Bandwidth-free nonparametric density estimation for grouped data

In some situations, data is collected under systematical and technical constraints due to uncertainty in experimental reports, intermittent measurements, confidentiality, and non-detects.

For this reason, it might not be possible to retrieve or receive the data in a conventional format but rather in a grouped form where only the number of occurrences is known within intervals.

The challenge is to estimate the density of the underlying ungrouped data based on the observed grouped data with no information regarding the underlying distribution.

To overcome this problem, this study introduces a mean-adjusted log-concave (MALC) density estimation method for univariate grouped data, aiming to provide a bandwidth-free non-parametric approach that does not rely on specific distributional assumptions.

The performance of the MALC method is evaluated through simulations across various distributions with different sample sizes and grid widths.

The results demonstrate the robustness and effectiveness of the MALC approach in grouped data analysis, offering a broader range of applications over traditional methods.