Component-wise outlier detection methods for robustifying multivariate functional samples
In: Statistical papers, Band 61, Heft 2, S. 595-614
ISSN: 1613-9798
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In: Statistical papers, Band 61, Heft 2, S. 595-614
ISSN: 1613-9798
In: Communications in statistics. Theory and methods, Band 42, Heft 7, S. 1265-1276
ISSN: 1532-415X
Health care expenditures constitute a significant portion of governmental budgets. The percentage of fraud, waste and abuse within that spending has increased over years. This paper introduces the emerging area of statistical medical fraud assessment, which becomes crucial to handle the increasing size and complexity of the medical programmes. An overview of fraud types and detection is followed by the description of medical claims data. The utilisation of sampling, overpayment estimation and data mining methods in medical fraud assessment are presented. Recent unsupervised methods are illustrated with real world data. Finally, the paper introduces potential future research areas such as integrated decision making approaches and Bayesian methods and concludes with an overall discussion. The main goal of this exposition is to increase awareness about this important area among a broader audience of statisticians.
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In: Socio-economic planning sciences: the international journal of public sector decision-making, Band 54, S. 47-57
ISSN: 0038-0121
In: Statistical papers, Band 58, Heft 2, S. 527-548
ISSN: 1613-9798