This paper proposes large deviation optimal properties of the empirical likelihood testing (ELT) moment selection procedures for moment restriction models. Since the parameter spaces of the moment selection problem is discrete, the conventional Pitman-type local alternative approach is not very helpful. By applying the theory of large deviations, we analyze convergence rates of the error probab...

The j-State General Markov Model of evolution (due to Steel) is a stochastic model concerned with the evolution of strings over an alphabet of size j. In particular, the Two-State General Markov Model of evolution generalises the well-known Cavender-Farris-Neyman model of evolution by removing the symmetry restriction (which requires that the probability that a `0' turns into a `1' along an edg...

Several new tests are proposed for examining the adequacy of a family of parametric models against large nonparametric alternatives. These tests formally check if the bias vector of residuals from parametric Ž ts is negligible by using the adaptive Neyman test and other methods. The testing procedures formalize the traditional model diagnostic tools based on residual plots. We examine the rates...

In the present paper we consider recursive games that satisfy an absorbing property defined by Vieille. We give two sufficient conditions for existence of an equilibrium payoff in such games, and prove that if the game has at most two non-absorbing states, then at least one of the conditions is satisfied. Using a reduction of Vieille, we conclude that every stochastic game which has at most two...

The data driven Neyman statistic consists of two elements: a score statistic in a finite dimensional submodel and a selection rule to determine the best fitted submodel. For instance, Schwarz BIC and Akaike AIC rules are often applied in such constructions. For moderate sample sizes AIC is sensitive in detecting complex models, while BIC works well for relatively simple structures. When the sam...

A neural network based coherent detector is proposed for detecting gaussian targets in gaussian clutter. Target and clutter ACF are supposed gaussian with different powers and one lag correlation coefficients. While clutter mean Doppler frequency is set to 1, the influence of target mean Doppler frequency is considered. The neural detector performance is compared to the Neyman-Pearson one. For ...