๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

An application of SPRT for detecting change point in a reliability growth model

โœ Scribed by Sudha Jain; R.K. Jain


Publisher
Elsevier Science
Year
1994
Tongue
English
Weight
108 KB
Volume
34
Category
Article
ISSN
0026-2714

No coin nor oath required. For personal study only.


๐Ÿ“œ SIMILAR VOLUMES


Change-point methods for Weibull models
โœ V. K. Jandhyala; S. B. Fotopoulos; N. Evaggelopoulos ๐Ÿ“‚ Article ๐Ÿ“… 1999 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 185 KB ๐Ÿ‘ 2 views

We develop change-point methodology for identifying dynamic trends in the scale and shape parameters of a Weibull distribution. The methodology includes asymptotics of the likelihood ratio statistic for detecting unknown changes in the parameters as well as asymptotics of the maximum likelihood esti

A Markov Chain Model of Population Growt
โœ Professor C. Lefevre ๐Ÿ“‚ Article ๐Ÿ“… 1988 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 455 KB ๐Ÿ‘ 3 views

This paper is concerned with a class of population growth procesees in discrete time; the simple epidemic process is considered as a specific example. A Markov chain model is constructed and standard Markov methods are used to study the main biological concepts. A simple and explicit formula is obta

A change point model for estimating the
โœ Charles B. Hall; Richard B. Lipton; Martin Sliwinski; Walter F. Stewart ๐Ÿ“‚ Article ๐Ÿ“… 2000 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 146 KB ๐Ÿ‘ 2 views

Dementia is characterized by accelerated cognitive decline before and after diagnosis as compared to normal ageing. Determining the time at which that rate of decline begins to accelerate in persons who will develop dementia is important both in describing the natural history of the disease process

MINIMISATION OF DECISION ERRORS IN A PRO
โœ G.M. LLOYD; M.L. WANG; T.L. PAEZ ๐Ÿ“‚ Article ๐Ÿ“… 1999 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 298 KB

Probabilistic neural nets have been applied in the detection of structural damage. These networks, which rely upon approximating the multivariate density of the training data, have been shown to be e!ective in some applications. However, quanti"cation of decision errors, which must ultimately be use