Statistical analysis of the inherent variability in the results of evolutionary debris models

Abstract

Space debris simulations, e.g. those performed by the Inter-Agency Debris Coordination Committee (Liou et al., 2013), showed that the number of objects in orbit is likely to increase. This study analyses the uncertainty in the results of space debris simulations performed using semi-stochastic models that necessitate the use of Monte Carlo simulations, which are often used by the Inter-Agency Debris Coordination Committee, amongst other studies. Statistics of the possible numbers of objects in orbit and collisions over the next 200 years are generated for the "mitigation only" scenario using a sample of 25 000 Monte Carlo runs. Bootstraps on the mean, median, variance, skewness and kurtosis of these distributions are performed. It is shown that the distribution of the objects predicted to be on-orbit becomes log-normal as collisions occur, and that Monte Carlo samples larger than traditionally used are needed to capture the debris simulation uncertainty.

Inheritance and Changes of Historical Folk Beliefs in Macao Based on Statistical Analysis

“…The statistical analysis method is an emerging statistical method that can synthesize many observations, consider the influence of each variable on the data, process the data, and dig out the useful information in the data through the method of comprehensive analysis, which brings great convenience to the statistical work and provides a realistic possibility to process the data more quickly and efficiently [20][21][22]. The use of statistical analysis methods to study the inheritance and change of folk beliefs can reveal the intrinsic connection between inheritance and change, as well as their importance.…”

Hypervelocity Impact Simulations Considering Space Objects With Various Shapes and Impact Angles

“…7.4: the dark line represents the mean value over all the Monte Carlo runs and the light coloured lines indicate the outcome of the single run. This representation was selected to visualise the variability across the single runs without introducing standard deviation bands as they may be not representative of the result distribution [27].…”