arXiv subjects

Shovanur Haque

Publications and source records attributed to Shovanur Haque.

3 recordsLinked to original sources

arXiv2020

Assessing the accuracy of individual link with varying block sizes and cut-off values using MaCSim approach

Record linkage is the process of matching together records from different data sources that belong to the same entity. Record linkage is increasingly being used by many organizations including statistical, health, government etc. to link administrative, survey, and other files to create a robust file for more comprehensive analysis. Therefore, it becomes necessary to assess the ability of a linking method to achieve high accuracy or compare between methods with respect to accuracy. In this paper, we evaluate the accuracy of individual link using varying block sizes and different cut-off values by utilizing a Markov Chain based Monte Carlo simulation approach (MaCSim). MaCSim utilizes two linked files to create an agreement matrix. The agreement matrix is simulated to generate re-sampled versions of the agreement matrix. A defined linking method is used in each simulation to link the files and the accuracy of the linking method is assessed. The aim of this paper is to facilitate optimal choice of block size and cut-off value to achieve high accuracy in terms of minimizing average False Discovery Rate and False Negative Rate. The analyses have been performed using a synthetic dataset provided by the Australian Bureau of Statistics (ABS) and indicated promising results.

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arXiv2020

Improved assessment of the accuracy of record linkage via an extended MaCSim approach

Record linkage is the process of bringing together the same entity from overlapping data sources while removing duplicates. Huge amounts of data are now being collected by public or private organizations as well as by researchers and individuals. Linking and analysing relevant information from this massive data reservoir can provide new insights into society. However, this increase in the amount of data may also increase the likelihood of incorrectly linked records among databases. It has become increasingly important to have effective and efficient methods for linking data from different sources. Therefore, it becomes necessary to assess the ability of a linking method to achieve high accuracy or to compare between methods with respect to accuracy. In this paper, we improve on a Markov Chain based Monte Carlo simulation approach (MaCSim) for assessing a linking method. MaCSim utilizes two linked files that have been previously linked on similar types of data to create an agreement matrix and then simulates the matrix using a proposed algorithm developed to generate re-sampled versions of the agreement matrix. A defined linking method is used in each simulation to link the files and the accuracy of the linking method is assessed. The improvement proposed here involves calculation of a similarity weight for every linking variable value for each record pair, which allows partial agreement of the linking variable values. A threshold is calculated for every linking variable based on adjustable parameter "tolerance" for that variable. To assess the accuracy of linking method, correctly linked proportions are investigated for each record. The extended MaCSim approach is illustrated using a synthetic dataset provided by the Australian Bureau of Statistics (ABS) based on realistic data settings. Test results show higher accuracy of the assessment of linkages.

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arXiv2020

Assessing the accuracy of record linkages with Markov chain based Monte Carlo simulation approach

Record linkage is the process of finding matches and linking records from different data sources so that the linked records belong to the same entity. There is an increasing number of applications of record linkage in statistical, health, government and business organisations to link administrative, survey, population census and other files to create a complete set of information for more complete and comprehensive analysis. To make valid inferences using a linked file, it is increasingly becoming important to assess the linking method. It is also important to find techniques to improve the linking process to achieve higher accuracy. This motivates to develop a method for assessing linking process and help decide which linking method is likely to be more accurate for a linking task. This paper proposes a Markov Chain based Monte Carlo simulation approach, MaCSim for assessing a linking method and illustrates the utility of the approach using a realistic synthetic dataset received from the Australian Bureau of Statistics to avoid privacy issues associated with using real personal information. A linking method applied by MaCSim is also defined. To assess the defined linking method, correct re-link proportions for each record are calculated using our developed simulation approach. The accuracy is determined for a number of simulated datasets. The analyses indicated promising performance of the proposed method MaCSim of the assessment of accuracy of the linkages. The computational aspects of the methodology are also investigated to assess its feasibility for practical use.

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