从长度分层样本中对鱼类生长估计的年龄测量错误进行解决
Nan Zheng1, Atefeh Kheirollahi1, Yildiz Yilmaz1
1Department of Mathematics and Statistics, Memorial University of Newfoundland, St. John's, NL A1C 5S7 Canada.
Biometrics
|April 22, 2024
概括
准确的鱼类生长模型对于渔业至关重要. 这项研究引入了一种新的方法来纠正年龄测量错误和长度分层年龄抽样,改善增长估计和不确定性评估.
科学领域:
- 渔业 科学 渔业 科学
- 量化生态学 量化生态学
- 统计建模 统计建模
背景情况:
- 鱼类生长模型对于渔业库存评估至关重要.
- 长度分层年龄采样 (LSAS) 是一种常见的,具有成本效益的方法,用于收集鱼类年龄长度数据.
- 年龄测量错误 (ME) 可以在增长估计中引入偏差.
研究的目的:
- 开发一种方法,在鱼类生长建模中同时考虑LSAS和年龄ME.
- 为鱼类生长模型提供准确的参数估计和不确定性测量.
- 通过模拟和现实世界渔业数据验证拟议的方法.
主要方法:
- 用于LSAS的经验比例概率和年龄ME的结构错误变量中的概率.
- 采用延续比率-逻辑模型来计算年龄分布,并采用离散方法来计算效率.
- 开发模型验证工具,包括不确定性测量和标准化余量.
主要成果:
- 忽视年龄ME导致了鱼类生长估计的重大偏差.
- 拟议的方法准确地估计了鱼类的生长和标准误差,不论年龄ME大小.
- 模拟研究和真实数据分析证实了新方法的有效性.
结论:
- 准确的鱼类生长建模需要考虑采样设计 (LSAS) 和数据错误 (ME).
- 开发的方法提供了一个强大的解决方案,以提高准确性和可靠性来估计鱼类生长参数.
- 该研究提供了用于渔业库存评估和管理的实际工具和验证方法.
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