In this paper, we study a nonparametric Bayesian inference on the family of nonincreasing density functions on real positive data. One interesting problem is the goodness of fit test in such a context. In other words, we consider nonparametric Bayesian testing on the family of nonincreasing density in this domain. So, we define nonparametric hypothesis testing and compare two different testing approaches. The first approach is given based on the Bayes factor. This approach is the well-known Bayesian approach for testing, although its computation is complicated. Decision-theoretic considerations with the loss function drive the second approach for a given distance. This second approach has the advantage of considering the distance to the null hypothesis but needs the definition of a threshold. When no threshold is known as a priori, a possibility exists to calculate a p-value, and the method becomes more complicated to compute. We propose a hybrid algorithm to accelerate the computation of the p-value. The comparison of both approaches is performed based on a simulation study.
Khazaei, S. (2024). Goodness of fit tests for nonincreasing densities on real positive data with nonparametric Bayesian methods. Journal of Statistical Modelling: Theory and Applications, 5(1), 15-32. doi: 10.22034/jsmta.2024.21451.1138
MLA
Soleiman Khazaei. "Goodness of fit tests for nonincreasing densities on real positive data with nonparametric Bayesian methods", Journal of Statistical Modelling: Theory and Applications, 5, 1, 2024, 15-32. doi: 10.22034/jsmta.2024.21451.1138
HARVARD
Khazaei, S. (2024). 'Goodness of fit tests for nonincreasing densities on real positive data with nonparametric Bayesian methods', Journal of Statistical Modelling: Theory and Applications, 5(1), pp. 15-32. doi: 10.22034/jsmta.2024.21451.1138
VANCOUVER
Khazaei, S. Goodness of fit tests for nonincreasing densities on real positive data with nonparametric Bayesian methods. Journal of Statistical Modelling: Theory and Applications, 2024; 5(1): 15-32. doi: 10.22034/jsmta.2024.21451.1138
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