Local Influence Diagnostics of Replicated Data with Measurement Errors

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Volume 13, Issue 1

Jingjing Lu, Hairong Li and Chunzheng Cao

J. Info. Comput. Sci. , 13 (2018), pp. 074-080.

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Abstract

Replicated data with measurement errors frequently exist in various scientific fields. In this work, we propose a replicated measurement error model for such data under scale mixtures of normal distributions. We consider local influence diagnostics to detect and classify outliers in the data through different perturbation schemes. A simulation study and an application confirm the effectiveness and robustness of the diagnostic method.

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@Article{JICS-13-074, author = {Jingjing Lu, Hairong Li and Chunzheng Cao}, title = {Local Influence Diagnostics of Replicated Data with Measurement Errors}, journal = {Journal of Information and Computing Science}, year = {2018}, volume = {13}, number = {1}, pages = {074--080}, abstract = {Replicated data with measurement errors frequently exist in various scientific fields. In this work, we propose a replicated measurement error model for such data under scale mixtures of normal distributions. We consider local influence diagnostics to detect and classify outliers in the data through different perturbation schemes. A simulation study and an application confirm the effectiveness and robustness of the diagnostic method. }, issn = {3080-180X}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jics/22465.html} }

TY - JOUR T1 - Local Influence Diagnostics of Replicated Data with Measurement Errors AU - Jingjing Lu, Hairong Li and Chunzheng Cao JO - Journal of Information and Computing Science VL - 1 SP - 074 EP - 080 PY - 2018 DA - 2018/03 SN - 13 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jics/22465.html KW - AB - Replicated data with measurement errors frequently exist in various scientific fields. In this work, we propose a replicated measurement error model for such data under scale mixtures of normal distributions. We consider local influence diagnostics to detect and classify outliers in the data through different perturbation schemes. A simulation study and an application confirm the effectiveness and robustness of the diagnostic method.

Jingjing Lu, Hairong Li and Chunzheng Cao. (2018). Local Influence Diagnostics of Replicated Data with Measurement Errors. Journal of Information and Computing Science. 13 (1). 074-080. doi:

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