A robust two-stage procedure for the Poisson process under the linear exponential loss function

Leng-Cheng Hwang

Statistics & Probability Letters, 2020, vol. 163, issue C

Abstract: Within the framework of Bayesian model, we consider the problem of sequentially estimating the intensity parameter of a homogeneous Poisson process with linear exponential (LINEX) loss function and fixed cost per unit time. Instead of fully sequential sampling, a two-stage sampling technique is introduced to solve the problem in this paper. The proposed two-stage procedure is robust in the sense that it does not depend on the prior. It is shown that the two-stage procedure shares the asymptotic properties with the asymptotically pointwise optimal procedures for a large class of the priors. A simulation study is conducted to compare the performances of the proposed two-stage procedure and the asymptotically pointwise optimal procedures.

Keywords: Asymptotically optimal; Asymptotically pointwise optimal; LINEX loss function; Robust; Two-stage procedure (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167715220300766
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:163:y:2020:i:c:s0167715220300766

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.spl.2020.108773

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().