• home

Abstract: In this study, we develop a factor model to explore areal data collected in space and time. The main goal is to incorporate the factor model with non-linear interactions (proposed in 2013) to handle a spatio-temporal random effect in the structure of the logistic regression. The spatial dependence between regions is established through the CAR model specified for each column of the loadings matrix. Temporal dependence is considered to associate the columns of the factor scores matrix. The presence of non-linear interactions is intended to improve cluster detection since new types of groups can emerge as a combination of main factor effects and the interaction effect. In terms of application, this study is motivated by the analysis of an electrocardiogram data set obtained between 2013 and 2016 in the Telessaude System of the Hospital das Clinicas at the Federal University of Minas Gerais in Brazil.

Join Zoom Meeting https://uvic.zoom.us/j/87235877567

Abstract: Network analysis is one of the prominent multivariate techniques used to study structural and functional connectivity of the brain. In a network model of the brain, vertices are used to represent voxels or regions of the brain, and edges between two nodes represent a physical or functional relationship between the two incident regions. Network investigations of connectivity have produced many important advances in our understanding of brain structure and function, including in domains of aging, learning and memory, cognitive control, emotion, and disease. Despite their use, network methodologies still face several important challenges. In this talk, I will focus on a particularly important challenge in the analysis of structural and functional connectivity: how does one jointly model the generative mechanisms of structural and functional connectivity with other modalities? I propose and describe a statistical network model, called the generalized exponential random graph model (GERGM), that flexibly characterizes the network topology of structural and functional connectivity and can readily integrate other modalities of data. The GERGM also directly enables the statistical testing of individual differences through the comparison of their fitted models. In applying the GERGM to the connectivity of healthy individuals from the Human Connectome Project, we find that the GERGM reveals remarkably consistent organizational properties guiding subnetwork architecture in the typically developing brain. We will discuss ongoing work of how to adapt these models to neuroimaging cohorts associated with the ADRC at the University of Pittsburgh, where the goal is to relate the dynamics of structural and functional connectivity with tau and amyloid – beta deposition in individuals across the Alzheimer’s continuum.

Join Zoom Meeting https://uvic.zoom.us/j/87235877567

Abstract: In this work, improved swarm intelligent algorithms, namely, Salp Swarm Optimization algorithm, whale optimization, and Grasshopper Optimization Algorithm are proposed for data clustering. Our proposed algorithms utilize the crossover operator to obtain an improvised version of the existing algorithms. The performance of our suggested algorithms is tested by comparing the proposed algorithms with standard swarm intelligent algorithms and other existing algorithms in the literature. Non-parametric statistical test, the Friedman test, is applied to show the superiority of our proposed algorithms over other existing algorithms in the literature. The performance of our algorithms outperforms the performance of other algorithms for the data clustering problem in terms of computational time and accuracy.

Join Zoom Meeting https://uvic.zoom.us/j/87235877567

Abstract: Spatio-temporal counts of infectious disease cases often contain an excess of zeros. Existing zero inflated Poisson models applied to such data do not adequately capture the switching of the disease between periods of presence and absence overtime. As an alternative, we develop a new zero-state coupled Markov switching Poisson Model, under which the disease switches between periods of presence and absence in each area through a series of partially hidden nonhomogeneous Markov chains coupled between neighboring locations. When the disease is present, an autoregressive Poisson model generates the cases with a possible 0 representing the disease being undetected. Bayesian inference and prediction is illustrated using spatio-temporal counts of dengue fever cases in Rio de Janeiro, Brazil. This is joint work with Dirk Douwes-Schultz.​

Join Zoom Meeting https://uvic.zoom.us/j/87235877567

Abstract: A two-stage enrichment design is a type of adaptive design, which extends a stratified design with a futility analysis on the marker negative cohort at the first stage, and the second stage can be either a targeted design with only the marker positive stratum, or still the stratified design with both marker strata, depending on the result of the interim futility analysis.

