Amit Singh Nayal

About

I'm a Statistician at AIIMS New Delhi with a PhD in Statistics. My expertise includes Statistical Inference, Biostatistics, Inferential Reliability Analysis, Data Mining, Multivariate Analysis, and Data Visualization. I specialize in developing and implementing research tools, with a strong focus on generating innovative ideas and devising practical solutions to complex, real-world problems.

Employment

• All India Institute of Medical Sciences Statistician
  2024 - Present
• Chaudhary Charan Singh University Guest Faculty
  2021 - 2024

Education

• Chaudhary Charan Singh University Ph.D.
  2021 - 2024
• Chaudhary Charan Singh University M.Sc.
  2017 - 2019

Projects & Funding

Projects & funding information is unavailable.

Publications (13)

• Reliability Analysis for a Mixed Poisson‐Lindley Stress‐Strength Model
  Abhishek Tyagi, Kartik Waliya, Alka Chaudhary

  +1 more

  • Amit Singh Nayal
  Quality and Reliability Engineering International 2026 DOI: 10.1002/qre.70274
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• Improving inference in exponential logarithmic distribution
  Amit Singh Nayal, Pedro Luiz Ramos, Abhishek Tyagi +1 more
  Communications in Statistics - Simulation and Computation 2026 DOI: 10.1080/03610918.2024.2446346
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• Green synthesized silver and zinc oxide nanoparticles with antifungal, DNA protection, DNA cleavage, and cytotoxic activities
  J M Singh, Ankit Kumar, Anupma Singh

  +2 more

  • Amit Singh Nayal
  • Dharmendra Pratap
  Scientific Reports 2026 DOI: 10.1038/s41598-026-45745-1
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• Comparative study of estimation methods for the Teissier distribution: performance evaluation and real-world applications
  Amit Singh Nayal
  International Journal of Modelling and Simulation 2026 DOI: 10.1080/02286203.2026.2622917
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• Molecular data reveal diversity of Tylodelphys spp. (Trematoda: Diplostomidae) in India: with evidence of new lineages, morphology and statistical analysis
  Komal Singh, Amit Singh Nayal, Haren Ram Chiary +6 more
  Molecular Biology Reports 2025 DOI: 10.1007/s11033-025-10456-9
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• Inferences on stress-strength reliability R=P[Y<X<Z] for progressively type-II censored Weibull half logistic distribution
  Amit Singh Nayal, Abhishek Tyagi, Shubhi Rathi

  +1 more

  • Bhupendra Kumar Singh
  OPSEARCH 2024 DOI: 10.1007/s12597-024-00810-z
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• Analyzing stress-strength reliability δ =P [U<V<W]: a Bayesian and frequentist perspective with Burr-XII distribution under progressive Type-II censoring
  Amit Singh Nayal, Bhupendra Kumar Singh, Vrijesh Tripathi

  +1 more

  • Abhishek Tyagi
  International Journal of System Assurance Engineering and Management 2024 DOI: 10.1007/s13198-024-02264-4
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• Comprehensive antifungal investigation of green synthesized silver nanoformulation against four agriculturally significant fungi and its cytotoxic applications
  Jyoti Singh, Ankit Kumar, Amit Singh Nayal

  +10 more

  • Sagar Vikal
  • Gyanika Shukla
  • Amardeep Singh
  • Anupma Singh
  • Sakshi Goswami
  • +5 more
  Scientific Reports 2024 DOI: 10.1038/s41598-024-56619-9
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• Analysing Random Censored Data from Discrete Teissier Model
  Reliability: Theory & Applications 2023 DOI: 10.24412/1932-2321-2023-172-403-411
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• Classical and Bayesian inferences on the stress-strength reliability R = P[Y<X<Z] in the geometric distribution setting
  Amit Singh Nayal, Bhupendra Kumar Singh, Abhishek Tyagi

  +1 more

  • Christophe Chesneau
  AIMS Mathematics 2023 DOI: 10.3934/math.20231054
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• Estimation of P[Y< Z] under Geometric-Lindley model
  Bhupendra Kumar Singh, Amit Singh Nayal, Abhishek Tyagi
  Ricerche di Matematica 2023 DOI: 10.1007/s11587-022-00753-x
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• Discrete Inverted Nadarajah-Haghighi Distribution: Properties and Classical Estimation with Application to Complete and Censored data
  Bhupendra Kumar Singh, Ravindra Pratap Singh, Amit Singh Nayal

  +1 more

  • Abhishek Tyagi
  Statistics, Optimization & Information Computing 2022 DOI: 10.19139/soic-2310-5070-1365
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• A Discrete Analogue of Teissier Distribution: Properties and Classical Estimation with Application to Count Data
  Reliability: Theory and Applications 2022 DOI: 10.24412/1932-2321-2022-167-340-355
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