Non-Parametric Goodness-of-Fit Tests Using Tsallis Entropy Measures
Mehmet Siddik Cadirci · 2025
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Description
Mehmet Siddik Cadirci develops new goodness-of-fit tests in a statistics paper from two thousand twenty-five, written alone. Such tests check whether data match a claimed distribution. The tests rest on Tsallis entropy, a generalized measure of uncertainty, with emphasis on two flexible distribution families.
The test statistic compares a closed-form entropy under the assumed model with a neighbor-based estimate from the data. Cadirci proves the estimator settles on the true value under mild conditions. Cutoff values come from resampling methods. Wide simulations report the tests' false alarm rates, power, and running time across settings. An applied example shows how to calibrate the tests in practice.
Catalog record
| CREATOR | Mehmet Siddik Cadirci |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 31 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |