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Event(s) on May 2016


  • Thursday, 19th May, 2016

    Title: Inverse scattering problems without the phase information
    Speaker: Prof. KLIBANOV Michael, Department of Mathematics, University of North Carolina, USA
    Time/Place: 16:00  -  17:00
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: In this talk, the speaker shall discuss his recent breakthroughs on recovering inhomogeneous mediums by using phaseless scattering measurements.


  • Friday, 20th May, 2016

    Title: Optimally estimating the sample mean from the sample size, median, mid-range and/or mid-quartile range
    Speaker: Ms. LUO Dehui, Department of Mathematics, Hong Kong Baptist University, Hong Kong
    Time/Place: 15:00  -  16:00
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: Recently in medical research area, evidence-based medicine is attracting increasing attention to improve decision making in medical practice via integrating evidence from well designed and conducted clinical research. Meta-analysis is a statistical technique widely used in evidence-based medicine for analytically combining the findings from independent clinical trials to provide an overall estimation of a treatment effectiveness. The sample mean and standard deviation are two commonly used statistics in meta-analysis but some trials use the median, the minimum and maximum values, or sometimes the first and third quartiles to report the results. Thus, to pool results in a consistent format, researchers need to transform those information back to the sample mean and standard deviation. Our work is to investigate the optimal estimation of the sample mean for meta-analysis from both theoretical and empirical perspectives. However, in the literature, a major drawback is that the sample size is either ignored or used in a stepwise but somewhat arbitrary manner, e.g., the famous method proposed by Hozo et al.(2005) . We solve this issue by incorporating the sample size in a smoothly changing weight in the estimators to reach the optimal estimation. Our proposed estimators not only improve the existing ones significantly but also share the same virtue of the simplicity. Additionally, by achieving the improvement of the sample mean estimator, we also discover the next task of developing a pre-test for symmetry to help researchers select appropriate data to conduct meta-analysis.


  • Friday, 27th May, 2016

    Title: A Statistical Framework for Single-Case Design with an Application in Post-stroke Rehabilitation
    Speaker: Dr. LU Ying, Department of Humanities and Social Science in the Professions, New York University, USA
    Time/Place: 10:00  -  11:00
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: This paper proposes a practical yet novel solution to a longstanding statistical testing problem regarding single subject design. In particular, we aim to resolve an important clinical question: does a new patient behave the same as one from a healthy population? This question cannot be answered using the traditional single subject design when only test subject information is used, nor can it be satisfactorily resolved by comparing a single-subject’s data with the mean value of a healthy population without proper assessment of the impact of between and within subject variability. Here, we use Bayesian posterior predictive draws based on a training set of healthy subjects to generate a template null distribution of the statistic of interest to test whether the test subject belongs to the healthy population. This method also provides an estimate of the error rate associated with the decision and provides a confidence interval for the point estimate of interest. Taken together, this information will enable clinicians to conduct evidence-based clinical decision making by directly comparing the observed measures with a pre-calculated null distribution for such measures. Simulation studies show that the proposed test performs satisfactorily under controlled conditions.