Schoch, Tobias
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The Swiss health care atlas - relaunch in scale
Inspired by the Dartmouth Atlas of Health Care, an early version of the Swiss Atlas of Health Care (SAHC) was released in 2017. The SAHC provides an intuitive visualization of regional variations of medical care delivery and thus allows for a broad diffusion of the contents. That is why the SAHC became widely accepted amongst health care stakeholders. In 2021, the relaunch of the SAHC was initiated to update as well as significantly expand the scope of measures depicted on the platform, also integrating indicators for outpatient care in order to better reflect the linkages between inpatient and outpatient health care provision. In the course of this relaunch, the statistical and technical aspects of the SAHC have been reviewed and updated. This paper presents the key aspects of the relaunch project and provides helpful insights for similar endeavors elsewhere.
Treatment of sample under-representation and skewed heavy-tailed distributions in survey-based microsimulation: An analysis of redistribution effects in compulsory health care insurance in Switzerland
The credibility of microsimulation modeling with the research community and policymakers depends on high-quality baseline surveys. Quality problems with the baseline survey tend to impair the quality of microsimulation built on top of the survey data. We address two potential issues that both relate to skewed and heavy-tailed distributions. First, we find that ultra-high-income households are under-represented in the baseline household survey. Moreover, the sample estimate of average income underestimates the known population average. Although the Deville-Särndal calibration method corrects the under-representation, it cannot achieve alignment of estimated average income in the right tail of the distribution with known population values without distorting the empirical income distribution. To overcome the problem, we introduce a Pareto tail model. With the help of the tail model, we can adjust the sample income distribution in the tail to meet the alignment targets. Our method can be a useful tool for microsimulation modelers working with survey income data. The second contribution refers to the treatment of an outlier-prone variable that has been added to the survey by record linkage (our empirical example is health care cost). The nature of the baseline survey is not affected by record linkage, that is, the baseline survey still covers only a small part of the population. Hence, the sampling weights are relatively large. An outlying observation together with a high sampling weight can heavily influence or even ruin an estimate of a population characteristic. Thus, we argue that it is beneficial – in terms of mean square error – to use robust estimation and alignment methods, because robust methods are less affected by the presence of outliers.
Social inequality and the biological standard of living: An anthropometric analysis of Swiss conscription data, 18751950
On the strong law of large numbers for nonnegative random variables. With an application in survey sampling
Strong laws of large numbers with arbitrary norming sequences for nonnegative not necessarily independent random variables are obtained. From these results we establish, among other things, stability results for weighted sums of nonnegative random variables. A survey sampling application is provided on strong consistency of the Horvitz-Thompson estimator and the ratio estimator.
Robust, distribution-free inference for income share ratios under complex sampling
wbacon: weighted BACON algorithms for multivariate outlier nomination (detection) and robust linear regression
Outlier nomination (detection) and robust regression are computationally hard problems. This is all the more true when the number of variables and observations grow rapidly. Among all candidate methods, the two BACON (blocked adaptive computationally efficient outlier nominators) algorithms of Billor et al.(2000) have favorable computational characteristics as they require only a few model evaluations irrespective of the sample size. This makes them popular algorithms for multivariate outlier nomination/detection and robust linear regression (at the time of writing Google Scholar reports more than 500 citations of the Billor et al.(2000) paper). wbacon is a package for the R statistical software (R Core Team, 2021). It is aimed at medium to large data sets that can possibly have (sampling) weights (e.g., data from complex survey samples). The package has a user-friendly Rinterface (with plotting methods, etc.) and is written mainly in the C language (with OpenMP support for parallelization; see OpenMP Architecture Review Board(2018)) for performance reasons.
Robust Unit-Level Small Area Estimation: A Fast Algorithm for Large Datasets
Small area estimation is a topic of increasing importance in official statistics. Although the classical EBLUP method is useful for estimating the small area means efficiently under the normality assumptions, it can be highly influenced by the presence of outliers. Therefore, Sinha and Rao (2009; The Canadian Journal of Statistics) proposed robust estimators/predictors for a large class of unit- and area-level models. We confine attention to the basic unit-level model and discuss a related, but slightly different, robustification. In particular, we develop a fast algorithm that avoids inversion and multiplication of large matrices, and thus permits the user to apply the method to large datasets. In addition, we derive much simpler expressions of the bounded-influence predicting equations to robustly predict the small-area means than Sinha and Rao (2009) did.