๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

A MARKOVIAN APPROACH TO ORDERED PEAK STATISTICS

โœ Scribed by BASU, B.; GUPTA, V. K.; KUNDU, D.


Publisher
John Wiley and Sons
Year
1996
Tongue
English
Weight
818 KB
Volume
25
Category
Article
ISSN
0098-8847

No coin nor oath required. For personal study only.

โœฆ Synopsis


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 the other existing theories and, in special cases, it is shown to lead to the independent order statistics as well as to a first passage problem. Digital simulation has been carried out to validate the analytical results. The effects of governing parameters on the statistics of various orders of peaks have also been studied.


๐Ÿ“œ SIMILAR VOLUMES


A statistical approach to class separabi
โœ Djamel A. Zighed; Stรฉphane Lallich; Fabrice Muhlenbach ๐Ÿ“‚ Article ๐Ÿ“… 2005 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 138 KB
A New Approach to Well-Ordered Quantum D
โœ Gรผnter Schmid; Norbert Beyer ๐Ÿ“‚ Article ๐Ÿ“… 2000 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 557 KB

Gold /

A Probabilistic Approach to the Descent
โœ Richard Ehrenborg; Michael Levin; Margaret A. Readdy ๐Ÿ“‚ Article ๐Ÿ“… 2002 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 117 KB

We present a probabilistic approach to studying the descent statistic based upon a two-variable probability density. This density is log concave and, in fact, satisfies a higher order concavity condition. From these properties we derive quadratic inequalities for the descent statistic. Using Fourier

Estimating moments of the minimum order
โœ Jean-Franรงois Angers; Duncan K. H. Fong ๐Ÿ“‚ Article ๐Ÿ“… 1994 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 487 KB

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