We propose a new method, the "Auto-SLEX" method, for analyzing bivariate non-stationary processes. The Auto-SLEX method is a procedure that automatically segments the time series into approximatively stationary blocks and automatically estimates the time-varying spectra and coherence.

This article reviews the role of wavelets in statistical time series analysis. We survey work that emphasises scale such as estimation of variance and the scale exponent of a process with a specific scale behaviour such as 1/f processes. We present some of our own work on locally stationary...

The economic literature proposes several nonparametric frontier estimators based on the idea of enveloping the data (FDH and DEA-type estimators). Many have claimed that FDH and DEA techniques are non-statistical, as opposed to econometric approaches where particular parametric expressions are...

This paper proposes a general methodology for bootstrapping in frontier models, extending the more restrictive method proposed in Simar and Wilson (1998a) by allowing for heterogeneity in the structure of efficiency. A numerical illustration with real data is provided to illstrate the methodology.

In this paper, we develop a Bayesian analysis of semi-parametric binary choice model. The prior specification of the functional parameter, namely the distribution function of a latent variable, is of the Dirichlet process type and the prior specification of the Euclidean parameter, namely the...