Benchmarks are standards that allow to identify opportunities for improvement among comparable units. The code performs a 2-step estimation of probabilistic benchmarks in noisy data sets: (i) double-hyperbolic undersampling filters the noise of key performance indicators (KPIs), and (ii) relevance vector machines estimate probabilistic benchmarks with the denoised KPIs. The usefulness of the methods is illustrated with an application to a database of nano-finance+.
引用格式
Rolando Gonzales Martinez (2024). Double-hyperbolic undersampling & probabilistic benchmarks (https://www.mathworks.com/matlabcentral/fileexchange/74398-double-hyperbolic-undersampling-probabilistic-benchmarks), MATLAB Central File Exchange. 检索来源 .
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版本 | 已发布 | 发行说明 | |
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1.0.0 |