Event(s) on February 2023

• Thursday, 2nd February, 2023

  Title:	Network functional varying coefficient model
  Speaker:	Professor Yanyuan Ma, Department of Statistics, Penn State University, USA
  Time/Place:	15:00  -  16:00 FSC1217
  Abstract:	We consider functional responses with network dependence observed for each individual at irregular time points. To model both the inter-individual dependence as well as within-individual dynamic correlation, we propose a network functional varying coefficient (NFVC) model. The response of each individual is characterized by a linear combination of responses from its connected nodes and its own exogenous covariates. All the model coefficients are allowed to be time dependent. The NFVC model adds to the richness of both the classical network autoregression model and the functional regression models. To overcome the complexity caused by the network inter-dependence, we devise a special nonparametric least squares type estimator, which is feasible when the responses are observed at irregular time points for different individuals. The estimator takes advantage of the sparsity of the network structure to reduce the computational burden. To further conduct the functional principal component analysis, a novel within-individual covariance function estimation method is proposed and studied. Theoretical properties of our estimators are analyzed, which involve techniques related to empirical processes, nonparametrics, functional data analysis and various concentration inequalities. We analyze a social network data to illustrate the powerfulness of the proposed procedure.

  
  

• Thursday, 2nd February, 2023

  Title:	Sample size determination for interval estimation of the prevalence of a sensitive attribute under randomized response models
  Speaker:	Professor Shifang Qiu, Department of Statistics, Chongqing University of Technology, China
  Time/Place:	16:00  -  17:00 FSC1217
  Abstract:	Studies with sensitive questions should include a sufficient number of respondents to adequately address the research interest. While studies with an inadequate number of respondents may not yield significant conclusions, studies with an excess of respondents become wasteful of investigators’ budget. Therefore, it is an important step in survey sampling to determine the required number of participants. In this article, we derive sample size formulas based on confidence interval estimation of prevalence for four randomized response models, namely, the Warner’s randomized response model, unrelated question model, item count technique model and cheater detection model. Specifically, our sample size formulas control, with a given assurance probability, the width of a confidence interval within the planned range. Simulation results demonstrate that all formulas are accurate in terms of empirical coverage probabilities and empirical assurance probabilities. All formulas are illustrated using a real-life application about the use of unethical tactics in negotiation.

  
  

• Friday, 3rd February, 2023

  Title:	Some studies on distributed multi-agent optimization
  Speaker:	Dr. Zhan Yu, The University of Hong Kong
  Time/Place:	11:00  -  12:00 FSC1217, or Zoom (Meeting ID: 979 5764 2212)
  Abstract:	In this talk, we introduce a distributed optimization problem over a multi-agent network. For solving the problem, a distributed gradient-free (zeroth-order) mirror descent method is provided by introducing a randomized gradient-free oracle in the mirror descent scheme associated with each local agent. We introduce two types of approximating sequences related to local agents. After describing the main ideas on estimating the error bounds, we introduce the ergodic convergence results of the two approximating sequences. The method is applicable to some tough environments where the first-order gradient information is inaccessible or costly to evaluate and the objective function is nonsmooth. We will also briefly discuss some other methods related to distributed optimization.

  
  

• Friday, 10th February, 2023

  Title:	High Dimensional Data Recovery
  Speaker:	Prof Michael Ng, Department of Mathematics, The University of Hong Kong, Hong Kong
  Time/Place:	11:30 FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
  Abstract:	In recent years, high dimensional data recovery has been extensively studied and analyzed, which can be applied to many data science applications such as recommendation, image recovery and statistical estimation. In this talk, we present several variants of data recovery models and their applications. We provide theoretical insight to create a rigorous scientific basis for solving such data recovery problems. Numerical examples are also given to demonstrate the usefulness of these models.

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