Generalized p value for multivariate Gaussian stochastic processes in continuous time
- 95 Downloads
We construct a Generalized p value for testing statistical hypotheses on the comparison of mean vectors in the sequential observation of two continuous time multidimensional Gaussian processes. The mean vectors depend linearly on two multidimensional parameters and with different conditions about their covariance structures. The invariance of the generalized p value considered is proved under certain linear transformations. We report results of a simulation study showing power and errors probabilities for them. Finally, we apply our results to a real data set.
KeywordsGeneralized p value Hypothesis testing Continuous time Multivariate Behrens–Fisher problem
Mathematics Subject ClassificationPrimary: 62M09 Secondary: 62H12
The authors thank the anonymous referees for their helpful comments and suggestions. The first and third authors were supported by MTM2015-65825-P
- Johnson NL, Kotz S, Balakrishnan N (1994) Continuous univariate distributions, vol. 1, Wiley Series in Probability and Mathematical Statistics: Applied Probability and Statistics. John Wiley & Sons, Inc., New YorkGoogle Scholar