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统计代写|数据科学、大数据和数据多样性代写Data Science, Big Data and Data Variety代考|Leveraging Machine Learning Algorithms for Finite Population
Breidt and Opsomer (2017) explored how MLMs can be used as the basis of the working models in the model-assisted approach to design-based estimation for probability samples. They provided a general framework within which predictions from various MLMs can be incorporated and used to derive finite population estimates and inference. They specifically illustrated how methods like $k$-nearest neighbors, CARTs and neural networks could be used within this framework. More recently, Buelens et al. (2018) explored similar approaches for using MLMs for inference from nonprobability samples. Their work compares quasi-randomization methods to model-based methods (or super-population models, as more formally explained by Elliott and Valliant (2017)), where various MLMs are used in creating the models. More specifically, Buelens et al. (2018) compared sample mean estimation, quasi-randomization pseudo-weighting based on poststratification via a known auxiliary variable for the entire population, generalized linear models, and a host of MLMs including $k$-nearest neighbors, ANNs, regression trees and support vector machines as the basis of generating model-based estimates. They tuned each of the MLMs using a repeated split-sample scenario based on 10 bootstrap replications, and the optimal values of the respective tuning parameters were then used to form models upon which final estimates were generated. In predicting a continuous outcome, Buelens and colleagues reported generally adequate, although varied, results from the MLMs compared to using either the sample mean or the pseudo-weighted quasi-randomization based estimator. Generally, the MLMs removed more or nearly the same amount of bias due to self-selection compared to either the sample mean or pseudo-weighted estimator, especially under moderate to severe levels of self-selection. They also identified support vector machines as a top performer compared to all other methods in almost all scenarios they examined.
Model-based estimates generally work well if the predictions made using the model are well suited for population members not included in the sample. The models are estimated using data from the sample (be it probability or nonprobability) based on a set of covariates that are available from members of the sample and population. If these covariates fail to fully represent the self-selection bias mechanism, the range of values on the outcome of interest may differ between the sample and population members not included in the sample. If so, some models will not be able to generate predicted values that extrapolate beyond the range of values observed from the population and this limitation will result in biased model-based estimates. Buelens and colleagues noted this limitation for the sample mean, pseudo-weighted estimator, $k$-nearest neighbors, and regression trees. They noted that generalized linear models, neural networks, and support vector machines (from among the MLMs they explored) are stronger choices in this situation. Reiterating points of exchangeability made by Mercer et al. (2017), predictive algorithm that can utilize it are very important. As this current work demonstrates. MLMs are not all equal in their ability to use such information adequately and some have limitations for such applications making method selection an important component in addition to variable selection for creating finite population inference using nonprobability samples.
统计代写|数据科学、大数据和数据多样性代写Data Science, Big Data and Data Variety代考|Discussion and Conclusions
This collection of examples, while not exhaustive, provides a glimpse into how survey researchers and social scientists are applying an assortment of data science methods within the new landscape. These examples show that data science methods are and can add value to the survey research process. But what is not as clear, yet, is just how the social sciences can add value to the broader data science community and Big Data ecosystem. Grimmer (2015) was quick to identify strengths that social scientists and survey researchers can bring to this conversation by stating, “Data scientists have significantly more experience with large datasets but they tend to have little training in how to infer causal effects in the face of substantial selection. Social scientists must have an integral role in this collaboration; merely being able to apply statistical techniques to massive datasets is insufficient. Rather, the expertise from a field that has handled observational data for many years is required.” In fact, the much-reported algorithmic bias among data scientists is a problem almost certainly related to coverage bias – an issue survey researchers have been investigating and mitigating for decades. More generally, advances on understanding and quantifying data error sources are yet another example where survey researchers can add value to the Big Data ecosystem. But apart from methodology, we believe that survey data in and of itself can add much needed insights into this ecosystem with the value proposition being directly related to the fact that surveys are often designed to maximize information about context and provide signals related to the “why” question. In this vein, survey data offer valuable additional sources of information that can enhance prediction accuracy.
This chapter is in no way comprehensive. Our goal was to provide survey researchers and social scientists, as well as data and computer scientists with some examples and ideas that illustrate how MLMs are and can be used throughout the main phases of the survey research process. If we were to cluster these examples in terms of how MLMs have been used, we would find four major uses simply labeled as processing, preparation, prioritization, and prediction.
