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Inferential Aspects of Doubly Truncated Generalized Gaussian Distribution

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Soft Computing: Theories and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1053))

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Abstract

Analyzing the data sets of image and signal processing, SQC, speech recognition, biological and industrial experiments is interpreted using generalized Gaussian distribution (GGD). The finite range of the experimental data derived the doubly truncated GGD instead of GGD and explained the importance of the proposed distribution. The distributional properties using order statistics have been derived and applied the method of moments and method of maximum likelihood estimation (MLE) to estimate the parameters of the doubly truncated GGD (DTGGD). Particular values of the parameters presented the significance test using Chi-square goodness of fit.

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Authors and Affiliations

  1. Department of Mathematics and Scientific Computing, National Institute of Technology Hamirpur (HP), Hamirpur, India

    Talari Ganesh

  2. Department of Statistics, Sri Venkateswara University, Tirupati, Andhra Pradesh, India

    Anithakumari Kattamanchi

Authors
  1. Talari Ganesh
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  2. Anithakumari Kattamanchi
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Corresponding author

Correspondence to Talari Ganesh .

Editor information

Editors and Affiliations

  1. Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Millie Pant

  2. Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Rajasthan, Jaipur, Rajasthan, India

    Tarun K. Sharma

  3. Department of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India

    Om Prakash Verma

  4. Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India

    Rajesh Singla

  5. Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India

    Afzal Sikander

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Ganesh, T., Kattamanchi, A. (2020). Inferential Aspects of Doubly Truncated Generalized Gaussian Distribution. In: Pant, M., Sharma, T., Verma, O., Singla, R., Sikander, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1053. Springer, Singapore. https://doi.org/10.1007/978-981-15-0751-9_50

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