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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|Gaining Insights Among Survey Variables

Arpino et al. (2018) used survival random forests to model changes in marital status over time in Germany based on data from the German Socio-Economic Panel survey to understand determinants of marriage dissolutions in Germany over the past two decades. They used variable importance measures to identify influential variables with the largest impact on predicting dissolution of marriages and partial dependence plots to gain insights into the direction of the association between the most influential variables and marriage dissolution status. Their investigation revealed that relationship between key continuous predictors and the marriage dissolution status may not be linear and that other effects may be affecting the outcome not as a main effect but through the moderation of another variable. These insights highlight future work in confirming these more complex relationships among a key set of predictors and marriage dissolution status.

As the previous example illustrates, variable importance measures, for example, derived from random forest models can be helpful in providing focus on a much smaller set of influential covariates. However, importance measures derived using regular random forests are often biased in the presence of many variables that might be correlated or might be of different variable types (Strobl et al. 2007). In the survey setting, some level of correlation or association almost always exists between the variables for any given prediction problem, and we certainly have a mix of variable types. So to identify a smaller set of important variables within the survey context, an alternate version of variable importance is needed. The fuzzy forests method applies a recursive feature elimination process and has been noted to offer estimates of variable importance that are nearly as unbiased as those computed for the more computationally expensive conditional forests (Conn et al. 2015). Dutwin and Buskirk (2017) applied a series of fuzzy random forest models to first identify a smaller set of important predictors for predicting household Internet status, from a collection of over 500 variables identified from 15 probability-based surveys. The goal was to identify a small, manageable set of variables that could be used to create a model for predicting Internet status and then be used to create weighting adjustments for coverage in subsequent online samples that fielded this small collection of questions. A probability-based RDD survey was fielded that identified about the same number of Internet and non-Internet households that asked the three dozen questions identified as important by the fuzzy forest models. Another fuzzy random forest model applied to the RDD survey data identified a final set of about a total of about 12 demographic and nondemographic variables that were most important for predicting Internet status. A final regular random forest model predicting non-Internet status was fit using data from the RDD survey and model performance measures for this model revealed that the demographic variables were important for identifying households without Internet (e.g. high sensitivity), while the core set of nondemographic variables were most powerful for identifying those households with Internet (e.g. high specificity).

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

Although the number of studies making adaptations of existing MLMs to incorporate sample design and weighting information is rather limited to date, we anticipate that such studies will continue to increase in the near future and will collectively represent an important contribution of survey research, statistics, and methodology to data science. McConville et al. (2017) adapted the LASSO and adaptive LASSO approaches to the survey setting. Their work established the theory for applying these methods to probability survey data and proposed two versions of lasso survey regression weights so that this method can be used to develop model-assisted, design-based estimates of multiple survey outcomes of interested. In a similar vein, Toth and Eltinge (2011) developed a method for adapting regression trees via recursive partitioning to the probability survey context by incorporating information about the complex sample design, including design weights. Toth (2017) later developed the R-package, rpms, that makes estimation of regression trees numerically possible and accessible for probability-based survey data. Toth and Eltinge’s work also unlocked the potential for using regression trees within the model-assisted approach to design-based inference. McConville and Toth (2019) explored regression tree estimators to automatically determine poststratification adjustment cells using auxiliary data available for all population units. They noted that one strength of this method over other model-assisted approaches is how naturally regression trees can incorporate both continuous and categorical auxiliary data. These poststrata have the ability to capture complex interactions between the variables, and as shown in the simulations, they can increase the efficiency of the model-assisted estimator. Additionally, the estimator is calibrated to the population totals of each poststratum.

Although not explicitly applicable only to survey data, Zhao et al. (2016) adapted random forest models to handle missing values on covariates and for facilitating improved estimation of proximity measures that rely on all observations rather than just those that are out of bag. Although individual decision trees have the ability to handle missing data via surrogates, this advantage is lost within the general random forest approach by virtue of the mechanics of only selecting a subset of variables for node splitting at each node. Zhao et al. applied this adapted random forest method to examine impacts of smoking on body mass index (BMI) using data from the National Health and Nutrition Examination Survey. Missing values were implicitly handled within their revised approach via surrogates and thus eliminated the need to explicitly imputing missing values for any of the covariates used in the model.

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|Best607

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。

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

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