Fractionally Supervised Classification with Maxima Nominated Samples
Fractionally supervised classification (FSC) offers a flexible framework for combining labeled and unlabeled data in model-based classification, but existing formulations assume simple random sampling. In many applications, however, the retained observation is an extreme order statistic from a set rather than a randomly selected unit. This is particularly appealing when the target population is rare, since maxima nomination sampling (NS) can enrich the sample with the most informative observations, as in screening, environmental monitoring, repeated testing, and reliability studies. Under such designs, the likelihood function changes fundamentally, and the usual FSC EM construction is no ...
| Authors | Mohammad Jafari Jozani, Jingyu Wang |
|---|---|
| Primary category | stat.ME |
| Published | 28 April 2026 |
| Last updated at source | 28 April 2026 |
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