Biometrika

By Chairunnisa Murniati, R. Pujihasvuty, Sri Lilestina Nasution, Hilma Amrullah. 1902

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Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity (2019, methodology): “For each dataset, we split it into training, validation, and testing sets by 6:2:2, and report the averaged Mean Squared Error (mse), Kendall’s Rank Correlation Coefficient (τ ) [Kendall, 1938], and Precision at 10 (p@10) to test the ranking performance.”
Phaser crystallographic software (2007, background): “For acentric structure factors these are defined through the multivariate complex normal distribution (Wooding, 1956), P Fð Þ ¼ Rj j 1exp FHR 1F ; ð13Þ where F is a column vector, FH is a row vector of its complex conjugate (the Hermitian transpose) and R is the covariance matrix with elements ij…”
Community detection in graphs (2009, methodology): “Gfeller and De Los Rios have proposed coarse-graining schemes that keep the properties of dynamic processes acting on the graph, like random walks (Gfeller and De Los Rios, 2007) and synchronization (Gfeller and De Los Rios, 2008). systems (Simon, 1962).”
Generalized M‐fluctuation tests for parameter instability (2007, background): “Keywords: fluctuation test, functional central limit theorem, generalized linear model, M-estimation, parameter instability, structural change.”
Highly clustered scale-free networks. (2001, background): “The model shows stylized features of real-world networks: power law distribution of degree, linear preferential attachment of new links and a negative correlation between the age of a node and its link attachment rate.”
Application of the complex multivariate normal distribution to crystallographic methods with insights into multiple isomorphous replacement phasing. (2003, unknown): “…for the real and imaginary parts of the structure factors are calculated and it is shown that hA i A j i 9 hB i B j i and hA i B j i 9 À hA j B i i. Wooding (1956) showed that if the real and imaginary components of a set of complex numbers have a multivariate Gaussian distribution with the…”
Equivariance and invariance properties of multivariate quantile and related functions, and the role of standardisation (2010, background): “…= u. (5) from Equation (5), QS(u, F ) is obtained by inverting the map x → E{S(x − X)}, from which it is seen that spatial quantiles are a special case of the ‘M-quantiles’ introduced by Breckling and Chambers (1988) and also treated by Koltchinskii (1997) and Breckling, Kokic, and Lübke (2001).”
Forecasting Currency Volatility: A Comparison of Implied Volatilities and AR(FI)MA Models (2004, background): “Granger and Joyeux (1980) and Hosking (1981) 3 Similar decay patterns are also found for the logarithm of realized volatility by ABDL (2001) for exchange rate markets and by ABDE (2001) and Areal and Taylor (2002) for stock markets. introduced a flexible class of long memory processes, called…”
Partial robust M-regression (2005, background): “Moreover, extreme leverage points can easily be detected by a robust principal components analysis, as in [11] or [12].”