184,105 research outputs found
Poisson Network Autoregression
We consider network autoregressive models for count data with a non-random
time-varying neighborhood structure. The main methodological contribution is
the development of conditions that guarantee stability and valid statistical
inference. We consider both cases of fixed and increasing network dimension and
we show that quasi-likelihood inference provides consistent and asymptotically
normally distributed estimators. The work is complemented by simulation results
and a data example
Composite Likelihood Inference by Nonparametric Saddlepoint Tests
The class of composite likelihood functions provides a flexible and powerful
toolkit to carry out approximate inference for complex statistical models when
the full likelihood is either impossible to specify or unfeasible to compute.
However, the strenght of the composite likelihood approach is dimmed when
considering hypothesis testing about a multidimensional parameter because the
finite sample behavior of likelihood ratio, Wald, and score-type test
statistics is tied to the Godambe information matrix. Consequently inaccurate
estimates of the Godambe information translate in inaccurate p-values. In this
paper it is shown how accurate inference can be obtained by using a fully
nonparametric saddlepoint test statistic derived from the composite score
functions. The proposed statistic is asymptotically chi-square distributed up
to a relative error of second order and does not depend on the Godambe
information. The validity of the method is demonstrated through simulation
studies
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