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摘要翻译：
在噪声环境中，用一个不完善的仪器从观测到的物体中识别出越来越小的信号，这对统计干净的数据分析提出了挑战。我们想计算在各种数据集中确定的频率相关或不相关的概率，这不能用简单的振幅比较来回答。我们的方法提供了一个统计估计量，在一组观测中具有不同强度的给定信号是工具起源的还是内在的。基于频谱显著性作为频率分析中的无偏统计量，对目标和背景光曲线的离散傅里叶变换进行了比较研究。单独的虚警概率被用来推断目标光谱中的峰值为实的条件概率，尽管背景或比较星的光谱中有相应的峰值。另外，我们可以计算多个数据集的DFT谱中同时出现但振幅不同的频率的联合概率，从而导致合成谱意义。这些对于研究在不同的过滤器中或在几次观测过程中观察到的恒星是有用的。合成谱显着性是对所考虑的DFT谱中没有一个重合峰是由噪声引起的概率的度量。灰姑娘是解决一般统计问题的数学方法。它的潜力超出了地面或空间的光度测量：适用于所有需要对不同数据集的周期性进行定量统计比较的情况。给出了合成灰姑娘模式和条件灰姑娘模式在不同观测设置下的应用实例。
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英文标题：
《Cinderella - Comparison of INDEpendent RELative Least-squares Amplitudes》
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作者：
P. Reegen, M. Gruberbauer, L. Schneider, and W.W. Weiss
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最新提交年份：
2008
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分类信息：
一级分类：Statistics 统计学
二级分类：Computation 计算
分类描述：Algorithms, Simulation, Visualization
算法、模拟、可视化
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一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
The identification of increasingly smaller signal from objects observed with a non-perfect instrument in a noisy environment poses a challenge for a statistically clean data analysis. We want to compute the probability of frequencies determined in various data sets to be related or not, which cannot be answered with a simple comparison of amplitudes. Our method provides a statistical estimator for a given signal with different strengths in a set of observations to be of instrumental origin or to be intrinsic. Based on the spectral significance as an unbiased statistical quantity in frequency analysis, Discrete Fourier Transforms (DFTs) of target and background light curves are comparatively examined. The individual False-Alarm Probabilities are used to deduce conditional probabilities for a peak in a target spectrum to be real in spite of a corresponding peak in the spectrum of a background or of comparison stars. Alternatively, we can compute joint probabilities of frequencies to occur in the DFT spectra of several data sets simultaneously but with different amplitude, which leads to composed spectral significances. These are useful to investigate a star observed in different filters or during several observing runs. The composed spectral significance is a measure for the probability that none of coinciding peaks in the DFT spectra under consideration are due to noise. Cinderella is a mathematical approach to a general statistical problem. Its potential reaches beyond photometry from ground or space: to all cases where a quantitative statistical comparison of periodicities in different data sets is desired. Examples for the composed and the conditional Cinderella mode for different observation setups are presented.
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PDF链接：
https://arxiv.org/pdf/710.2963
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