统计代写|数据科学、大数据和数据多样性代写Data Science, Big Data and Data Variety代考|Gaining Insights Among Survey Variables
阿尔皮诺等人。（2018 年）根据德国社会经济小组调查的数据，使用生存随机森林来模拟德国婚姻状况随时间的变化，以了解过去二十年来德国婚姻解体的决定因素。他们使用变量重要性度量来确定对预测婚姻解体影响最大的影响变量和部分依赖图，以深入了解最有影响力的变量与婚姻解体状态之间的关联方向。他们的调查显示，关键连续预测变量与婚姻解体状态之间的关系可能不是线性的，并且其他影响可能不是作为主要影响而是通过调节另一个变量来影响结果。
如前面的示例所示，变量重要性度量（例如，从随机森林模型得出的）有助于将注意力集中在一组更小的有影响力的协变量上。然而，在存在许多可能相关或可能属于不同变量类型的变量时，使用常规随机森林得出的重要性度量通常存在偏差（Strobl et al. 2007）。在调查环境中，对于任何给定的预测问题，变量之间几乎总是存在某种程度的相关或关联，而且我们当然有多种变量类型。因此，要在调查环境中识别一组较小的重要变量，需要变量重要性的替代版本。模糊森林方法应用了递归特征消除过程，并且已经注意到提供的变量重要性估计值几乎与计算成本更高的条件森林的估计值一样无偏（Conn et al. 2015）。Dutwin 和 Buskirk (2017) 应用了一系列模糊随机森林模型，从 15 个基于概率的调查中确定的 500 多个变量的集合中，首先确定了一组较小的用于预测家庭互联网状态的重要预测因子。目标是确定一个小的、可管理的变量集，这些变量可用于创建预测互联网状态的模型，然后用于为后续在线样本的覆盖范围创建加权调整，这些样本涉及这一小部分问题。开展了一项基于概率的 RDD 调查，确定了大约相同数量的互联网和非互联网家庭提出了由模糊森林模型确定为重要的三打问题。另一个应用于 RDD 调查数据的模糊随机森林模型确定了最终的一组大约总共 12 个人口统计和非人口统计变量，这些变量对于预测互联网状态最重要。使用来自 RDD 调查和模型性能测量的数据拟合预测非互联网状态的最终常规随机森林模型，显示人口统计变量对于识别没有互联网的家庭很重要（例如，高灵敏度），而非人口统计的核心集变量对于识别那些拥有互联网的家庭最有效（例如，高特异性）。
统计代写|数据科学、大数据和数据多样性代写Data Science, Big Data and Data Variety代考|Adapting Machine Learning Methods to the Survey Setting
尽管迄今为止对现有 MLM 进行调整以纳入样本设计和加权信息的研究数量相当有限，但我们预计此类研究将在不久的将来继续增加，并将共同代表调查研究、统计数据和数据科学的方法论。麦康维尔等人。(2017) 将 LASSO 和自适应 LASSO 方法应用于调查环境。他们的工作确立了将这些方法应用于概率调查数据的理论，并提出了两个版本的套索调查回归权重，以便该方法可用于开发对感兴趣的多个调查结果的模型辅助、基于设计的估计。与此相类似，Toth 和 Eltinge（2011 年）开发了一种方法，通过合并有关复杂样本设计的信息（包括设计权重），通过递归分区将回归树适应概率调查上下文。Toth (2017) 后来开发了 R 包 rpms，它使回归树的估计在数值上成为可能，并且可用于基于概率的调查数据。Toth 和 Eltinge 的工作还释放了在模型辅助方法中使用回归树进行基于设计的推理的潜力。McConville 和 Toth (2019) 探索了回归树估计器，以使用所有人口单位可用的辅助数据自动确定分层后调整单元。他们指出，与其他模型辅助方法相比，这种方法的一个优势是回归树如何自然地结合连续和分类辅助数据。这些后层有能力捕捉变量之间的复杂相互作用，并且如模拟所示，它们可以提高模型辅助估计器的效率。此外，估计量被校准到每个后层的人口总数。
尽管并非明确适用于调查数据，但 Zhao 等人。(2016) 调整了随机森林模型来处理协变量上的缺失值，并促进改进对依赖于所有观察结果的邻近度度量的估计，而不仅仅是那些不合常理的观察值。尽管单个决策树具有通过代理处理缺失数据的能力，但由于在每个节点处仅选择变量子集进行节点分裂的机制，这种优势在一般随机森林方法中丢失了。赵等人。应用这种适应的随机森林方法，使用来自国家健康和营养检查调查的数据来检查吸烟对体重指数 (BMI) 的影响。
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