Global ETD Search

1	Testing For Normality of Censored Data Andersson, Johan, Burberg, Mats January 2015 (has links) In order to make statistical inference, that is drawing conclusions from a sample to describe a population, it is crucial to know the correct distribution of the data. This paper focused on censored data from the normal distribution. The purpose of this paper was to answer whether we can test if data comes from a censored normal distribution. This by using normality tests and tests designed for censored data and investigate if we got correct size of these tests. This has been carried out with simulations in the program R for left censored data. The results indicated that with increasing censoring normality tests failed to accept normality in a sample. On the other hand the censoring tests met the requirements with increasing censoring level, which was the most important conclusion in this paper. Censored data normality test Cramer-von Mises test statistic Anderson-Darling test statistic testing for normality
2	Stability Analysis of Hydrodynamic Performance Indicator Based on Historic Data Sets Özel Kennedy, Canan January 2022 (has links) This paper presents a stability analysis of the sensor data which is collected by QTAGG from alarge ocean going ship and using the stability results, introduces some information about howthe measurements that come from the sensors can be improved and how reliable they are. In thetheoretical part, some background information is given mainly based on British Standard(BS)ISO 19030 which was published in November, 2016. This source basically includes someinformation about the measurement of changes in hull and propeller performance of a vessel.Using the theoretical information, in the implementation part, the necessary methods areimplemented in python programming language on a real life data set of a vessel which is givenfrom QTAGG company. To measure the stability of the parameters in the data, we loosenthe filters of the parameters and observe how they respond to the technical changes. In orderto understand how loosen the filters can be made, a reference speed-power curve is createdby using a curve fitting method, and after creating a performance indicator by utilizing thereference curve, Anderson-Darling and Shapiro-Wilk tests are used to measure the stability ofthe performance indicator. Besides these numerical tests, some visual methods such as Q-Qplot and histogram plot are also used in this process. Finally, we could provide stability resultsby using both our theoretical knowledge and the practical implementation. Stability analysis Anderson-Darling test Shapiro-Wilk test curve fitting Q-Q plot British Standard(BS) ISO 19030 Mathematical Analysis Matematisk analys
3	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
4	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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