TESTING IN STATIONARY MODELS BASED ON DIVERGENCES OF OBSERVED AND THEORETICAL FREQUENCIES

Goodness-of-fit tests for stationary distributions of dependent data a re considered, based on f-divergences of observed and theoretical cell frequencies. Pearson's chi(n)(2) is a special version. A methodology is presented leading to asymptotically cu-level variants of these test s, and also to the selection of most powerful versions. This methodolo gy is illustrated on binary Markov data. Similar procedures have been previously established for independent data. The possibility to extend these procedures to dependent data is a new argument in favour of the f-divergence alternatives to the classical Pearson's chi(n)(2).