.png){kind=icon}
返回
Alternative formulae for parameter estimation in partial errors-in-variables models
.png){kind=icon}
PRE
AI
DOI：10.1007/s00190-014-0756-2.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The purpose of this note is to derive alternative formulae for parameter estimation in partial errors-in-variables models to those given in Xu et al. (J Geod 86:661-675, 2012). We show that these new formulae are more compact and more straightforward to interpret the corrections of the random elements of the design matrix. Nevertheless, each formula has its own computational advantage, depending on the relationship between the number of measurements y and that of the independent random elements of the design matrix. The original formula is computationally more efficient, if the number of measurements y is significantly larger than that of the independent random elements of the design matrix; otherwise, the new alternative formula is much more efficient.
Keyword:
Errors-in-variables
Partial EIV model
Structured EIV model
Total least squares
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4
论文数:
2.5K
被引数:
7.6K
机构
引用论文
.png){kind=icon}
Total least squares adjustment in partial errors-in-variables models: algorithm and statistical analysis变量部分误差模型中的总最小二乘调整: 算法和统计分析
PREAI
分享收藏分享收藏分享收藏
查看更多内容