Author(s)

Carolina Crisci, Gonzalo Perera, Lia Sampognaro

Departamento Modelización Estadística de Datos e Inteligenica Artificial (MEDIA), CURE, Rocha, Universidad de la República, Montevideo, Uruguay.

In this article we improve a goodness-of-fit test, of the Kolmogorov-Smirnov type, for equally distributed- but not stationary-strongly dependent data. The test is based on the asymptotic behavior of the empirical process, which is much more complex than in the classical case. Applications to simulated data and discussion of the obtained results are provided. This is, to the best of our knowledge, the first result providing a general goodness of fit test for non-weakly dependent data.

Kolmogorov-Smirnov Test, Strongly Dependent Data, Asymptotic Behavior of Empirical Processes

Crisci, C. , Perera, G. and Sampognaro, L. (2023) Goodness-of-Fit Test for Non-Stationary and Strongly Dependent Samples. Advances in Pure Mathematics, 13, 226-236. doi: 10.4236/apm.2023.135016.