𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Extracting macroscopic stochastic dynamics: Model problems

✍ Scribed by Wilhelm Huisinga; Christof Schütte; Andrew M. Stuart


Publisher
John Wiley and Sons
Year
2002
Tongue
English
Weight
556 KB
Volume
56
Category
Article
ISSN
0010-3640

No coin nor oath required. For personal study only.


📜 SIMILAR VOLUMES


Inference in dynamic stochastic frontier
✍ Efthymios G. Tsionas 📂 Article 📅 2006 🏛 John Wiley and Sons 🌐 English ⚖ 87 KB

## Abstract An important issue in models of technical efficiency measurement concerns the temporal behaviour of inefficiency. Consideration of dynamic models is necessary but inference in such models is complicated. In this paper we propose a stochastic frontier model that allows for technical inef

Iterative methods for dynamic stochastic
✍ Raymond K. Cheung 📂 Article 📅 1998 🏛 John Wiley and Sons 🌐 English ⚖ 147 KB 👁 1 views

We consider a routing policy that forms a dynamic shortest path in a network with independent, positive and discrete random arc costs. When visiting a node in the network, the costs for the arcs going out of this node are realized, and then the policy will determine which node to visit next with the

A Stochastic Model for Early HIV-1 Popul
✍ Henry C. Tuckwell; Emmanuelle Le Corfec 📂 Article 📅 1998 🏛 Elsevier Science 🌐 English ⚖ 309 KB

A simple stochastic mathematical model is developed and investigated for early human immunodeficiency virus type-1 (HIV-1) population dynamics. The model, which is a multi-dimensional diffusion process, includes activated uninfected CD4 + T cells, latently and actively infected CD4 + T cells and fre

Dynamic stochastic copula models: estima
✍ Christian M. Hafner; Hans Manner 📂 Article 📅 2010 🏛 John Wiley and Sons 🌐 English ⚖ 389 KB

## SUMMARY We propose a new dynamic copula model in which the parameter characterizing dependence follows an autoregressive process. As this model class includes the Gaussian copula with stochastic correlation process, it can be viewed as a generalization of multivariate stochastic volatility model