For Dependent Data, Mean Shift Algorithm and Predictive Regions in Human Health

  Prof. Mohamed El Machkouri, Associate professor, Applied Mathematics, University of Rouen Normandy

  Feb 12, 16:00 - 17:30

  B1 L4 R4102

  nonparametric estimation

  In this talk, we present theoretical asymptotic results for the nonparametric estimation of the conditional density of a scalar response variable Y given the explanatory X taking values in a finite-dimensional space when the sample of observations is considered as a sequence of dependent random variables.

Jan 1 - Jan 7, 2023

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  Bayesian Non-parametric Models for Time Series Decomposition

  Guillermo C. Granados Garcia, Ph.D. Student, Statistics

  Jan 5, 17:00 - 19:00

  B1 R4214

  spectral density function

  The standard approach to analyzing brain electrical activity is to examine the spectral density function (SDF) and identify frequency bands, defined apriori, that have the most substantial relative contributions to the overall variance of the signal. However, a limitation of this approach is that the precise frequency and bandwidth of oscillations are not uniform across cognitive demands. Thus, these bands should not be arbitrarily set in any analysis. To overcome this limitation, we propose three Bayesian Non-parametric models for time series decomposition, which are data-driven approaches that identify (i) the number of prominent spectral peaks, (ii) the frequency peak locations, and (iii) their corresponding bandwidths (or spread of power around the peaks).

Apr 24 - Apr 30, 2022

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  Recent approaches for flexible and efficient analysis of non-stationary time series

  Prof. Raquel Prado, Department of Statistics, University of California

  Apr 28, 16:30 - 17:30

  B2 B3 A0215

  During the first part of the talk we present an approach that allows for flexible analysis of multivariate non-stationary time series via dynamic models on the partial autocorrelation domain. We discuss various aspects of these models, including the use of shrinkage priors to deal with overfitting issues, as well as hierarchical extensions.
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  Time-domain and frequency-domain approaches for multiple and multivariate time series - Lecture 3

  Prof. Raquel Prado, Department of Statistics, University of California

  Apr 27, 16:00 - 17:30

  B1 L4 R4102

  In the first part of this lecture we present a review dynamic linear models for multivariate time series and hierarchical dynamic linear models for multiple time series. Topics related to model building as well as closed form, approximate and simulation-based methods for Bayesian filtering, smoothing and forecasting within these classes of models will be discussed.
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  Non-Gaussian geostatistical models using nearest neighbors processes

  Prof. Bruno Sanso, Department of Statistics, University of California

  Apr 26, 16:30 - 17:30

  B2 B3 A0215

  We present a framework for non-Gaussian spatial processes that encompasses large distribution families. Spatial dependence for a set of irregularly scattered locations is described with a mixture of pairwise kernels. Focusing on the nearest neighbors of a given location, within a reference set, we obtain a valid spatial process:
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  Conditionally Gaussian dynamic models, non-linear models and multi-process models for univariate time series - Lecture 2

  Prof. Raquel Prado, Department of Statistics, University of California

  Apr 26, 14:00 - 15:30

  B1 L4 R3119

  We discuss conditionally Gaussian dynamic linear models for analysis and forecasting of univariate time series and present simulation-based methods for Bayesian filtering and smoothing within this class of models, including Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods.
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  Spatio-Temporal Statistical Models Using Integro-Differential Equations

  Prof. Bruno Sanso, Department of Statistics, University of California

  Apr 25, 17:45 - 19:00

  B1 L4 R4102

  We will start by presenting the general framework of Bayesian hierarchical dynamic models (BHDM) for space-time data. Within this framework, we will consider in some detail the special case of linear dynamics. We will review MCMC estimation for conditionally linear dynamic models. We will introduce integro-differential models and give a SPDE justification that provides insights into the connections between the dynamics of the process and the properties of the kernel defining the IDE.
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  Dynamic linear models: Theory, computation and case studies - Lecture 1

  Prof. Raquel Prado, Department of Statistics, University of California

  Apr 25, 16:00 - 17:30

  B1 L4 R4102

  In this lecture we present an overview of dynamic linear models for analysis and forecasting of univariate time series. We will discuss principles for model building and methods for Bayesian filtering, smoothing and forecasting. We will illustrate the use of these models in several case studies arising in different applied areas including environmental sciences and neuroscience.
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  Model Survival Data with high and ultrahigh dimensional predictors: feature screening and statistical inference - Lecture 2

  Yi Li, Professor, Biostatistics, University of Michigan

  Apr 24, 16:00 - 18:00

  B1 L4 R4102

  Continuing on with Lecture 1, this short course introduces various cutting-edge methods that handle survival outcome data with ultrahigh dimensional predictors, that is, when the dimension of predictors is much higher than the sample size. We will also discuss several new methods for quantifying the uncertainty of estimates in a high dimensional survival setting, a very active area in machine learning.

