Nonparametric probability density estimation: improvements to the histogram for laboratory data

The histogram has long been used in the clinical laboratory for the depiction and manipulation of frequency data. We present recent results of refinements to the usual histogram procedures along with modern alternative methods of estimating frequency distributions, including the kernel and discrete maximum penalized likelihood estimation (DMPLE) approaches. We compared these nonparametric methods on 15 different types of simulated distributions, and on several sets (greater than 1000 subjects/set) of real data, including alanine aminotransferase, aspartate aminotransferase, and lactate dehydrogenase levels. Each frequency curve estimation technique was evaluated by measuring the integrated mean square error between each technique's prediction and the true underlying distribution, using Monte Carlo techniques on sample sets with size 49 and 119. The kernel method was the clear method of choice, both in performance (best in 22/36 cases) and in practical usage.