CAREER: Nonparametric methods in multiple dimensions: shape restrictions, bootstrap and beyond

职业：多维非参数方法：形状限制、引导程序等

• 批准号: 1150435
• 负责人: Bodhisattva Sen
• 金额: $ 40万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1150435&HistoricalAwards=false
• 关键词: CAREER; Nonparametric; methods; multiple; dimensions

This proposal deals with some problems on estimation and inference using nonparametric methods. Nonparametric procedures have become increasingly popular in the theory and practice of statistics in recent times primarily because of their fundamental advantages over parametric methods: greater flexibility and more 'data-driven' features. In this proposal, the investigator studies three core directions of statistical research in this area: (A) Nonparametric function estimation under shape restrictions, (B) Dimension reduction using semi/non-parametric techniques, and (C) Bootstrap based inference in non-standard problems. The main motivation for this research is in developing nonparametric procedures that are completely automated (free from tuning parameters, e.g., smoothing bandwidths) but still flexible enough to incorporate data-driven features. A major part of this proposal deals with nonparametric methods applicable to multivariate data, an area that has received relatively less attention, though often felt to be necessary in performing real data analysis.With the advancement in modern computing facilities and the explosion in collection of large scale data sets, the use of more complicated/intricate statistical procedures involving numerical optimization techniques are becoming increasingly popular. However, a complete theoretical analysis of most of these procedures is still largely unavailable. This research aims at understanding the theoretical and computational aspects of some of these statistical procedures, and quantifying the uncertainties involved in such stochastic optimization problems. The intended applications of the proposed research are diverse, ranging from detecting the advent of global warming, to estimating the radial velocity distribution of stars in a galaxy, to developing inferential techniques for binary choice models (of special interest to econometricians), and would involve collaborations at different levels with statisticians, biostatisticians, epidemiologists, econometricians and astronomers.

本文讨论了用非参数方法进行估计和推断的一些问题。近年来，非参数方法在统计学的理论和实践中越来越受欢迎，主要是因为它们比参数方法具有更大的灵活性和更多的“数据驱动”功能。在这个建议中，研究者研究了这一领域统计研究的三个核心方向：（A）形状限制下的非参数函数估计，（B）使用半/非参数技术的降维，以及（C）非标准问题中基于Bootstrap的推断。这项研究的主要动机是开发完全自动化的非参数程序（无需调整参数，例如，平滑带宽），但仍然足够灵活以合并数据驱动特征。这个建议的一个主要部分涉及适用于多变量数据的非参数方法，一个领域，已收到相对较少的关注，但往往被认为是必要的，在执行真实的数据analysis.With在现代计算设备的进步和爆炸收集的大规模数据集，使用更复杂/复杂的统计过程，涉及数值优化技术变得越来越流行。然而，一个完整的理论分析大多数这些程序仍然是在很大程度上不可用。本研究旨在了解其中一些统计过程的理论和计算方面，并量化此类随机优化问题中涉及的不确定性。拟议研究的预期应用是多种多样的，从探测全球变暖的到来，到估计星系中恒星的径向速度分布，到开发二元选择模型的推理技术（计量经济学家特别感兴趣），并将涉及与统计学家、生物统计学家、流行病学家、计量经济学家和天文学家在不同级别的合作。