Resampling Methods for Survey Data with Extensions in other Contexts

具有其他上下文扩展的调查数据重采样方法

• 批准号: RGPIN-2017-06037
• 负责人: Mashreghi, Zeinab
• 金额: $ 1.02万
• 依托单位: University of Winnipeg
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=658961
• 关键词: Resampling; Methods; Survey; Data; Extensions

How accurate is a given statistic? This might be the first question that a researcher asks once a statistic is used to estimate a parameter of interest. Obtaining accuracy measures of a given statistic, such as the variance, is not always easy through analytical methods. That is why resampling methods, such as the bootstrap, have been widely used in the literature to estimate such measurements. In my research program, I intend to study the theoretical developments and practical applications of bootstrap methods in order to establish new ideas.******Statistics Canada provides researchers with access to data files containing columns of bootstrap weights. These weights account for sampling variability in the observations and can be easily used to compute the variance of estimators or construct confidence intervals. Unfortunately, life is rarely that simple and one important practical problem in statistical surveys is the presence of item non-response in most data files. Item non-response is usually compensated using imputation which fills the empty cells in the data file. Treating the imputed values as if they were observed values may lead to serious underestimation of the variance of point estimators since bootstrap methods for full response survey data take into account neither the variability due to item non-response, nor imputation. I plan to build bootstrap methods for imputed survey data assuming the cases of unequal response probabilities and complex survey designs. ******The bootstrap is widely applied in different statistical areas. The generalized bootstrap for estimating equations is applied to estimate the variance of model parameter estimates. Under this approach, we intend to find optimal bootstrap weights in the case of a semi-parametric regression model for autocorrelated time series of count data with applications in finance and epidemiology.******In another application, I intend to develop a bootstrap method for prevalent cohort survival data. A special case of such data is length-biased right censored data. Interest mostly stems from challenges that some Canadian statisticians were faced with while analyzing survival with dementia data collected as part of the Canadian Study of Health and Aging survey. The existing bootstrap methods for such survival data do not consider the extra available information in the left truncation distribution. Thus, such bootstrap methodologies are not efficient. I plan to develop an efficient bootstrap method tailored for such data. Studying jackknife resampling methods in such settings is also a part of my research. The jackknife methods are usually aim at reducing bias where the plug-in estimators are often biased due to right censoring and/or biased sampling. ******These projects will improve current statistical techniques and produce new practical approaches while training strong statisticians who will work in academia or industry in Canada.

给定的统计数据有多准确？这可能是研究人员在使用统计数据估计感兴趣的参数时问的第一个问题。通过分析方法获得给定统计量的准确性度量（例如方差）并不总是容易的。这就是为什么像自举法这样的再分配方法在文献中被广泛用于估计这样的测量值。在我的研究计划中，我打算研究bootstrap方法的理论发展和实际应用，以建立新的想法。加拿大统计局为研究人员提供了访问包含自助权重列的数据文件的权限。这些权重说明了观测值的抽样变异性，并且可以很容易地用于计算估计量的方差或构造置信区间。不幸的是，生活很少那么简单，统计调查中的一个重要实际问题是大多数数据文件中存在项目无应答。项目无应答通常通过填补数据文件中的空单元来补偿。将插补值视为观察值可能导致严重低估点估计量的方差，因为完全应答调查数据的自助法既不考虑项目无应答引起的变异性，也不考虑插补。我计划建立自举方法估算调查数据假设的情况下，不等的响应概率和复杂的调查设计。* *应用估计方程的广义自助法估计模型参数估计的方差。在这种方法下，我们打算在自相关计数数据时间序列的半参数回归模型的情况下找到最佳自举权重，并在金融和流行病学中应用。在另一个应用中，我打算开发一种用于流行队列生存数据的bootstrap方法。这种数据的一个特殊情况是长度偏右删失数据。兴趣主要源于一些加拿大统计学家在分析作为加拿大健康和老龄化研究调查的一部分收集的痴呆症数据时面临的挑战。现有的Bootstrap方法没有考虑左截断分布中的额外可用信息。因此，这样的引导方法不是有效的。我计划开发一种针对此类数据量身定制的高效自举方法。在这种情况下研究折刀式治疗方法也是我研究的一部分。刀切法的目的通常是减少偏差，其中插入式估计量经常由于右删失和/或有偏抽样而有偏。** 这些项目将改进目前的统计技术并产生新的实用方法，同时培训将在加拿大学术界或工业界工作的强有力的统计人员。