Mstep_hist function

M step for histograms

M step for histograms estimator

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. & BIRGÉ, L. (2009). Estimating the intensity of a random measure by histogram type estimators. Probab. Theory Related Fields 143, 239–284.

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, 633–661.