Inference on parameters of a shifted rescaled Wiener process based on nonidentically distributed observations

Abstract

The paper deals with a special type of a random process. The inference is based on the first time when the process reaches a pre-specified positive boundary, where the boundary can be different for each observation.

We focus on estimation of the parameters of the process and we shortly deal with testing hypotheses about one parameter at a time.

Keywords

• Inference
• parameters
• shifted
• rescaled
• Wiener
• process
• based
• nonidentically
• distributed
• observations