✦ LIBER ✦

Statistical analysis for stationary time processes with irregular observations

✍ Scribed by M.A. Ghazal

Book ID: 108395584
Publisher: Elsevier Science
Year: 2003
Tongue: English
Weight: 86 KB
Volume: 134
Category: Article
ISSN: 0096-3003
DOI: 10.1016/s0096-3003(01)00290-9

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

SUFFICIENT STATISTICS FOR STATIONARY DIS

SUFFICIENT STATISTICS FOR STATIONARY DISCRETE-TIME GAUSSIAN RANDOM PROCESSES

✍ Bradley W. Dickinson 📂 Article 📅 1982 🏛 John Wiley and Sons 🌐 English ⚖ 196 KB

Prediction Error for Continuous-Time Sta

Prediction Error for Continuous-Time Stationary Processes with Singular Spectral Densities

✍ Mamikon S. Ginovyan; Levon V. Mikaelyan 📂 Article 📅 2008 🏛 Springer Netherlands 🌐 English ⚖ 577 KB

A probability chart for statistical proc

A probability chart for statistical process analysis, with applications

✍ Nina F. Thornhill; Roddy J. Hutchison; Barry T. Haughie 📂 Article 📅 1996 🏛 Elsevier Science 🌐 English ⚖ 655 KB

Calculating spatial statistics for veloc

Calculating spatial statistics for velocity jump processes with experimentally observed reorientation parameters

✍ E.A. Codling; N.A. Hill 📂 Article 📅 2005 🏛 Springer 🌐 English ⚖ 403 KB

First passage time statistics for some s

First passage time statistics for some stochastic processes with superimposed shot noise

✍ Jaume Masoliver; George H. Weiss 📂 Article 📅 1988 🏛 Elsevier Science 🌐 English ⚖ 482 KB

We present a method for finding statistical properties of the first passage time to exit an interval of general diffusion processes subject to random delta function impulses. Exact solutions are found for the mean first passage time for Brownian motion. Other special cases, detailed in the text, can

Statistical analysis and online monitori

Statistical analysis and online monitoring for handling multiphase batch processes with varying durations

✍ Chunhui Zhao; Shengyong Mo; Furong Gao; Ningyun Lu; Yuan Yao 📂 Article 📅 2011 🏛 Elsevier Science 🌐 English ⚖ 825 KB

In the present work, statistical analysis and online monitoring is presented for handling uneven-length multiphase batch processes. Firstly, the irregular batches are classified into different uneven-length groups according to the changes of underlying characteristics. Then multi-source measurement