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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=675437
• 关键词: Resampling; Methods; Survey; Data; Extensions; 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？这可能是研究人员提出的第一个问题，一旦使用统计量来估计感兴趣的参数。通过分析方法，获得给定统计量的准确度量（例如方差）并不总是那么容易。这就是为什么在文献中广泛使用重新采样方法（例如引导程序）来估计此类测量值。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Bootstrap权重列的数据文件。这些权重解释了观测值的采样可变性，并且可以轻松地用于计算估计器的方差或构建置信区间的方差。不幸的是，在统计调查中，生活很少是一个简单而重要的实际问题，就是大多数数据文件中存在项目无响应。通常使用填充数据文件中的空单元格的插补来补偿项目无响应。将估算值视为被观察到的值，可能会导致点估计器的方差严重低估，因为用于完整响应调查数据的引导程序方法既不考虑项目无响应引起的可变性，也没有考虑到归因。我计划构建引导方法，以假设响应概率不相等和复杂的调查设计案例，以估算调查数据。 ****** bootstrap广泛应用于不同的统计区域。用于估计方程的广义引导程序用于估计模型参数估计的方差。在这种方法下，我们打算在自相关时间序列数据的半参数回归模型的情况下找到最佳的引导权重。此类数据的特殊情况是长度偏向的右审查数据。兴趣主要来自一些加拿大统计学家面临的挑战，同时分析了作为加拿大健康和衰老调查研究的一部分收集的痴呆症数据的生存。此类生存数据的现有引导方法未考虑左截断分布中的额外可用信息。因此，这种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有效的引导方法。在这种情况下，研究折刀重采样方法也是我的研究的一部分。折刀方法通常旨在减少由于右审查和/或偏置采样而经常偏置插件估计器的偏差。 *****这些项目将改善当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大统计学家。