Colloquium/Seminar

YearMonth
2017 Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Oct   Nov   Dec  
2016 Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Oct   Nov   Dec  
2015 Jan   Feb   Mar   Apr   May   Jun   Aug   Sep   Oct   Nov   Dec  
2014 Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec  
2013 Jan   Feb   Mar   Apr   May   Jun   Aug   Sep   Nov   Dec  
2012 Jan   Feb   Apr   May   Jun   Jul   Aug   Sep   Nov   Dec  
2011 Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec  
2010 Jan   Feb   Mar   Apr   May   Jun   Sep   Oct   Nov   Dec  
2009 Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec  
2008 Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec  
2007 Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec  
2006 Jan   Feb   Mar   Apr   May   Jun   Jul   Sep   Oct   Nov   Dec  
2005 Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec  
2004 Jan   Feb   Mar   Apr   May   Aug   Sep   Oct   Nov   Dec  

Event(s) on September 2012


  • Monday, 3rd September, 2012

    Title: TVICA - Time Varying Independent Component Analysis and Its Application to Financial Data
    Speaker: Prof. Wolfgang K. Härdle, Ladislaus von Bortkiewicz Chair of Statistics C.A.S.E. - Centre for Applied Statistics and Economic, Humboldt-Universität zu Berlin, Germany
    Time/Place: 11:00  -  12:00
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: Source extraction and dimensionality reduction are important in analyzing high dimensional and complex financial time series that are neither Gaussian distributed nor stationary. Independent component analysis (ICA) method can be used to factorize the data into a linear combination of independent components, so that the high dimensional problem is converted to a set of univariate ones. However conventional ICA methods implicitly assume stationarity or stochastic homogeneity of the analyzed time series, which leads to a low accuracy of estimation in case of a changing stochastic structure. A time varying ICA (TVICA) is proposed here. The key idea is to allow the ICA filter to change over time, and to estimate it in so-called local homogeneous intervals. The question of how to identify these intervals is solved by the LCP (local change point) method. Compared to a static ICA, the dynamic TVICA provides good performance both in simulation and real data analysis. The data example is concerned with independent signal processing and deals with a portfolio of highly traded stocks.


  • Wednesday, 5th September, 2012

    Title: Local Quantile Regression
    Speaker: Ms. WANG Weining, Ladislaus von Bortkiewicz Chair of Statistics C.A.S.E. - Center for Applied Statistics & Economics C, Humboldt-Universität zu Berlin, Germany
    Time/Place: 10:00  -  11:00
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: Quantile regression is a technique to estimate conditional quantile curves. It provides a comprehensive picture of a response contingent on explanatory variables. In a flexible modeling framework, a specific form of the conditional quantile curve is not a priori fixed. This motivates a local parametric rather than a global fixed model fitting approach. A nonparametric smoothing estimator of the conditional quantile curve requires to balance between local curvature and stochastic variability. In this paper, we suggest a local model selection technique that provides an adaptive estimator of the conditional quantile regression curve at each design point. Theoretical results claim that the proposed adaptive procedure performs as good as an oracle which would minimize the local estimation risk for the problem at hand. We illustrate the performance of the procedure by an extensive simulation study and consider a couple of applications: to tail dependence analysis for the Hong Kong stock market and to analysis of the distributions of the risk factors of temperature dynamics.


  • Wednesday, 5th September, 2012

    Title: Basis Selection from Multiple Libraries
    Speaker: Prof. Yuedong Wang, Department of Statistics and Applied Probability, University of California, Santa Barbara, USA
    Time/Place: 11:00  -  12:00
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: We propose a new method for estimating complex functions by linear combinations of basis functions selected adaptively from different classes of basis functions called libraries. Libraries are chosen to model various features of a function such as change points and oscillations. Data-driven estimates of model complexities based on the generalized degrees of freedom or covariance penalty are used to correct bias incurred by adaptive model selection. The proposed method is general in the sense that it can be applied to any generic libraries including spline and wavelet bases. Simulations and real data sets will be used for illustration.


  • Friday, 7th September, 2012

    Title: Spectral Galerkin Method for Space-Time fractional diffusion equation
    Speaker: Mr. CHEUNG Ka Chun, Department of Mathematics, Hong Kong Baptist University, Hong Kong
    Time/Place: 11:00  -  12:00
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: In this talk, we investigate the initial-boundary value problem of diffusion equation with Caputo fractional derivative in time and Riemann-Liouville derivative in space. Well-posedness of the weak formulation will be provided and the weak solution is obtained from spectral Galerkin method. In the weak formulation, integrals were computed by Gauss-Legendre quadrature and Gauss-Jacobi quadrature. Numerical solution will be provided.