Abstract
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 language | English |
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Pages (from-to) | 259-272 |
Number of pages | 14 |
Journal | Communications in Statistics: Simulation and Computation |
Volume | 53 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2024 |
Keywords
- Exact inference
- Laplace distribution
- generalized Type-I hybrid censoring
- generalized Type-II hybrid censoring
- maximum likelihood estimation
Cite this
Zhu, X., & Balakrishnan, N. (2024). Exact likelihood inference for Laplace distribution based on generalized hybrid censored samples. Communications in Statistics: Simulation and Computation, 53(1), 259-272. https://doi.org/10.1080/03610918.2021.2018458