Adaptive Methods for Nonparametric Classification and Regression/Supervised Learning, Inference in HMM and State Space Models and Inference in Semiparametric Models

在非参数和半参数推理中，Bickel与Ritov等人合作，建议研究如何使用不断增加但按比例减少的交叉验证样本来系统地优化监督学习(分类和回归)过程，例如ADA Boost。此外，他们还建议研究半参数假设检验的统一理论，并开发有效的生物等效性检验。在相依数据模型中，他们建议将以前关于隐马尔可夫模型的结果扩展到状态空间模型，并研究通过拟合和使用粒子过滤器等近似似然得到的过程的行为；研究人员和合作者建议分析和开发新的有效方法来识别(通过机器)新感知的对象的类型或从历史信息预测某些特征。从机器读取手写邮政编码到预测汽车从一个目的地到另一个目的地的旅行时间，再到从微阵列数据预测肿瘤类型，这一领域的研究范围很广。在类似的方向上，他们建议看看基于计算机模拟的理想预测方法在适用于语音识别等情况的非常复杂的模型中工作得如何。此外，他们还建议研究推论方法，以确定一种可能更昂贵和有副作用的新药是否比目前使用的药物足够好，可以授权销售。

In non and semiparametric inference Bickel, in collaboration with Ritov and others, proposes to study how an increasing but proportionally vanishingly small cross validation sample can be used systematically to optimize supervised learning (classification and regression) procedures, for example ADA BOOST. Further they propose to study a unified theory for testing of semiparametric hypotheses and develop efficient tests for bioequivalence. In dependent data models, they propose to extend previous results on Hidden Markov models to state space models and study how procedures obtained by fitting and use of approximate likelihoods such as particle filters behave.The investigator and collaborators propose to analyze and develop new effective methods for identifying (by machine) the type of a newly perceived object or predicting some feature from historical information. This ranges from machine reading of hand written zip codes to predicting travel times of cars from one destination to another to predicting tumor type from microarray data. In a similar direction they propose to see how well computer simulation based approximations to ideal prediction methods work in very complicated models applying to situations such as voice recognition. Further they propose to study methods of inference bearing on questions such as whether a new drug which may be more expensive and have side-effects is sufficiently better than drugs currently in use to be authorized for distribution.