超越单峰回归：用分段单峰回归或去卷积模型建模多模态
Beyond unimodal regression: modelling multimodality with piecewise unimodal regression or deconvolution models
摘要：
形状约束使我们能够在回归设置中反映先验知识。例如，单峰约束可以描述强度先增大然后减小的常见情况。然而，数据形状通常表现出多种模式。因此，我们超越了单峰回归，并提出用分段单峰回归或基于单峰峰形的反卷积模型来建模多峰。单峰回归及其多峰扩展的可靠性在三个应用领域内得到了证明：海洋生物学、天体粒子物理学和呼吸气体分析。尽管存在这种多样性，但在每个应用中都获得了有价值的结果。这也鼓励在其他领域使用这些方法。
英文摘要：
Shape constraints enable us to reflect prior knowledge in regression settings. A unimodality constraint, for example, can describe the frequent case of a first increasing and then decreasing intensity. Yet, data shapes often exhibit multiple modes. Therefore, we go beyond unimodal regression and propose modelling multimodality with piecewise unimodal regression or with deconvolution models based on unimodal peak shapes. Usefulness of unimodal regression and its multimodal extensions is demonstrated within three applications areas: marine biology, astroparticle physics and breath gas analysis. Despite this diversity, valuable results are obtained in each application. This encourages the use of these methods in other areas as well.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
