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1	A Study of Control Charts with Variable Sample Size Huang, Guo-Tai 08 July 2004 (has links) Shewhart X bar control charts with estimated control limits are widely used in practice. When the sample size is not fixed,we propose seven statistics to estimate the standard deviation sigma . These estimators are applied to estimate the control limits of Shewhart X bar control chart. The estimated results through simulated computation are given and discussed. Finally, we investigate the performance of the Shewhart X bar control charts based on the seven estimators of sigma via its simulated average run length (ARL). Contaminated normal distribution Simulation Average run length (ARL) Control charts
2	Observation error model selection by information criteria vs. normality testing Lehmann, Rüdiger 17 October 2016 (has links) (PDF) To extract the best possible information from geodetic and geophysical observations, it is necessary to select a model of the observation errors, mostly the family of Gaussian normal distributions. However, there are alternatives, typically chosen in the framework of robust M-estimation. We give a synopsis of well-known and less well-known models for observation errors and propose to select a model based on information criteria. In this contribution we compare the Akaike information criterion (AIC) and the Anderson Darling (AD) test and apply them to the test problem of fitting a straight line. The comparison is facilitated by a Monte Carlo approach. It turns out that the model selection by AIC has some advantages over the AD test. Maximum-Likelihood-Schätzung Robuste Schätzung Gaußsche Normalverteilung Laplace-Verteilung Verallgemeinerte Normalverteilung Kontaminierte Normalverteilung Akaike's Informationskriterium Anderson-Darling-Test Monte-Carlo-Methode maximum likelihood estimation robust estimation Gaussian normal distribution Laplace distribution generalized normal distribution contaminated normal distribution Akaike information criterion Anderson Darling test Monte Carlo method ddc:500 rvk:ZI 9070
3	Observation error model selection by information criteria vs. normality testing Lehmann, Rüdiger January 2015 (has links) To extract the best possible information from geodetic and geophysical observations, it is necessary to select a model of the observation errors, mostly the family of Gaussian normal distributions. However, there are alternatives, typically chosen in the framework of robust M-estimation. We give a synopsis of well-known and less well-known models for observation errors and propose to select a model based on information criteria. In this contribution we compare the Akaike information criterion (AIC) and the Anderson Darling (AD) test and apply them to the test problem of fitting a straight line. The comparison is facilitated by a Monte Carlo approach. It turns out that the model selection by AIC has some advantages over the AD test. info:eu-repo/classification/ddc/500 ddc:500

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