Non/semiparametric methods for nonlinear/hazards/censored regression; Nonparametric monotone empirical Bayes; Non/semiparametric seemingly unrelated regression

用于非线性/危险/删失回归的非/半参数方法；

• 批准号: 4631-2012
• 负责人: Singh, Radhey
• 金额: $ 0.87万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=568364
• 关键词: Non; semiparametric; methods; nonlinear; hazards

When the response is the time-to-event outcome such as lifetimes of patients, data are often clustered and censored. For example, in a clinical trial, researchers are interested in evaluating the effect of a treatment on survival in the HIV-1 seropositive drug users adjusted for other predictive covariates such as BMI (body mess index) and age. Some patients may still be alive when the study terminates. Hence, the survival times of these patients are censored. On the other hand, when patients are taken from different health centers; there might be a dependence between patients in the same center. Part of the objective of this research proposal is to develop semi-parametric regression models for fixed and mixed covariate effects with censored and clustered samples and to investigate the large sample performance of the estimators. In many fields, like medicine, industry, engineering and biological sciences, often situations involving sequences of similar but independent investigations arise. In such situations the parameter of interest often varies unpredictably as the sequence progresses with unknown probability distribution, and hence a minimum risk decision, what is usually called Baysian decision, cannot be made. However, the information collected from the previous investigations can sometimes be utilized to formulate a decision, what is popularly known as empirical Bayes decision, with risk close to the minimum Bayes risk. Part of the objective of this research is to propose improved monotone EB procedures with risks close to the minimum Bayes risk. In almost every discipline a response data depend on several causal covariates, and one is often faced with the problem of modeling the response data on covariates for forecasting/ prediction purpose. Part of the objectives of this research proposal is to extend the research on modeling problem to the situations where we deal with two or more systems of responses which depend on two or more sets of covariates and utilize all the information to provide best fit to responses. This known as seemingly unrelated regressions method is useful in economics, social, biological, epidemiology, engineering and reliability sciences.

当响应是至事件发生时间结局（如患者的寿命）时，数据通常会进行聚类和删失。例如，在一项临床试验中，研究人员感兴趣的是评估治疗对HIV-1血清阳性吸毒者生存率的影响，并调整其他预测协变量，如BMI（身体质量指数）和年龄。一些患者在研究终止时可能仍然存活。因此，对这些患者的生存时间进行删失。另一方面，当患者来自不同的健康中心时，同一中心的患者之间可能存在依赖性。本研究提案的部分目标是开发半参数回归模型，用于删失和聚类样本的固定和混合协变量效应，并研究估计量的大样本性能。 在许多领域，如医学，工业，工程和生物科学，经常出现涉及类似但独立的调查序列的情况。在这种情况下，随着序列以未知的概率分布前进，感兴趣的参数经常不可预测地变化，因此不能做出通常称为贝叶斯决策的最小风险决策。然而，从以前的调查中收集的信息有时可以用来制定一个决策，通常称为经验贝叶斯决策，风险接近最小贝叶斯风险。本研究的部分目的是提出改进的单调EB程序的风险接近最小贝叶斯风险。 在几乎所有的学科中，响应数据都依赖于多个因果协变量，并且人们经常面临着为预测/预测目的对协变量的响应数据建模的问题。本研究提案的部分目标是将建模问题的研究扩展到我们处理两个或多个依赖于两组或多组协变量的响应系统的情况，并利用所有信息提供最佳拟合响应。这种被称为看似无关回归的方法在经济学、社会学、生物学、流行病学、工程学和可靠性科学中很有用。