Events

Nov 12 - Nov 18, 2023

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  Family of Mean-Mixtures of Multivariate Normal Distributions

  Prof. Narayanaswamy Balakrishnan, Department of Mathematics and Statistics, McMaster University

  Nov 12, 15:30 - 16:30

  B1 L4 R4102

  In this talk, I will describe the family of mean-mixtures of multivariate normal distributions and establish many of its properties, stochastic representations, moments, distributional shape characteristics, etc.
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  Models and Inference for Non-Gaussian Random Vectors and Fields

  Sagnik Mondal, Ph.D. Student, Statistics

  Nov 12, 12:30 - 14:30

  B5 L5 R5209

  The multivariate Gaussian distribution is widely used in many statistical applications due to its appealing features. However, real-world data often violate its assumptions, showing skewness and/or tail-thickness.

Sep 10 - Sep 16, 2023

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  Goodness-of-fit tests for multivariate skewed distributions based on the characteristic function

  Maicon J. Karling, Postdoctoral Research Fellow, Statistics

  Sep 14, 12:00 - 13:00

  B9 L2 H2

  Goodness-of-fit tests determine how well a set of observed data fits a particular probability distribution. They can also show if some categorical variable follows a hypothesized family of distributions.

Jun 4 - Jun 10, 2023

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  Stochastic environmental modeling in a time of convergence: physics meets artificial intelligence

  Prof. Stefano Castruccio, Associate professor, University of Notre Dame, USA

  Jun 4, 10:00 - 11:00

  B1 L4 R4102

  It is widely acknowledged how the relentless surge of Volume, Velocity and Variety of data, as well as the simultaneous increase of computational resources have stimulated the development of data-driven methods with unprecedented flexibility and predictive power. However, not every environmental study entails a large data set: many applications ranging from astronomy or paleo-climatology have a high associated sampling cost and are instead constrained by physics-informed partial differential equations. Throughout the past few years, a new and powerful paradigm has emerged in the machine learning literature, merging data-driven and physics-informed problems, hence providing a unified framework for a whole spectrum of problems ranging from data-rich/context-poor to data-poor/context-rich. In this talk, I will present this new framework and discuss some of the most recent efforts to reformulate it as a stochastic model-based approach, thereby allowing calibrated uncertainty quantification.

Apr 30 - May 6, 2023

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  Sub-dimensional Mardia measures of multivariate skewness and kurtosis

  Joydeep Chowdhury, Postdoctoral Research Fellow, Statistics

  May 1, 12:00 - 13:00

  B9 L3 R3128

  The Mardia measures of multivariate skewness and kurtosis summarize the respective characteristics of a multivariate distribution with two numbers. However, these measures do not reflect the sub-dimensional features of the distribution. Consequently, testing procedures based on these measures may fail to detect skewness or kurtosis present in a sub-dimension of the multivariate distribution. We introduce sub-dimensional Mardia measures of multivariate skewness and kurtosis, and investigate the information they convey about all sub-dimensional distributions of some symmetric and skewed families of multivariate distributions.

Nov 6 - Nov 12, 2022

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  The role of skewed distributions in Bayesian inference: conjugacy, scalable approximations and asymptotics

  Daniele Durante, Assistant Professor of Statistics at the Department of Decision Sciences, Bocconi University, Italy

  Nov 8, 15:00 - 16:00

  B1 L4 R4102

  bayesian inference

  In this talk, I will review, unify and extend recent advances in Bayesian inference and computation for such a class of models, proving that unified skew-normal (SUN) distributions (which include Gaussians as a special case) are conjugate to the general form of the likelihood induced by these formulations. This result opens new avenues for improved posterior inference, under a broad class of widely-implemented models, via novel closed-form expressions, tractable Monte Carlo methods based on independent and identically distributed samples from the exact SUN posterior, and more accurate and scalable approximations from variational Bayes and expectation-propagation. These results will be further extended, in asymptotic regimes, to the whole class of Bayesian generalized linear models via novel limiting approximations relying on skew-symmetric distributions.
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  Multivariate Functional Data Analysis and Visualization

  Zhuo Qu, Ph.D. Student, Statistics

  Nov 7, 11:00 - 13:00

  B3 L5 R5220

  functional data analysis multivariate functional data statistics

  As a branch of statistics, functional data analysis studies observations regarded as curves, surfaces, or other objects evolving over a continuum. Current methods in functional data analysis usually require data to be smoothed and analyzed marginally, which may hide some outlier information or take extra time on pretreating the data. After exploring model-based fitting for regularly observed multivariate functional data, we explore new visualization tools, clustering, and multivariate functional depths for irregularly observed (sparse) multivariate functional data.

