A theory based on Markovian principles and transition probability description is presented here to predict the statistics of the ordered peaks in a random process. It takes into account the statistical dependence that exists between the peaks in a single time history. The theory is more general than
A Fuzzy Kth ordered statistic
β Scribed by P. A. Schaefer; H. B. Mitchell; D. D. Estrakh
- Publisher
- John Wiley and Sons
- Year
- 1999
- Tongue
- English
- Weight
- 139 KB
- Volume
- 14
- Category
- Article
- ISSN
- 0884-8173
No coin nor oath required. For personal study only.
β¦ Synopsis
Robust signal processing algorithms rely on integer ordered statistics. This means that such algorithms cannot be used in a fuzzy environment where variables have fuzzy ranks. In this paper, we show how to calculate the kth order statistic for a set of arguments with fuzzy rank and thereby use classical robust signal processing algorithms in a fuzzy environment. The paper concludes with a simple application of using the fuzzy kth ordered statistic to filtering a noisy signal.
π SIMILAR VOLUMES
This paper addresses a family of probability models for the failure time process known as order statistics models. Conventional order statistics models make rather strong distributional assumptions about the detection times: typically they assume that these come from some parametric family of distri
Let Yi -N(Bi, o?), i = 1, . . . , p, be independently distributed, where Bi and of are unknown. A Bayesian approach is used to estimate the first two moments of the minimum order statistic, W = min( Y I , . . . , Y,). In order to compute the Bayes estimates, one has to evaluate the predictive densit