A Circular Statistical Method for Extracting Rotation Measures

We propose a new method for the extraction of Rotation Measure from spectral polarization data. The method is based on maximum likelihood analysis and takes into account the circular nature of the polarization data. The method is unbiased and statistically more efficient than the standard $\chi^2$ procedure. We also find that the method is computationally much faster than the standard $\chi^2$ procedure if the number of data points are very large.Comment: 17 pages, 5 figure

A statistical method to determine open cluster metallicities

The study of open cluster metallicities helps to understand the local stellar formation and evolution throughout the Milky Way. Its metallicity gradient is an important tracer for the Galactic formation in a global sense. Because open clusters can be treated in a statistical way, the error of the cluster mean is minimized. Our final goal is a semi-automatic statistical robust method to estimate the metallicity of a statistically significant number of open clusters based on Johnson BV data of their members, an algorithm that can easily be extended to other photometric systems for a systematic investigation. This method incorporates evolutionary grids for different metallicities and a calibration of the effective temperature and luminosity. With cluster parameters (age, reddening and distance) it is possible to estimate the metallicity from a statistical point of view. The iterative process includes an intrinsic consistency check of the starting input parameters and allows us to modify them. We extensively tested the method with published data for the Hyades and selected sixteen open clusters within 1000pc around the Sun with available and reliable Johnson BV measurements. In addition, Berkeley 29, with a distance of about 15kpc was chosen. For several targets we are able to compare our result with published ones which yielded a very good coincidence (including Berkeley 29).Comment: 14 pages, 6 figures, accepted for publication in Astronomy & Astrophysic

New statistical method identifes cytokines that distinguish stool microbiomes

Regressing an outcome or dependent variable onto a set of input or independent variables allows the analyst to measure associations between the two so that changes in the outcome can be described by and predicted by changes in the inputs. While there are many ways of doing this in classical statistics, where the dependent variable has certain properties (e.g., a scalar, survival time, count), little progress on regression where the dependent variable are microbiome taxa counts has been made that do not impose extremely strict conditions on the data. In this paper, we propose and apply a new regression model combining the Dirichlet-multinomial distribution with recursive partitioning providing a fully non-parametric regression model. This model, called DM-RPart, is applied to cytokine data and microbiome taxa count data and is applicable to any microbiome taxa count/metadata, is automatically fit, and intuitively interpretable. This is a model which can be applied to any microbiome or other compositional data and software (R package HMP) available through the R CRAN website

Log-based Anomaly Detection of CPS Using a Statistical Method

Detecting anomalies of a cyber physical system (CPS), which is a complex system consisting of both physical and software parts, is important because a CPS often operates autonomously in an unpredictable environment. However, because of the ever-changing nature and lack of a precise model for a CPS, detecting anomalies is still a challenging task. To address this problem, we propose applying an outlier detection method to a CPS log. By using a log obtained from an actual aquarium management system, we evaluated the effectiveness of our proposed method by analyzing outliers that it detected. By investigating the outliers with the developer of the system, we confirmed that some outliers indicate actual faults in the system. For example, our method detected failures of mutual exclusion in the control system that were unknown to the developer. Our method also detected transient losses of functionalities and unexpected reboots. On the other hand, our method did not detect anomalies that were too many and similar. In addition, our method reported rare but unproblematic concurrent combinations of operations as anomalies. Thus, our approach is effective at finding anomalies, but there is still room for improvement

A statistical method to estimate low-energy hadronic cross sections

In this article we propose a model based on the Statistical Bootstrap approach to estimate the cross sections of different hadronic reactions up to a few GeV in c.m.s energy. The method is based on the idea, when two particles collide a so called fireball is formed, which after a short time period decays statistically into a specific final state. To calculate the probabilities we use a phase space description extended with quark combinatorial factors and the possibility of more than one fireball formation. In a few simple cases the probability of a specific final state can be calculated analytically, where we show that the model is able to reproduce the ratios of the considered cross sections. We also show that the model is able to describe proton\,-\,antiproton annihilation at rest. In the latter case we used a numerical method to calculate the more complicated final state probabilities. Additionally, we examined the formation of strange and charmed mesons as well, where we used existing data to fit the relevant model parameters.Comment: 12 pages, 12 figures, submitted to EPJ

A statistical method to estimate low-energy hadronic cross sections

In this article we propose a model based on the Statistical Bootstrap approach to estimate the cross sections of different hadronic reactions up to a few GeV in c.m.s energy. The method is based on the idea, when two particles collide a so called fireball is formed, which after a short time period decays statistically into a specific final state. To calculate the probabilities we use a phase space description extended with quark combinatorial factors and the possibility of more than one fireball formation. In a few simple cases the probability of a specific final state can be calculated analytically, where we show that the model is able to reproduce the ratios of the considered cross sections. We also show that the model is able to describe proton\,-\,antiproton annihilation at rest. In the latter case we used a numerical method to calculate the more complicated final state probabilities. Additionally, we examined the formation of strange and charmed mesons as well, where we used existing data to fit the relevant model parameters.Comment: 12 pages, 12 figures, submitted to EPJ