Author: Odile PonsPublisher:
ISBN: 9782953412215
Category :
Languages : en
Pages : 262
Book Description
Nonparametric Estimation for Renewal and Markov Processes
Author: Odile PonsPublisher:
ISBN: 9782953412215
Category :
Languages : en
Pages : 262
Book Description
Nonparametric Estimation Based on Censored Observation of a Markov Renewal Process
Author: R. D. GillPublisher:
ISBN:
Category : Nonparametric estimation
Languages : en
Pages : 27
Book Description
Nonparametric Estimation Based on Censored Observations of a Markov Renewal Process
Author: Richard D. GillPublisher:
ISBN:
Category : Markov processes
Languages : en
Pages : 27
Book Description
Nonparametric Estimation in Markov Processes
Author: George G. RoussasApplied Nonparametric Statistics in Reliability
Author: M. Luz GámizPublisher: Springer Science & Business Media
ISBN: 0857291181
Category : Technology & Engineering
Languages : en
Pages : 238
Book Description
Nonparametric statistics has probably become the leading methodology for researchers performing data analysis. It is nevertheless true that, whereas these methods have already proved highly effective in other applied areas of knowledge such as biostatistics or social sciences, nonparametric analyses in reliability currently form an interesting area of study that has not yet been fully explored. Applied Nonparametric Statistics in Reliability is focused on the use of modern statistical methods for the estimation of dependability measures of reliability systems that operate under different conditions. The scope of the book includes: smooth estimation of the reliability function and hazard rate of non-repairable systems; study of stochastic processes for modelling the time evolution of systems when imperfect repairs are performed; nonparametric analysis of discrete and continuous time semi-Markov processes; isotonic regression analysis of the structure function of a reliability system, and lifetime regression analysis. Besides the explanation of the mathematical background, several numerical computations or simulations are presented as illustrative examples. The corresponding computer-based methods have been implemented using R and MATLAB®. A concrete modelling scheme is chosen for each practical situation and, in consequence, a nonparametric inference procedure is conducted. Applied Nonparametric Statistics in Reliability will serve the practical needs of scientists (statisticians and engineers) working on applied reliability subjects.
Nonparametric Estimation of Nonincreasing Densities and Use of Data from Renewal Processes
Author: Carleton University. Laboratory for Research in Statistics and ProbabilityPublisher: Laboratory for Research in Statistics and Probability, Carleton University = Laboratoire de recherche en statistique et probabilités, Carleton University,$c1989.
ISBN:
Category : Density functionals
Languages : en
Pages : 66
Book Description
Estimation of Reliability of Repairable Semi-Markov Systems with Finite State Space
Author: LimniosPublisher: Wiley-Blackwell
ISBN: 9781848218901
Category :
Languages : en
Pages :
Book Description
Non-parametric Estimation for Non-homogeneous Semi-Markov Processes: an Application to Credit Bank
Author: André MonteiroNonparametric Estimation for Non-time-homogeneous Markov Processes in the Problem of Competing Risks
Author: Thomas Richard FlemingPublisher:
ISBN:
Category : Markov processes
Languages : en
Pages : 138
Book Description
Nonparametric Estimation in Markov Processes
Author: George C. RoussasPublisher:
ISBN:
Category :
Languages : en
Pages : 13
Book Description
The purpose of the present paper is to consider the non-parametric estimation of densities in the case of Markov processes. Asymptotically unbiased estimates for the initial and (two-dimensional) joint densities are constructed. These estimates are shown to be consistent in quadratic mean, and furthermore a consistent, in the probability sense, estimate for the transition density is obtained. It is shown that, under suitable conditions, all three estimators mentioned, properly normalized, are asymptotically normal.