Mstep_hist: M step for histograms

In ppsbm: Clustering in Longitudinal Networks

Description

M step for histograms estimator

Usage

Mstep_hist(data, VE, directed, sparse)

Arguments

data	Data same of mainVEM
VE	Results of the previous VE for iterative computation
directed	Boolean for directed (TRUE) or undirected (FALSE) case
sparse	Boolean for sparse (TRUE) or not sparse (FALSE) case

References

BARAUD, Y. & BIRG<c3><89>, L. (2009). Estimating the intensity of a random measure by histogram type estimators. Probab. Theory Related Fields 143, 239<e2><80><93>284.

MATIAS, C., REBAFKA, T. & VILLERS, F. (2018). A semiparametric extension of the stochastic block model for longitudinal networks. Biometrika.

REYNAUD -BOURET, P. (2006). Penalized projection estimators of the Aalen multiplicative intensity. Bernoulli 12, 633<e2><80><93>661.

ppsbm documentation built on May 1, 2019, 11:26 p.m.