Optimal estimation of the length-biased inverse Gaussian mean with a case study on Eastern Tropical Pacific dolphins

IF 3 4区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES
Sudeep R. Bapat, Neeraj Joshi
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引用次数: 0

Abstract

This paper deals with estimating the underlying parameter of a length-biased inverse Gaussian distribution, when the observations are prone to length-biased sampling. Length-biased sampling occurs when the observations of smaller lengths or dimensions are neglected from the sample. We focus on a particular type of sequential fixed-accuracy confidence interval for estimation purposes. This method proves to be both time and cost efficient as one is able to perform the estimation using an optimal number of observations according to some set criteria. We discuss the applicability of our proposed method with regards to estimating the cluster size of the "Eastern Tropical Pacific Dolphins", which are often vulnerable to length biasedness.

Abstract Image

长度偏置反高斯平均值的最优估算与东太平洋热带海豚案例研究
本文讨论的是当观测数据容易受到长度偏置采样的影响时,如何估计长度偏置反向高斯分布的基本参数。当较小长度或尺寸的观测值在样本中被忽略时,就会出现长度偏置抽样。我们将重点放在一种特定类型的序列固定精度置信区间的估计上。事实证明,这种方法既省时又省钱,因为我们可以根据某些设定的标准,使用最佳观测值的数量来进行估计。我们讨论了我们提出的方法在估算 "东热带太平洋海豚 "集群规模方面的适用性,因为这种集群规模往往容易受到长度偏差的影响。
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来源期刊
Environmental and Ecological Statistics
Environmental and Ecological Statistics 环境科学-环境科学
CiteScore
5.90
自引率
2.60%
发文量
27
审稿时长
>36 weeks
期刊介绍: Environmental and Ecological Statistics publishes papers on practical applications of statistics and related quantitative methods to environmental science addressing contemporary issues. Emphasis is on applied mathematical statistics, statistical methodology, and data interpretation and improvement for future use, with a view to advance statistics for environment, ecology and environmental health, and to advance environmental theory and practice using valid statistics. Besides clarity of exposition, a single most important criterion for publication is the appropriateness of the statistical method to the particular environmental problem. The Journal covers all aspects of the collection, analysis, presentation and interpretation of environmental data for research, policy and regulation. The Journal is cross-disciplinary within the context of contemporary environmental issues and the associated statistical tools, concepts and methods. The Journal broadly covers theory and methods, case studies and applications, environmental change and statistical ecology, environmental health statistics and stochastics, and related areas. Special features include invited discussion papers; research communications; technical notes and consultation corner; mini-reviews; letters to the Editor; news, views and announcements; hardware and software reviews; data management etc.
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