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首页> 外文期刊>Radiation Protection Dosimetry >MAXIMUM LIKELIHOOD ESTIMATES OF MEAN AND VARIANCE OF OCCUPATION RADIATION DOSES SUBJECTED TO MINIMUM DETECTION LEVELS
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MAXIMUM LIKELIHOOD ESTIMATES OF MEAN AND VARIANCE OF OCCUPATION RADIATION DOSES SUBJECTED TO MINIMUM DETECTION LEVELS

机译：最小检测水平下的均值的最大似然估计和职业辐射剂量的方差

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摘要

Data collection and its analysis in the field of nuclear safety is an important task in the sense that it powers the improvement of safety as well as reliability of the plant. Thus, occupational exposure data analysis is presented to measure the safety or reliability of radiation protection of a given facility. It also is required as a basic input in making decisions on radiation protection regulations and recommendations. A common practice in radiation protection is to record a zero for observation below minimum detection limit (MDL) doses, which leads to an underestimation of true doses and overestimation of the dose-response relationship. Exposure data (both external and internal) are collected by monitoring each individual and this kind of monitoring generally is graded as low-level monitoring. So, in such low-level monitoring, the occurrence of exposure below MDL invites statistical complications for estimating mean and variance because the data are generally censored, i.e observations below MDL are marked. In Type I censoring, the point of censoring (e.g. the detection limit) is 'fixed' a priori for all observations and the number of the censored observations varies. In Type II censoring, the number of censored observations is fixed a priori, and the point of censoring vary. The methodology generally followed in estimating mean and variance with these censored data was the replacement of missing dose by half the MDL. In this paper, authors have used the maximum likelihood estimation (MLE) approach for the estimation of mean and standard deviation. A computer code BDLCENSOR has been developed in which all these MLE-based advanced algorithms are implemented. In addition to the MLE-based method, an expectation maximisation algorithm has also been implemented. The code is written using Visual BASIC 6.0. The paper describes the details of the algorithms adopted for handling such censored data to estimate bias free mean and standard deviation.
机译：核安全领域的数据收集及其分析是一项重要任务，从某种意义上说，数据收集和分析可为提高工厂的安全性和可靠性提供动力。因此，提出了职业暴露数据分析以测量给定设施的辐射防护的安全性或可靠性。在制定辐射防护法规和建议时，也需要将其作为基本输入。辐射防护的一种常见做法是在最小检测极限（MDL）剂量以下记录一个零观测值，这会导致低估真实剂量并高估了剂量反应关系。通过监视每个人来收集暴露数据（外部和内部），这种监视通常被归类为低级监视。因此，在这样的低水平监视中，由于通常会检查数据，即在MDL以下进行标记，因此发生MDL以下的暴露会引发统计上的复杂性，以估计均值和方差。在类型I审查中，审查的点（例如检出限）是对所有观察值先验地``固定''的，并且被审查的观察点的数量各不相同。在II型审查中，审查观测的数量是先验固定的，并且审查的点也有所不同。估计这些审查数据的均值和方差时通常采用的方法是将丢失的剂量替换为MDL的一半。在本文中，作者使用最大似然估计（MLE）方法估计均值和标准差。已经开发了计算机代码BDLCENSOR，其中实现了所有这些基于MLE的高级算法。除了基于MLE的方法外，还实现了期望最大化算法。该代码是使用Visual BASIC 6.0编写的。本文介绍了用于处理此类检查数据以估计无偏差均值和标准偏差的算法的详细信息。

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