Confidence interval estimation for the difference of censored zero-inflated gamma distributions

This paper studies the problem of constructing confidence intervals (CIs) for the difference between coefficients of variation (CV) of two censored zero-inflated gamma distributions. Firstly, we propose a Fiducial inference based method that extends the traditional CI construction method of gamma distribution to the scenario of zero-inflated gamma distributions with censored data. Secondly, we propose a Box-Cox transformation based method, in which not only the sample data needs to be transformed, but also the detection limit of gamma distribution with censored data accordingly. Thirdly, we investigate the Method of Variance Estimate Recovery (MOVER), to combine the previous two gamma distribution CI estimates with three binomial distribution CI estimates, and obtain six combination methods. Furthermore, we conduct Monte Carlo simulations to evaluate the performances of the proposed methods, the results indicate that all CI construction methods achieve satisfactory performances in terms of coverage probability, average length and tail error rates. Finally, we perform real data analysis using 11 years of precipitation data from Zhengzhou and Lhasa.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2024-11-27
DOI
https://doi.org/10.1038/s41598-024-79706-3
Citations
1
Primary Topic
Statistical Methods and Inference
Type
article
Field-Weighted Citation Impact
0.71

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Confidence interval estimation for the difference of censored zero-inflated gamma distributions

Min Wu, Yuhang Qian, Hongping Guo, Yiran Zhu
1 citations
Scientific Reports
Statistical Methods and Inference
0.71
article

Confidence interval estimation for the difference of censored zero-inflated gamma distributions

Min Wu, Yuhang Qian, Hongping Guo, Yiran Zhu
article en
1 citations

Abstract

This paper studies the problem of constructing confidence intervals (CIs) for the difference between coefficients of variation (CV) of two censored zero-inflated gamma distributions. Firstly, we propose a Fiducial inference based method that extends the traditional CI construction method of gamma distribution to the scenario of zero-inflated gamma distributions with censored data. Secondly, we propose a Box-Cox transformation based method, in which not only the sample data needs to be transformed, but also the detection limit of gamma distribution with censored data accordingly. Thirdly, we investigate the Method of Variance Estimate Recovery (MOVER), to combine the previous two gamma distribution CI estimates with three binomial distribution CI estimates, and obtain six combination methods. Furthermore, we conduct Monte Carlo simulations to evaluate the performances of the proposed methods, the results indicate that all CI construction methods achieve satisfactory performances in terms of coverage probability, average length and tail error rates. Finally, we perform real data analysis using 11 years of precipitation data from Zhengzhou and Lhasa.

Scientific ReportsVol. 14(1)
Qingdao University (CN), Nanjing Audit University (CN), Hubei University of Science and Technology (CN), Hubei Normal University (CN)
National Natural Science Foundation of China, Qingdao University
Reduced inequalities
Openalex Percentile: Top 24%
Statistical Methods and Inference
0.71
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Confidence interval estimation for the difference of censored zero-inflated gamma distributions — Min Wu, Yuhang Qian, et al. · Scientific Reports (2024) | TGRS Research Map | TGRS