Weighted skewness and kurtosis unbiased by sample size and Gaussian uncertainties

Central moments and cumulants are often employed to characterize the distribution of data. The skewness and kurtosis are particularly useful for the detection of outliers, the assessment of departures from normally distributed data, automated classification techniques and other applications. Estimators of higher order moments that are robust against outliers are more stable but might miss characteristic features of the data, as in the case of astronomical time series exhibiting brief events like stellar bursts or eclipses of binary systems, while weighting can help identify reliable measurements from uncertain or spurious outliers. Furthermore, noise is an unavoidable part of most measurements and their uncertainties need to be taken properly into account during the data analysis or biases are likely to emerge in the results, including basic descriptive statistics. This work provides unbiased estimates of the weighted skewness and kurtosis moments and cumulants, corrected for biases due to sample size and Gaussian noise, under the assumption of independent data. A comparison of biased and unbiased weighted estimators is illustrated with simulations as a function of sample size and signal-to-noise ratio, employing different data distributions and weighting schemes related to measurement uncertainties and the sampling of the signal. Detailed derivations and figures of simulation results are presented in the Appendices available online.