May 29 - Jun 4, 2022

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  Differentially private testing of symmetry equivalence hypotheses based on the characteristic function

  Prof. Simos G. Meintanis, University of Athens

  May 29, 15:30 - 16:30

  B1 L4 R4102

  Differential privacy Neighborhood of model validation Symmetry testing

  We investigate privacy aspects of tests for symmetry equivalence null hypotheses. Specifically, we consider weighted L2 type tests as well as chi-squared type tests for multivariate symmetry based on the characteristic function, and their privacy properties are specifically quantified within the context of differential privacy. We consider both the case of known centre as well as tests for symmetry about an unknown centre.

Feb 20 - Feb 26, 2022

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  High Performance Statistical Computing (HPSC): Challenges and Best Practices

  Sameh Abdulah, Research Scientist, Hierarchical Computations on Manycore Architectures

  Feb 24, 19:00 - Feb 25, 02:00

  KAUST

  applied statistics big data

  ************************************************** Organizers: Sameh Abdulah and Marc Genton ************************************************** Abstract Big data in terms of volume, intensity, and complexity is one of the main challenges to statisticians. The availability of different data sources has essential implications in collecting colossal data volumes that require other ways to be managed and analyzed. Existing analytical tools cannot be applied easily to big data volumes due to memory and computation constraints. Previously, statistical applications and traditional high performance

Jun 6 - Jun 12, 2021

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  Lagrangian Spatio-Temporal Covariance Functions for Multivariate Nonstationary Random Fields

  Mary Lai O. Salvana, Ph.D. Student, Statistics

  Jun 7, 17:00 - 19:00

  KAUST

  In geostatistical analysis, we are faced with the formidable challenge of specifying a valid spatio-temporal covariance function, either directly or through the construction of processes. This task is difficult as these functions should yield positive definite covariance matrices. In recent years, we have seen a flourishing of methods and theories on constructing spatio-temporal covariance functions satisfying the positive definiteness requirement. The current state-of-the-art when modeling environmental processes are those that embed the associated physical laws of the system. The class of Lagrangian spatio-temporal covariance functions fulfills this requirement. Moreover, this class possesses the allure that they turn already established purely spatial covariance functions into spatio-temporal covariance functions by a direct application of the concept of Lagrangian reference frame. In this dissertation, several developments are proposed and new features are provided to this special class.

May 30 - Jun 5, 2021

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  Stationary and Cyclostationary Processes for Time Series and Spatio-Temporal Data

  Soumya Das, Ph.D. Student, Statistics

  Jun 1, 16:00 - 18:00

  KAUST

  Due essentially to the difficulties associated with obtaining explicit forms of stationary marginal distributions of non-linear stationary processes, appropriate characterizations of such processes are worked upon little. After discussing an elaborate motivation behind this thesis and presenting preliminaries in Chapter 1, we characterize, in Chapter 2, the stationary marginal distributions of certain non-linear multivariate stationary processes. To do so, we show that the stationary marginal distributions of these processes belong to specific skew-distribution families, and for a given skew-distribution from the corresponding family, a process, with stationary marginal distribution identical to that given skew-distribution, can be found.

Feb 28 - Mar 6, 2021

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  On The Coupling between HPC and Statistics: Challenges, Opportunities, and Future Trends of Emerging Techniques -- (Virtual SIAM CSE21 Minisymposium)

  Mar 1, 09:00 - 16:00

  KAUST

  applied statistics big data

  Abstract Big data modeling/inference and large-scale simulations have followed largely independent paths to the high-performance computing (HPC) frontier, but important opportunities now arise that can be addressed by combining the strengths of each. HPC is becoming increasingly significant in scaling existing statistical methods to larger and more complex applications and developing novel methods that are amenable to scaling within the constraints that exist in modern HPC architectures. The purpose of this minisymposium is to bring together researchers in the area of statistics and HPC to

Oct 25 - Oct 31, 2020

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  Spatio-Temporal Statistical Modeling with Application to Wind Energy Assessment in Saudi Arabia

  Wanfang Chen, Ph.D., Statistics

  Oct 26, 15:00 - 17:00

  KAUST

  In this thesis, I firstly provide a comprehensive assessment of wind energy resources and associated spatio-temporal patterns over Saudi Arabia in both current and future climate conditions, based on a Regional Climate Model output. A high wind energy potential exists and is likely to persist at least until 2050 over a vast area of Western Saudi Arabia, particularly in the region between Medina and the Red Sea coast and during Summer months. Since an accurate assessment of wind extremes is crucial for risk management purposes, I then present the first high-resolution risk assessment of wind extremes over Saudi Arabia.

