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Zhiliang Ying

  1. Parameter Estimation using Empirical Likelihood combined with Market Information.

    Mon, 01/16/2012 - 15:01 — SomNambul
    Authors: Zhiliang Ying, Steven Kou, Tony Sit
    Subjects: Methodology
    Abstract

    During the last decade Levy processes with jumps have received increasing
    popularity for modelling market behaviour for both derviative pricing and risk
    management purposes. Chan et al. (2009) introduced the use of empirical
    likelihood methods to estimate the parameters of various diffusion processes
    via their characteristic functions which are readily avaiable in most cases.
    Return series from the market are used for estimation.

  2. An Empirical Likelihood Approach to Nonparametric Covariate Adjustment in Randomized Clinical Trials.

    Wed, 08/03/2011 - 15:01 — SomNambul
    Authors: Zhiliang Ying, Xiaoru Wu
    Subjects: Methodology
    Abstract

    Covariate adjustment is an important tool in the analysis of randomized
    clinical trials and observational studies. It can be used to increase
    efficiency and thus power, and to reduce possible bias. While most statistical
    tests in randomized clinical trials are nonparametric in nature, approaches for
    covariate adjustment typically rely on specific regression models, such as the
    linear model for a continuous outcome, the logistic regression model for a
    dichotomous outcome and the Cox model for survival time. Several recent efforts
    have focused on model-free covariate adjustment.

  3. Rapid Learning with Stochastic Focus of Attention.

    Tue, 05/03/2011 - 15:01 — SomNambul
    Authors: Zhiliang Ying, Raphael Pelossof
    Subjects: Learning
    Abstract

    We present a method to stop the evaluation of a decision making process when
    the result of the full evaluation is obvious. This trait is highly desirable
    for online margin-based machine learning algorithms where a classifier
    traditionally evaluates all the features for every example. We observe that
    some examples are easier to classify than others, a phenomenon which is
    characterized by the event when most of the features agree on the class of an
    example.

  4. Theory of Self-learning Q-Matrix.

    Mon, 11/01/2010 - 16:01 — SomNambul
    Authors: Jingchen Liu, Gongjun Xu, Zhiliang Ying
    Subjects: Statistics
    Abstract

    Cognitive assessment is a growing area in psychological and educational
    measurement, where tests are given to assess mastery/deficiency of attributes
    or skills. A key issue is the correct identification of attributes associated
    with items in a test. In this paper, we set up a mathematical framework under
    which theoretical properties may be discussed. We establish sufficient
    conditions to ensure that the attributes required by each item are learnable
    from the data.

Math-arch

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