A class of generalized autoregressive moving average (GARMA) models is developed that
extends the univariate Gaussian ARMA time series model to a flexible observation-driven …

Some important aspects of the asymptotic likelihood theory for stochastic processes is
reviewed, with particular attention to martingale properties and to information and higher …

It is shown how one can construct a model for a jump process depending on an arbitrary
intensity measure with the property that if the measure is absolutely continuous it reduces to …

Exponential families of stochastic processes are parametric stochastic p-cess models for
which the likelihood function exists at all? nite times and has an exponential representation …

In one of his fundamental papers Le Can (1960) introduced what is now called locally
asymptotically normal (LAN) families of distributions and obtained several basic results …

This paper is mainly concerned with the asymptotic theory of maximum likelihood estimation
for continuous-time stochastic processes. The role of martingale limit theory in this theory is …

Statistical inference carries great significance in model building from both the theoretical and
the applications points of view. Its applications to engineering and economic systems …

Asymptotic normality of the posterior distribution of a parameter in a stochastic process is
shown to hold under conditions which do little more than ensure consistency of a maximum …

Our main concern in this paper is the estimation of m and σ 2 from a branching process {Z t
(n)} having a random number of ancestors Z 0 (n), as both n (and thus Z 0 (n) in some sense) …

Conditional exponential families of Markov processes are defined and a representation of
the score function martingale is established for the important conditionally additive case …