Asymmetric kernel and positive wavelet smoothing for size-biased data

针对大小偏差数据的非对称核和正小波平滑

• 批准号: 3661-2011
• 负责人: Chaubey, Yogendra
• 金额: $ 0.95万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=592763
• 关键词: Asymmetric; kernel; positive; wavelet; smoothing

I have developed through previous research grants some appropriate smoothing methods dealing with survival data. These methods have been useful for estimating the survival probability beyond the largest observation in the sample as well as other important characteristics of the population such as the expected life time conditional on a given age. Traditional methods such as the kernel methods require additional corrective measures to ward of the boundary bias that is avoided in the new smoothing techniques. The basic objective of the present proposal is to investigate extensions and modifications of such techniques to non-standard data such as weighted data, censored data and dependent data. These extensions are useful in practical settings of many applied fields such as reliability and engineering, clinical studies as well as agricultural studies.

我已经通过以前的研究赠款开发了一些适当的平滑方法处理生存数据。这些方法对于估计样本中最大观测值以外的生存概率以及人口的其他重要特征（如以给定年龄为条件的预期寿命）非常有用。传统的方法，如核方法需要额外的校正措施，以避免边界偏差，在新的平滑技术。本提案的基本目标是研究将这种技术扩展和修改到非标准数据，如加权数据、删失数据和相关数据。这些扩展在许多应用领域的实际环境中是有用的，如可靠性和工程，临床研究以及农业研究。