Apr 5 - Apr 11, 2020

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  Computation of High-Dimensional Multivariate Normal and Student-t Probabilities Based on Matrix Compression Schemes

  Jian Cao, Ph.D., Statistics

  Apr 6, 16:00 - 18:00

  KAUST

  The thesis focuses on the computation of high-dimensional multivariate normal (MVN) and multivariate Student-t (MVT) probabilities. Firstly, a generalization of the conditioning method for MVN probabilities is proposed and combined with the hierarchical matrix representation. Next, I revisit the Quasi-Monte Carlo (QMC) method and improve the state-of-the-art QMC method for MVN probabilities with block reordering, resulting in a ten-time-speed improvement. The thesis proceeds to discuss a novel matrix compression scheme using Kronecker products. This novel matrix compression method has a memory footprint smaller than the hierarchical matrices by more than one order of magnitude. A Cholesky factorization algorithm is correspondingly designed and shown to accomplish the factorization in 1 million dimensions within 600 seconds. To make the computational methods for MVN probabilities more accessible, I introduce an R package that implements the methods developed in this thesis and show that the package is currently the most scalable package for computing MVN probabilities in R. Finally, as an application, I derive the posterior properties of the probit Gaussian random field and show that the R package I introduce makes the model selection and posterior prediction feasible in high dimensions.

Mar 8 - Mar 14, 2020

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  Functional data depth and its application in the visualization of spatio-temporal covariance structures

  Mar 12, 12:00 - 13:00

  KAUST

  functional data analysis spatio-temporal statistics

  Functional data analysis is a very active research area due to the overwhelming existence of functional data. In the first part of this talk, I will introduce how functional data depth is used to carry out exploratory data analysis and explain recently-developed depth techniques. In the second part, I will discuss spatio-temporal statistical modeling. It is challenging to build realistic space-time models and assess the validity of the model, especially when datasets are large. I will present a set of visualization tools we developed using functional data analysis techniques for visualizing covariance structures of univariate and multivariate spatio-temporal processes. I will illustrate the performance of the proposed methods in the exploratory data analysis of spatio-temporal data.

Nov 24 - Nov 30, 2019

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  Asymptotically Exact Variational Bayes for High-Dimensional Probit Models

  Prof. Daniele Durante, Department of Decision Sciences, Bocconi University, Italy

  Nov 27, 15:30 - 16:30

  B1 L4 R4102

  Bayesian approach to inference decision sciences

  Abstract There are several Bayesian models where the posterior density is not available in a closed and tractable form. In these situations, Markov chain Monte Carlo algorithms and approximate methods, such as variational Bayes and expectation-propagation, provide common solutions to perform posterior inference. However, in high-dimensional studies, with large or even huge p, these approaches still face open problems in terms of scalability and quality of the posterior approximation. Notably, such issues also arise in basic predictor-dependent models for binary data which appear in a wide

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.

Nov 10 - Nov 16, 2019

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  Optimal Singular Value Thresholding in Correlated Noise

  Prof. David L. Donoho, Department of Statistics, Stanford University

  Nov 12, 15:00 - 16:00

  B19 Auditorium

  We consider the problem of recovering a low-rank signal matrix in the presence of a general, unknown additive noise; more specifically, noise where the eigenvalues of the sample covariance matrix have a general bulk distribution. We assume given an upper bound for the rank of the assumed orthogonally invariant signal, and develop a selector for hard thresholding of singular values, which adapts to the unknown correlation structure of the noise.
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  Deepnet Spectra and the Two Cultures of Data Science

  Prof. David L. Donoho, Department of Statistics, Stanford University

  Nov 12, 12:00 - 13:00

  B9 L2 H2 R2325

  A variety of intriguing patterns in eigenvalues were observed and speculated about in ML conference papers. We describe the work of Vardan Papyan showing that the traditional subdisciplines, properly deployed, can offer insights about these objects that ML researchers had.

Mar 31 - Apr 6, 2019

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  High-dimensional Q-learning for Dynamic Treatment Regimes

  Prof. Rui Song, Department of Statistics, North Carolina State University

  Mar 31, 12:00 - 13:00

  B3 L5 R5220

  HQ-learning statistical inference data analysis simulation

  Dynamic treatment regimes are a set of decision rules and each treatment decision is tailored over time according to patients’ responses to previous treatments as well as covariate history. There is a growing interest in development of correct statistical inference for optimal dynamic treatment regimes to handle the challenges of nonregularity problems in the presence of nonrespondents who have zero-treatment effects, especially when the dimension of the tailoring variables is high.

Oct 4 - Oct 10, 2015

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  Workshop on Computational Space-Time Statistics

  Oct 4, 09:45 - Oct 6, 10:45

  B1 L4 R4102

  Statistics of extremes Environmental Statistics

  Workshop on Computational Space-Time Statistics