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Exploratory Data Analysis for Possibly Censored Data from Skewed Distributions

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  • A.C. Kimber

Abstract

Some simple data analytic techniques are discussed in relation to skewed data, as might arise in reliability or survival studies, for example. Preliminary screening for outliers via outsider labelling rules is investigated and letter‐value‐based methods are adapted to cope with censored observations by means of the product limit estimator.

Suggested Citation

  • A.C. Kimber, 1990. "Exploratory Data Analysis for Possibly Censored Data from Skewed Distributions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 39(1), pages 21-30, March.
    • Handle: RePEc:bla:jorssc:v:39:y:1990:i:1:p:21-30
    DOI: 10.2307/2347808
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    Cited by:

    1. Wei Cui & Zai-zai Yan & Xiu-yun Peng & Gai-mei Zhang, 2022. "Reliability analysis of log-normal distribution with nonconstant parameters under constant-stress model," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 818-831, April.
    2. Schwertman, Neil C. & Owens, Margaret Ann & Adnan, Robiah, 2004. "A simple more general boxplot method for identifying outliers," Computational Statistics & Data Analysis, Elsevier, vol. 47(1), pages 165-174, August.
    3. Madaki Yusuf & Mohd Abu Bakar & Qasim Husain & Noor Ibrahim & Jayanthi Arasan, 2017. "A Bayesian via Laplace Approximation on Log-gamma Model with Censored Data," Modern Applied Science, Canadian Center of Science and Education, vol. 11(1), pages 1-14, September.
    4. Carling, Kenneth, 1998. "Resistant outlier rules and the non-Gaussian case," Working Paper Series 2001:7, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    5. Carling, Kenneth, 2000. "Resistant outlier rules and the non-Gaussian case," Computational Statistics & Data Analysis, Elsevier, vol. 33(3), pages 249-258, May.
    6. Gustavo Felipe Martin Nascimento & Frédéric Wurtz & Patrick Kuo-Peng & Benoit Delinchant & Nelson Jhoe Batistela, 2021. "Outlier Detection in Buildings’ Power Consumption Data Using Forecast Error," Energies, MDPI, vol. 14(24), pages 1-15, December.
    7. Hubert, M. & Vandervieren, E., 2008. "An adjusted boxplot for skewed distributions," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5186-5201, August.

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