Exact likelihood inference for Laplace distribution based on generalized hybrid censored samples

Xiaojun Zhu*, Narayanaswamy Balakrishnan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


In this paper, we first develop exact likelihood inference for Laplace distribution based on a generalized Type-I hybrid censored sample (Type-I HCS). We derive explicit expressions for the maximum likelihood estimators (MLEs) of the location and scale parameters. We then derive the joint moment generating function (MGF) of the MLEs, and use it to obtain the exact distributions and moments of the MLEs. Using an analogous approach, we extend the results to a generalized Type-II hybrid censored sample (Type-II HCS) next. Finally, we present a numerical example to illustrate all the results established here.

Original languageEnglish
Pages (from-to)259-272
Number of pages14
JournalCommunications in Statistics: Simulation and Computation
Issue number1
Publication statusPublished - 2024


  • Exact inference
  • Laplace distribution
  • generalized Type-I hybrid censoring
  • generalized Type-II hybrid censoring
  • maximum likelihood estimation

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