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Confidence Intervals for Mean of Delta Two-Parameter Exponential Distribution

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Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13199))

Abstract

The two-parameter exponential distribution is widely used for many applications in real life, and the data can include zero observations. The mean, which represents the center of a population, is one of the parameters of interest. Herein, we propose confidence intervals for the mean of a delta two-parameter exponential distribution based on parametric bootstrapping (PB), standard bootstrapping (SB), the generalized confidence interval (GCI), and the method of variance estimates recovery (MOVER). The performances of the proposed confidence intervals were evaluated by using coverage probabilities and average lengths via Monte Carlo simulations. The results indicate that GCI can be recommended for small-to-moderate sample sizes whereas PB is appropriate for large sample sizes.

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Authors and Affiliations

  1. Faculty of Applied Science, Department of Applied Statistics, King Mongkut’s University of Technology North Bangkok, Bangkok, 10800, Thailand

    Wansiri Khooriphan, Sa-Aat Niwitpong & Suparat Niwitpong

Authors
  1. Wansiri Khooriphan
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  2. Sa-Aat Niwitpong
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  3. Suparat Niwitpong
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Corresponding author

Correspondence to Sa-Aat Niwitpong .

Editor information

Editors and Affiliations

  1. Osaka Prefecture University, Sakai, Osaka, Japan

    Katsuhiro Honda

  2. University of Hyogo, Kobe, Japan

    Tomoe Entani

  3. Osaka Prefecture University, Sakai, Japan

    Seiki Ubukata

  4. Japan Advanced Institute of Science and Technology, Nomi, Japan

    Van-Nam Huynh

  5. Osaka University, Toyonaka, Osaka, Japan

    Masahiro Inuiguchi

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Khooriphan, W., Niwitpong, SA., Niwitpong, S. (2022). Confidence Intervals for Mean of Delta Two-Parameter Exponential Distribution. In: Honda, K., Entani, T., Ubukata, S., Huynh, VN., Inuiguchi, M. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2022. Lecture Notes in Computer Science(), vol 13199. Springer, Cham. https://doi.org/10.1007/978-3-030-98018-4_10

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  • DOI: https://doi.org/10.1007/978-3-030-98018-4_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-98017-7

  • Online ISBN: 978-3-030-98018-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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