We propose two nonparametric transition density-based speciþcation tests for continuous-time diffusion models. In contrast to marginal density as used in the literature, transition density can capture the full dynamics of a diffusion process, and in particular, can distinguish processes with...

The aim of this article is to (a) reexamine the nature of structural equation modeling (SEM) estimates of autoregressive moving average (ARMA) parameters; (b) replicate S. Van Buuren's simulation experiment in light of P. C. M. Molenaar's comment; and (c) examine the behavior of the...

Conference Paper

Estimation results in linear regression models are sometimes in contrast with what was expected on the basis of a certain set of hypotheses or theory, in the sense that one or more parameters have the "wrong sign". One could be inclined to think that this is due to collinearity across...

We propose two nonparametric transition density-based speciþcation tests for continuous-time diffusion models. In contrast to marginal density as used in the literature, transition density can capture the full dynamics of a diffusion process, and in particular, can distinguish processes with...

Estimation results in linear regression models are sometimes in contrast with what was expected on the basis of a certain set of hypotheses or theory, in the sense that one or more parameters have the "wrong sign". One could be inclined to think that this is due to collinearity across...

This paper considers a class of nonstationary Gaussian processes with possible long-range dependence (LRD) and intermittency. The author proposes a new estimation method to simultaneously estimate both the LRD and intermittency parameter. An application of the proposed estimation method to a...

For many problems of statistical inference in regression modelling, the Fisher information matrix depends on certain nuisance parameters which are unknown and which enter the model nonlinearly. A common strategy to deal with this problem within the context of design is to construct maximin...