Analysis of Admittance Data: Comparison of a Parametric and a Nonparametric Method
โ Scribed by J. Winterhalter; D.G. Ebling; D. Maier; J. Honerkamp
- Publisher
- Elsevier Science
- Year
- 1999
- Tongue
- English
- Weight
- 137 KB
- Volume
- 153
- Category
- Article
- ISSN
- 0021-9991
No coin nor oath required. For personal study only.
โฆ Synopsis
The thermal relaxation times are characteristic parameters of deep levels which can be calculated by the analysis of admittance data. The contributions of these characteristic parameters can be sharp or broadened. If sharp contributions are assumed the analysis procedure is called a parametric method. This procedure leads to a wellposed inverse problem but additionally the unknown number of discrete contributions must be determined. For broadened contributions a nonparametric method is used. This procedure leads to an ill-posed inverse problem but the number of contributions is determined automatically. Both kinds of analysis methods are compared with a Monte Carlo study on simulated admittance data. In addition, the parametric and nonparametric procedures are used to analyze experimental admittance data in order to obtain the deep levels and electrical properties of a semi-insulating GaAs Schottky diode.
๐ SIMILAR VOLUMES
## Abstract For calculation of outlier reference intervals, by definition nonparametric statistics are applied, while for mean value reference intervals parametric or nonparametric statistics can be used. The aim of this study was to compare the mean value reference intervals and their sensitivity
There are often longitudinal data in clinical research, where parametric methods cannot be used because of categorical response and/or small sample sizes. propose a generalization of the nonparametric log-rank and Gehan-Wilcoxon tests. Their tests are also asymptotically distribution-free and can h
We derived three parametric survival models (the log-normal, log logit, and Weibull) from the clinical data of chemotherapy trials for stage II breast cancer. We then used these models to generate simulated survival data, which we analysed using both parametric (log-normal) and non-parametric (logra