Lexical parsing is the whole set of techniques to increase from a raw text to a sequence of tagged words ; these tags are morphological and grammatical information. Lexical parsing generally uses an electronic dictionary. But a few of them accommodate themselves to proper nouns that are a...

This paper studies the implementation of the coupling from the past (CFTP)method of Propp and Wilson (1996) in the set-up of two and three component mixtures with known components. We show that monotonicity structures can be exhibited in both cases, but that CFTP an still be costly for three...

Applying the concept of underlying inflation can be thought of as an attempt to capture the general trend in inflation more accurately than with readily available data on headline inflation. In this paper a number of approaches to the analysis of underlying inflation are examined from a unifying...

The data consists of multivariate failure times under random censorship. By the kernel smoothing techniques, convolutions of integrated multivariated hazard functions provide some estimators of the so-called multivariate hazard functions (Fermanian (1995)). We adopt the method of Jones, Marron...

This paper obtains asymptotic expansions of the frequentist distributions of modified likelihood ratio statistics when the observations are discrete. An upper bound of the uncertainty due to the discrete nature of the observations is obtained, slightly larger than Yarnold's result (obtained in...

In this paper, we study maximum likelihood estimation and Lagrange multiplier testing of a one-way error components regression model suitable for incomplete panel and including parametrically specified variance functions for both individual-specific and general error disturbances.

This paper proposes alternative methods for constructing estimators from accept-reject samples by incorporating the variables rejected by the algorithm.

This paper synthesises a global approach to both Bayesian and likelihood treatments of the estimation of the parameters of a hidden Markov model for the cases of normal and Poisson underlying distribution.