Evaluation metrics are essential in selecting the most appropriate predictive model. Conventional metrics such as R-squared, root mean squared error (RMSE), and mean absolute percentage error (MAPE) rely on arithmetic averages, making them sensitive to outliers, undefined values, and scale...

In this article, new ratio regression type estimators with imputation have been proposed as means to overcome the problem of missing data relating to a studied variable in a sample survey. It has been shown that the suggested estimators are more efficient than the mean method of imputation, the...

The rare access to exact official geocoordinates opens new methodological possibilities for analyzing highly sensitive tax data. We explore their visualization potential and systematically evaluate aggregation as an anonymization strategy, with particular attention to its methodological and...

Data is an essential fuel for artificial intelligence (AI) to power the underlying machine learning (ML) algorithms. Missing data is common in most real-world datasets due to measurement errors, non-responses, and human errors during the data collection, which can ultimately lead to reduced...

The availability of geocoordinates offers valuable insights into spatial patterns of economic, demographic and health outcomes. However, disclosing the exact geolocation of statistical units to secondary analysts contravenes the responsible use of data. To protect privacy, anonymisation methods...