Hypothesis Testing for Fractional Stochastic Partial Differential Equations with Applications to Neurophysiology and Finance
Asian Research Journal of Mathematics · pp. 1–24 · Published 2 May 2017
10.9734/ARJOM/2017/33094Abstract
The paper obtains explicit form of fine large deviation theorems for the log-likelihood ratio in testing fractional stochastic partial differential equation models using a finite number of Fourier coefficients of the solution. The equation is driven by additive noise that is white in space and colored (fractional) in time with Hurst parameter H ≥ 1/2. It obtains explicit rates of decrease of the error probabilities of Neyman-Pearson, Bayes and minimax tests. Finally, it provides several examples including two practical examples of membrane voltage model from neurophysiology and forward interest rate model from finance.
Cited by 1
Jaya P. N. Bishwal · Parameter Estimation in Stochastic Volatility Models · 2022
Related research
- Standardized Reporting of Statistical Results in APA Format: Enhancing Clarity, Transparency, and Reproducibility in Research — shares topic coverage
- On the Performance of Lottery Winning Strategies: A Case Study of Oyo State Lottery, Nigeria — shares topic coverage
- Utilization, Correlates and Predictors of Contraceptive Use among Married Women in Amassoma, Bayelsa State, Nigeria — shares topic coverage
- Application of Analysis of Variance (ANOVA) in Biological Science Research — shares topic coverage
- Developing Grade 10 Learners’ Hypothetico-deductive Reasoning through Teacher-made Thought Experiments and Hypothesis-testing Activities — shares topic coverage
Article metrics
Real usage data collected on this platform.
0
Page views
0
PDF downloads
0
Outbound clicks
1
Citations
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
No views recorded yet.
Traffic sources
Referring site, by host.
No traffic recorded yet.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.