Journal of Biometrics & Biostatistics

Ying Zhang

Publications

• Research Article
  Bias in Estimation of a Mixture of Normal Distributions
  Author(s): Spencer Lourens, Ying Zhang, Jeffrey D Long and Jane S PaulsenSpencer Lourens, Ying Zhang, Jeffrey D Long and Jane S Paulsen
  
  Estimating parameters in a mixture of normal distributions dates back to the 19th century when Pearson originally considered data of crabs from the Bay of Naples. Since then, many real world applications of mixtures have led to various proposed methods for studying similar problems. Among them, maximum likelihood estimation (MLE) and the continuous empirical characteristic function (CECF) methods have drawn the most attention. However, the performance of these competing estimation methods has not been thoroughly studied in the literature and conclusions have not been consistent in published research. In this article, we review this classical problem with a focus on estimation bias. An extensive simulation study is conducted to compare the estimation bias between the MLE and CECF methods over a wide range of disparity values. We use the overlapping coefficient (OVL) to measure the amou.. Read More»
  DOI: 10.4172/2155-6180.1000179

  Abstract PDF