Journal article
Bias in Estimation of a Mixture of Normal Distributions
Journal of biometrics & biostatistics, Vol.4(5), 1000179
01/01/2013
DOI: 10.4172/2155-6180.1000179
PMCID: PMC4257062
PMID: 25485171
Abstract
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 amount of disparity, and provide a practical guideline for estimation quality in mixtures of normal distributions. Application to an ongoing multi-site Huntington disease study is illustrated for ascertaining cognitive biomarkers of disease progression.
Details
- Title: Subtitle
- Bias in Estimation of a Mixture of Normal Distributions
- Creators
- Spencer Lourens - University of IowaYing Zhang - University of IowaJeffrey D Long - University of IowaJane S Paulsen - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Journal of biometrics & biostatistics, Vol.4(5), 1000179
- DOI
- 10.4172/2155-6180.1000179
- PMID
- 25485171
- PMCID
- PMC4257062
- ISSN
- 2155-6180
- eISSN
- 2155-6180
- Language
- English
- Date published
- 01/01/2013
- Academic Unit
- Psychiatry; Psychological and Brain Sciences; Biostatistics
- Record Identifier
- 9984281657102771
Metrics
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