Statistical analysis of astro-geodetic data through principal component analysis, linear modelling and bootstrap based inference

The paper demonstrates the application of statistical based methodology for the analysis of the vertical deviation angle. The studied data set contains astro-geodetic observations. The Principal Component Analysis and the Multiple Linear Regression models are embedded within a bootstrap procedure, i...

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Bibliographic Details
Published inarXiv.org
Main Authors Gornea, Andreea Ioana, Calin, Alexandru, Dumitru, Paul Daniel, Nedelcu, Dan Alin, Stoica, Radu Stefan
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 20.09.2018
Subjects
Data correlation
Deviation
Principal components analysis
Regression analysis
Regression models
Statistical analysis
Statistical inference
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Summary:The paper demonstrates the application of statistical based methodology for the analysis of the vertical deviation angle. The studied data set contains astro-geodetic observations. The Principal Component Analysis and the Multiple Linear Regression models are embedded within a bootstrap procedure, in order to overcome the difficulties related to data correlation, while taking advantage of all the information provided. The methodology is applied on real data. The obtained results indicate that the pressure, the temperature and the humidity are variables that may influence the measure of the vertical deviation.
ISSN:2331-8422