.png){kind=icon}
.png){kind=icon}
Return
Cherry-picking for complex data: robust structure discovery
.png){kind=icon}
PRE
AI
DOI：10.1098/rsta.2009.0119.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Complex data often arise as a superposition of data generated from several simpler models. The traditional strategy for such cases is to use mixture modelling, but it can be problematic, especially in higher dimensions. This paper considers an alternative approach, emphasizing data exploration and robustness to model misspecification. The strategy is applied to problems in regression, cluster analysis and multidimensional scaling. The approach is illustrated through simulation and the analysis of several datasets.
Keywords:
cluster analysis
elemental sets
mixture models
multidimensional scaling
regression
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
P
IF:
3.7
Papers:
7.7K
Citations:
2.8W