Debt collection is a massive industry, within the USA alone more than $50 billion recovered each year. However the information available is often limited and incomplete, and predicting whether a given debtor would repay is inherently a challenging task. This has amplified research on debt recovery classification and prediction models of late. This report considers three main mathematical, data mining and statistical models in debt recovery classification, in logistic regression, artificial neural networks and affinity analysis. It also compares the effectiveness of the above-mentioned tools in evaluating whether a debt is likely to be repaid. The construction and analysis of the models were based on a fairly large unbalanced data sample provided by a debt collection agency. We have shown that all three models could classify the debt repayments with a considerable accuracy, if the assumptions of the models are satisfied.
Conservation areas are critical for biodiversity conservation, but few citizen science studies have evaluated their efficiency. In the absence of thorough survey data, this study assessed which species benefit most from conservation areas using citizen science bird counts extracted from the Atlas of Living Australia. This was accomplished by fitting temporal models using citizen science data taken from ALA for the years 2010–2019 using the INLA approach. The trends for six resident shorebird species were compared to those for the Australian Pied Oystercatcher, with the Black-fronted Dotterel, Red-capped Dotterel, and Red-kneed Dotterel exhibiting significantly steeper increasing trends. For the Black-fronted Dotterel, Masked Lapwing, and Red-kneed Dotterel, steeper rising trends were recorded in conservation areas than in other locations. The Dotterel species’ conservation status is extremely favourable. This study demonstrates that, with some limits, statistical models can be used to track the persistence of resident shorebirds and to investigate the factors affecting these data.
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