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How Measurement Fallibility Affects Power and Required Sample Sizes for Several Parametric and Nonparametric Statistics Gibbs Y. Kanyongo Duquesne University kanyongogduqedu Gordon P.
To contrast with parametric methods we will define nonparametric methods.
Applied nonparametric statistics in reliability. 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.
Applied Nonparametric Statistics in Reliability Springer Series in Reliability Engineering - Kindle edition by Gámiz M. B Limnios Nikolaos Lindqvist Bo Henry. Download it once and read it on your Kindle device PC phones or tablets.
Use features like bookmarks note taking and highlighting while reading Applied Nonparametric Statistics in Reliability Springer. 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. Applied nonparametric statistics in reliability Subject. London Springer 2011 Keywords.
Signatur des Originals Print. T 12 B 3460. Digitalisiert von der TIB Hannover 2013.
This book concerns the use of nonparametric statistical tools for the inferences of the performance characteristics of reliability dynamic systems operating in a certain physical environment that determines their behaviour through time. Springer 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.
This book is primarily intended for practitioners and researchers in reliability engineering who faced with reliability data would like to explore the possibility of nonparametric descriptions of underlying probability mechanisms. The book is hence an alternative to much of the current reliability literature which is based on parametric modelling. A series of Monte Carlo experiments were conducted to determine the effect of changes in reliability on parametric and nonparametric statistical methods including the paired samples dependent t test pooled-variance independent t test one-way analysis of variance with three levels Wilcoxon signed-rank test for paired samples and Mann-Whitney-Wilcoxon test for independent groups.
The following non-parametric analysis methods are essentially variations of this concept. The Kaplan-Meier estimator also known as the product limit estimator can be used to calculate values for non-parametric reliability for data sets with multiple failures and suspensions. The equation of the estimator is given by.
Journal of Modern Applied Statistical Methods Volume 6Issue 1 Article 9 5-1-2007 Reliability and Statistical Power. How Measurement Fallibility Affects Power and Required Sample Sizes for Several Parametric and Nonparametric Statistics Gibbs Y. Kanyongo Duquesne University kanyongogduqedu Gordon P.
Brook Ohio University Lydia Kyei-Blankson. Nonparametric statistics is the branch of statistics that is not based solely on parametrized families of probability distributions common examples of parameters are the mean and variance. Nonparametric statistics is based on either being distribution-free or having a specified distribution but with the distributions parameters unspecified.
Nonparametric statistics includes both descriptive statistics and. Nonparametric Methods. To contrast with parametric methods we will define nonparametric methods.
These are statistical techniques for which we do not have to make any assumption of parameters for the population we are studying. Indeed the methods do not have any dependence on the population of interest. We believe the strength of our program lies in our diverse faculty who bring their expertise and real-world applications of mathematical statistics sampling theory survival analysis nonparametric methods applied probability reliability theory re-sampling methods image analysis statistical learning into.
Bayesian Computation Survival Analysis Nonparametric Bayesian Statistics Software Reliability Longitudinal Data Analysis Survey Sampling Bioinformatics and Biostatistics Victor Hugo Lachos.