In this paper we consider a new analytic center cutting plane method in a projective space. We prove the efficiency for the general scheme and show that these results can be used in the analysis of a feasability problem, the variational inequality problem and the problem if constrained...

This paper is a user' guide to a set of Guss procedures developed at the Bank of Canada for estimating regime-switching models. The procedure can estimate relatively quickly a wide variety of switching models and so should prove useful to the applied researchers. Sample program listings are...

This paper proposes a Bayesian method of performance evaluation for investment managers. We begin with a flexible set of prior beliefs that can be elicited without any reference to probability distributions or their parameters. We then combine these prior beliefs with a general multi-factor...

The entropy principle yields, for a given set moments, a density that involves the smallest amount of prior information. We first show how entropy densities may be constructed in a numerically efficient way as the minimization of a potential. Next, for the case where the first four moments are...

The purpose of this paper is to consider the problem of statistical inference about a hazard rate function that is specified as the product of a parametric regression part and a non-parametric baseline hazard. Unlike Cox's proportional hazard model, the baseline hazard not only depends on the...

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.

We propose a nonparametric estimation technique of the coefficients of an univariate diffusion process. The estimation is done with a process observed at random times, corresponding to the crossing times of discrete levels. We first estimate the scale function of the process, which allows to...

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.