In this talk we consider the situation where the marker assay and the classification rule are possibly subject to error. We derive the sequential tests for the global hypothesis as well as the component tests for the overall cohort and the marker-positive cohort. We discuss the power analysis with the control of the type-I error rate and show the adverse impact of the misclassification on the powers. We also show the enhanced power of the two-stage enrichment over the one-stage design, and illustrate with examples of the recent successful development of immunotherapy in non-small-cell lung cancer.​

Join Zoom Meeting https://uvic.zoom.us/j/87235877567

Bio: Cedric Beaulac has recently received his PhD in Statistical Sciences from the University of Toronto and was awarded a CANSSI Distinguished Postdoctoral Fellowship in 2021. He is currently a postdoctoral fellow at Simon Fraser University and the University of Victoria where he studies neuroimaging genetics problems under the supervision of Farouk Nathoo, Jiguo Cao, and Mirza Faisal Beg. His research interests lie at the intersection of statistical sciences and machine learning.​

Abstract: In this presentation, we discuss the Variational AutoEncodeur (VAE): a latent variable model emerging from the machine learning community. To begin, we introduce the theoretical foundations of the model and its relationship with well-established statistical models. Then, we discuss how we used VAEs to solve two widely different problems. First, we tackled a classic statistical problem, survival analysis, and then a classic machine learning problems, image analysis and image generation. We conclude with a short discussion of our latest research project where we establish a new metric for the evaluation or regularization of latent variable models such a Gaussian Mixture Models and VAEs.​

Join Zoom Meeting https://uvic.zoom.us/j/87235877567

Abstract: In longitudinal studies, we measure the same variables at multiple time-points to track their change over time. The exact data collection schedules (i.e., time of participants' visits) are often pre-determined to accommodate the ease of project management and compliance. Therefore, it is common to schedule those visits at equally spaced time intervals. However, recent publications based on simulated experiments indicate that the power of studies and the precision of model parameter estimators is related to the participants' visiting scheme. So, in this work, we investigate how to schedule participants' visits to better study the accelerated cognitive decline of senior adults, where a broken-stick model is often applied. We formulate this optimal design problem on scheduling participants' visiting into a high- dimensional optimization problem and derive its approximate solution by adding reasonable constraints. Based on this approximation, we propose a novel design of the visiting scheme that aims to maximize the power (i.e. reduce the variance of estimators) in identifying the onset of accelerated decline. Using both simulation studies and evidence from real data, we demonstrate that our design outperforms the standard equally-spaced one when we have strong prior knowledge on the change-points. This novel design helps researchers plan their longitudinal studies with improved power in detecting pattern change without collecting extra data. Also, this individual-level scheduling system helps monitor seniors' cognitive function and, therefore, benefits the development of personal level treatment for cognitive decline, which agrees with the trend of the health care system.

Join Zoom Meeting https://uvic.zoom.us/j/87235877567

Abstract:
We investigate a class of minimax optimal regression designs for models with heteroscedastic errors, and the resulting designs are robust against possible misspecification of the error variance. Since we never know the error variance exactly in practice, it is important to allow small deviations from the assumed error variance and construct robust optimal designs. In this talk, we discuss minimax design criteria, properties of minimax designs, and an effective algorithm to solve challenging non-convex optimization problems for finding those minimax designs on a discrete design space. Examples are given to illustrate minimax optimal designs and their properties. This is a joint work with Hanan Abousaleh.

Join Zoom Meeting https://uvic.zoom.us/j/87235877567

Abstract

The intrinsic infinite-dimensional nature of functional data creates a bottleneck in the application of traditional classifiers to functional settings. These classifiers are generally either unable to generalize to infinite dimensions or have poor performance due to the curse of dimensionality. To address this concern, we propose building a distance-weighted discrimination (DWD) classifier on scores obtained by projecting data onto one specific direction. We choose this direction by minimizing, over a reproducing kernel Hilbert space, an empirical risk function containing the DWD classifier loss function. Our proposed classifier avoids overfitting and enjoys the appealing properties of DWD classifiers. We further extend this framework to accommodate functional data classification problems where scalar covariates are involved. In contrast to previous work, we establish a non-asymptotic estimation error bound on the relative misclassification rate. Through simulation studies and a real-world application, we demonstrate that the proposed classifier performs favourably relative to other commonly used functional classifiers in terms of prediction accuracy in finite-sample settings.

https://uvic.zoom.us/j/87235877567