Next Article in Journal
A Note on the Minimum Size of a Point Set Containing Three Nonintersecting Empty Convex Polygons
Previous Article in Journal
Multi-Granulation Graded Rough Intuitionistic Fuzzy Sets Models Based on Dominance Relation
Open AccessArticle

Statistical Inference for the Information Entropy of the Log-Logistic Distribution under Progressive Type-I Interval Censoring Schemes

Department of Mathematics, Beijing Jiaotong University, Beijing 100044, China
*
Author to whom correspondence should be addressed.
Symmetry 2018, 10(10), 445; https://doi.org/10.3390/sym10100445
Received: 9 September 2018 / Revised: 21 September 2018 / Accepted: 25 September 2018 / Published: 28 September 2018
In recent years, information entropy has been studied and developed rapidly across disciplines as a measure of information value. In this article, the maximum likelihood estimation and EM algorithm are used to estimate the parameters of the log-logistic distribution for progressive type-I interval censored data, and the hypothesis testing algorithm of information entropy is proposed. Finally, Monte Carlo numerical simulations are conducted to justify the feasibility of the algorithm. View Full-Text
Keywords: information entropy; progressive type-I interval censoring; log-logistic distribution; EM algorithm; hypothesis testing information entropy; progressive type-I interval censoring; log-logistic distribution; EM algorithm; hypothesis testing
Show Figures

Figure 1

MDPI and ACS Style

Du, Y.; Guo, Y.; Gui, W. Statistical Inference for the Information Entropy of the Log-Logistic Distribution under Progressive Type-I Interval Censoring Schemes. Symmetry 2018, 10, 445.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop