In an infinite-horizon setting, Ferejohn and Page showed that Arrow’s axioms and stationarity lead to a dictatorship by the first generation. Packel strengthened this result by proving that no collective choice rule generating complete social preferences can satisfy unlimited domain, weak Pareto and stationarity. We prove that a domain restriction can be imposed and completeness can be dropped ...

We derive new stationarity results for derivative-free, generating set search methods for linearly constrained optimization. We show that a particular measure of stationarity is of the same order as the step length at an identifiable subset of the iterations. Thus, even in the absence of explicit knowledge of the derivatives of the objective function, we still have information about stationarit...

BACKGROUND Understanding complex brain networks using functional magnetic resonance imaging (fMRI) is of great interest to clinical and scientific communities. To utilize advanced analysis methods such as graph theory for these investigations, the stationarity of fMRI time series needs to be understood as it has important implications on the choice of appropriate approaches for the analysis of ...

In this contribution we generalize the test of sphericity as a test of stationarity for time-series. The sphericity statistics is in our case a measure of distance between the empirical correlations calculated on two contiguous segments of the same process. We prove that under the hypothesis of stationarity the logarithm of the sphericity converges in distribution to a quadratic form in a multi...

This paper provides a strict stationarity proof for general nonlinear regression models. In particular, it shows the existence of a strictly stationary solution to first order dynamic nonlinear models when the innovations are only assumed to satisfy a strict stationarity or strong mixing condition. The results of this paper can be applied to show the strict stationarity of threshold unit root m...

Reinforcement learning induces non-stationarity at several levels. Adaptation to non-stationary environments is of course a desired feature of a fair RL algorithm. Yet, even if the environment of the learning agent can be considered as stationary, generalized policy iteration frameworks, because of the interleaving of learning and control, will produce non-stationarity of the evaluated policy a...

In geosimulation models it is often, implicitly or explicitly, assumed that the relationship between the system state change and its explanatory processes, i.e. model structure, is stationary. Potential systemic changes influencing the stationarity of this relationship are thereby disregarded. We present a framework to assess model structure stationarity and apply it on a case study in which we...

This article proposes a test of stationarity for random fields on a regular lattice motivated by a problem arising from texture analysis. Our approach is founded on the locally stationary two-dimensional wavelet (LS2W) process model for lattice processes which has previously been used for standard texture analysis tasks such as texture discrimination and classification. We propose two variants ...