Apr 17 - Apr 23, 2022

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  Gaussian Graphical Regression with High-Dimensional Responses and Covariates

  Yi Li, Professor, Biostatistics, University of Michigan

  Apr 21, 16:30 - 17:30

  B2 B3 A0215

  Though Gaussian graphical models have been widely used in many scientific fields, relatively limited progress has been made to link graph structures to external covariates. We propose a Gaussian graphical regression model, which regresses both the mean and the precision matrix of a Gaussian graphical model on covariates. In the context of co-expression quantitative trait locus (QTL) studies, our method can determine how genetic variants and clinical conditions modulate the subject-level network structures, and recover both the population-level and subject-level gene networks.
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  Regularized Regression for Survival Data: Methods and Applications

  Yi Li, Professor, Biostatistics, University of Michigan

  Apr 19, 16:00 - 18:00

  B1 L4 R4102

  In the era of biomedical big data, survival outcome data with high-throughput predictors are routinely collected. These high dimensional data defy classical survival regression models, which are either infeasible to fit or likely to incur low predictability because of overfitting. This short course will introduce the basic concepts of survival analysis and various cutting-edge methods that handle survival outcome data with high dimensional predictors. I will cover statistical principles and concepts behind the methods, and will also discuss their applications to the real medical examples.

Nov 28 - Dec 4, 2021

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  Semiparametric Regression Models for Between and Within-subject Attributes: Applications to High-Dimensional Data, Asymptotic Efficiency and Beyond

  Jinyuan Liu, PhD Student, Biostatistics, University of California, USA

  Nov 28, 08:30 - 09:30

  KAUST

  Breakthroughs such as high-throughput sequencing are generating flourishing high-dimensional data that provoke challenges in both statistical analyses and interpretations.

Oct 10 - Oct 16, 2021

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  Bayesian image analysis in Fourier space (BIFS) models and some relationships with Markov random fields

  Prof. John Kornak, Biostatistics, University of California, San Francisco

  Oct 14, 16:30 - 17:45

  B4 B5 A0215

  bayesian analysis

  Abstract For over 30 years, Bayesian image analysis has provided an important pathway to image reconstruction and enhancement, by balancing a priori expectations of image characteristics with a model for the noise process. The conventional Bayesian modeling approach defined in image space implements priors that describe inter-dependence between spatial locations (and can therefore be difficult to model and compute). However, similar models can be developed more conveniently in Fourier transformed space as a large set of independent processes. The originally complex high-dimensional estimation

Nov 22 - Nov 28, 2020

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  BICNet: A Bayesian Approach for Estimating Task Effects on Intrinsic Connectivity Networks in fMRI Data

  Meini Tang, M.S. Student, Statistics

  Nov 24, 09:00 - 10:00

  KAUST

  Intrinsic connectivity networks (ICNs) refer to brain functional networks that are consistently found under various conditions, during tasks or at rest. Some studies demonstrated that while some stimuli do not impact intrinsic connectivity, other stimuli actually activate intrinsic connectivity through suppression, excitation, moderation or modification. Most analyses of fMRI data use ad-hoc methods to estimate the latent structure of ICNs. Modeling the effects on ICNs has also not been fully investigated. We propose a Bayesian Intrinsic Connectivity Network (BICNet) model, an extended Bayesian dynamic sparse latent factor model, to identify the ICNs and quantify task-related effects on the ICNs. BICNet has the following advantages: (1) It simultaneously identifies the individual and group-level ICNs; (2) It robustly identifies ICNs by jointly modeling rfMRI and tfMRI; (3) Compared to ICA-based methods, it can quantify the difference of ICN amplitudes across different states; (4) The sparsity of ICNs automatically performs feature selection, instead of ad-hoc thresholding. We apply BICNet to the rfMRI and language tfMRI data from the HCP and identify several ICNs related to distinct language processing functions.

Mar 29 - Apr 4, 2020

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  Classification of Functional Data by Detecting the Discrepancy of Second Moment Structure of Scaled functions

  Shuhao Jiao, Postdoctoral Research Fellow, Statistics

  Apr 2, 12:00 - 13:00

  KAUST

  Functional data

  This talk presents a new classification method for functional data. We consider the case where different groups of functions have similar means so that it is difficult to classify them based on only the mean function. To overcome this limitation, we propose the second moment based functional classifier (SMFC). Here, we demonstrate that the new method is sensitive to divergence in the second moment structure and thus produces lower rate of misclassification compared to other competitor methods. Our method uses the Hilbert-Schmidt norm to measure the divergence of second moment structure. One important innovation of our classification procedure lies in the dimension reduction step. The method data-adaptively discovers the basis functions that best capture the discrepancy between the second moment structures of the groups, rather than uses the functional principal component of each individual group, and good performance can be achieved as unnecessary variability is removed so that the classification accuracy is improved. Consistency properties of the classification procedure and the relevant estimators are established. Simulation study and real data analysis on phoneme and rat brain activity trajectories empirically validate the superiority of the proposed method.

