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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=711435
• 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.

一个给定的统计数据有多准确？一旦统计数据被用来估计感兴趣的参数，这可能是研究人员提出的第一个问题。通过分析方法获得给定统计数据的精确度度量，如方差，并不总是那么容易。这就是为什么重新采样方法，如Bootstrap，在文献中被广泛使用来估计这种测量。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，以建立新的思路。 加拿大统计局为研究人员提供了访问包含引导权重列的数据文件。这些权重考虑了观测中的抽样可变性，可以很容易地用于计算估计器的方差或构造置信度区间。不幸的是，生活很少如此简单，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响应。通常使用填充数据文件中的空单元格的补偿来补偿没有响应的项。把估计值当作观测值来对待，可能会导致对点估计值的方差估计严重偏低，因为用于全响应调查数据的Bootstrap方法既没有考虑项目无响应引起的变异性，也没有考虑归因性。我计划为输入的调查数据建立Bootstrap方法，假设响应概率不相等和复杂的调查设计的情况。 Bootstrap在不同的统计领域有着广泛的应用。将方程估计的广义Bootstrap方法应用于模型参数估计的方差估计。在这种方法下，我们打算在计数数据的自相关时间序列的半参数回归模型的情况下找到最优自举权重，并将其应用于金融和流行病学。 在另一个应用中，我打算为流行的队列生存数据开发一种Bootstrap方法。这类数据的一个特例是长度偏差的右删失数据。人们的兴趣主要来自一些加拿大统计学家在使用作为加拿大健康与老龄化研究调查一部分收集的痴呆症数据分析存活率时面临的挑战。现有的用于这类生存数据的Bootstrap方法没有考虑左截断分布中的额外可用信息。因此，这种自举方法是不有效的。我计划开发一种为这些数据量身定做的高效引导方法。在这样的环境下研究刀切重采样方法也是我研究的一部分。刀切方法通常是为了减少偏差，其中插件估计器往往由于正确的审查和/或有偏抽样而产生偏差。 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大统计人员。