Markku Kuismin | Searcharxiv

arXiv2026

Using the rejection sampling for finding tests

A new method based on the rejection sampling for finding statistical tests is proposed. This method is conceptually intuitive, easy to implement, and applicable for arbitrary dimension. To illustrate its potential applicability, three distinct empirical examples are presented: (1) examine the differences between group means of correlated (repeated) or independent samples, (2) examine if a mean vector equals to a specific fixed vector, and (3) investigate if samples come from a specific population distribution. The simulation examples indicate that the new test has similar statistical power as uniformly the most powerful (unbiased) tests. Moreover, these examples demonstrate that the new test is a powerful goodness-of-fit test.

stat.ME↗

arXiv2021

Capacitated spatial clustering with multiple constraints and attributes

Capacitated spatial clustering, a type of unsupervised machine learning method, is often used to tackle problems in compressing, classifying, logistic optimization and infrastructure optimization. Depending on the application at hand, a wide set of extensions may be necessary in clustering. In this article we propose a number of novel extensions to PACK that is a novel capacitated spatial clustering method. These extensions are relocation and location preference of cluster centers, outliers, and non-spatial attributes. The strength of PACK is that it can consider all of these extensions jointly. We demonstrate the usefulness PACK with a real world example in edge computing server placement for a city region with various different set ups, where we take into consideration outliers, center placement, and non-spatial attributes. Different setups are evaluated with summary statistics on spatial proximity and attribute similarity. As a result, the similarity of the clusters was improved at best by 53%, while simultaneously the proximity degraded only 18%. In alternate scenarios, both proximity and similarity were improved. The different extensions proved to provide a valuable way to include non-spatial information into the cluster analysis, and attain better overall proximity and similarity. Furthermore, we provide easy-to-use software tools (rpack) for conducting clustering analyses.

stat.AP↗