Feb 2 - Feb 8, 2020

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  Geospatial Data Science for Public Health Surveillance - Seminar

  Paula Moraga, Lecturer, Department of Mathematical Sciences, University of Bath, UK

  Feb 5, 12:00 - 13:00

  B9 L2 H2

  Geospatial Data Public Health

  In this talk, I will give an overview of my research which focuses on the development of innovative statistical methods and interactive visualization applications for geospatial data analysis and health surveillance. I will illustrate some of my projects in the following areas: 1. Development of new statistical methodology; 2. Development of open-source statistical software such as the R packages; 3. Health surveillance projects. Finally, I will describe my future research on innovation in data acquisition and visualization, precision disease mapping, and digital health surveillance, and how it can inform policymaking and improve population health globally.

Nov 17 - Nov 23, 2019

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  2019 Statistics and Data Science Workshop

  Nov 18, 00:00 - Nov 20, 23:45

  B2 B3 A0215

  2019 Statistics and Data Science Workshop confirmed speakers include Prof. Alexander Aue, University of California Davis, USA, Prof. Francois Bachoc, University Toulouse 3, France, Prof. Rosa M. Crujeiras Casais, University of Santiago de Compostela, Spain, Prof. Emanuele Giorgi, Lancaster University, UK, Prof. Jeremy Heng, ESSEC Asia-Pacific, Singapore, Prof. Birgir Hrafnkelsson, University of Iceland, Iceland, Prof. Ajay Jasra, KAUST, Saudi Arabia, Prof. Emtiyaz Khan, RIKEN Center for Advanced Intelligence Project, Japan, Prof. Robert Krafty, University of Pittsburgh, USA, Prof. Guido Kuersteiner, University of Maryland, USA, Prof. Paula Moraga, University of Bath, UK, Prof. Tadeusz Patzek, KAUST, Saudi Arabia, Prof. Brian Reich, North Carolina State University, USA, Prof. Dag Tjostheim, University Bergen, Norway, Prof. Xiangliang Zhang, KAUST, Saudi Arabia, Sylvia Rose Esterby, University of British Colombia, Canada, Prof. Abdel El-Shaarawi, Retired Professor at the National Water Research Institute, Canada. View Workshop schedule and abstracts here.

Apr 21 - Apr 27, 2019

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  Almost everything you always wanted to know about NONLINEAR STATE SPACE MODELS (but were afraid to ask)

  Prof, David Stoffer, University of Pittsburgh, Pennsylvania, USA

  Apr 26, 15:00 - 18:00

  B1 L4 R4102

  university of pittsburgh linear guassian model Numerical Optimization

  Ever wonder why, when you fly to Jeddah you don't end up in Riyadh? The tracking devices use a nonlinear state space model to make sure your plane is on course. While inference for the linear Gaussian model is fairly simple, inference for nonlinear models can be difficult and often relies on derivative free numerical optimization techniques. A promising method that I will discuss is based on particle approximations of the conditional distribution of the hidden process given the data. This distribution is needed for both classical inference (e.g., Monte Carlo EM type algorithms) and Bayesian inference (e.g., Gibbs sampler).
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  Forecasting Multiple Time Series with One-Sided Dynamic Principal Components

  Prof. Daniel Peña Sánchez de Rivera, Department of Statistics, Universidad Carlos III de Madrid

  Apr 25, 16:00 - 17:00

  B1 L4 R4102

  Time Series big data

  Generalized Dynamic principal components are presented and it is shown how to define one side inear combinations of the present and past values of the series that minimize the reconstruction mean squared error (ODPC). It is shown that the ODPC introduced in this paper can be successfully used for forecasting high-dimensional multiple time series.
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  Inference for Spatial Trends

  Professor Ngai Hang Chan, Professor of Statistics, Chinese University of Hong Kong

  Apr 23, 16:00 - 17:00

  B1 L4 R4102

  Non-stationary spatial models are widely applicable in diverse disciplines, ranging from bio-medical sciences to geophysical studies. In many of theses applications, testing for structural changes in the trend and testing the specific form of the trend are highly relevant. A novel statistics based on a discrepancy measure over small regions is proposed in this paper. Such a measure can be used to construct tests for structural trends and to identify change boundaries of the trends. By virtue of the m-dependence approximation of a stationary random eld, asymptotic properties and limit distributions of these tests are established. The method is illustrated by simulations and data analysis.

Nov 11 - Nov 17, 2018

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  2018 Workshop on Statistics and Data Science

  Nov 12, 08:30 - Nov 15, 17:00

  Auditorium 0215 between Buildings 4 & 5

  data science high dimensional statistics modeling biological processes physical processes

  The Statistics Program at KAUST is proud to host the 2018 Workshop on Statistics and Data Science. This workshop gathers the leading experts on statistical data science to discuss the current needs, challenges and opportunities of modeling massive and high dimensional data, predicting complex biological and physical processes. The workshop will run from November 12-14. Talks and posters presentations will take place in Auditorium 0215 (between Buildings 4 & 5). View Workshop's Agenda.