On the Folded Normal Distribution
By Michail Tsagris, Christina Beneki, Hossein Hassani. 2014
317 citations recorded by the source corpus.
The characteristic function of the folded normal distribution and its moment function are derived. The entropy of the folded normal distribution and the Kullback–Leibler from the normal and half normal distributions are approximated using Taylor series. The accuracy of the results are also assessed using different criteria. The maximum likelihood estimates and confidence intervals for the parameters are obtained using the asymptotic theory and bootstrap method. The coverage of the confidence intervals is also examined.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/d67fdad6c36e8e1f5f3fecea0aac7a368204b705
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In Statistics and Probability
Citations in the corpus
- High‐dimensional quantile regression: Convolution smoothing and concave regularization (2021, unknown): “Note that |Gu| follows a folded normal distribution (Leone et al., 1961) with mean E|Gu| = (2∕𝜋)1∕2he−u2∕(2h2) + u{1 − 2Φ(−u∕h)}.”
- $L1$ -Norm Batch Normalization for Efficient Training of Deep Neural Networks (2018, background): “Then, Y = |X − E(X)| has a folded normal distribution [40].”
- Jump detection with wavelets for high-frequency financial time series (2014, methodology): “disentangling the spot volatility of the underlying process (σt ) from the microstructure noise (η) is a challenging task, since σ ∗ t is the only information available (See Zhang et al. (2005) and Zhang (2006)). However, in the case of the jump detection, it is equivalent to have a larger spot vola”