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Statistical Inference for Processes Depending on
Environments and Application in Regenerative Processes
Christine Jacob, Nadia Lalam, Nicolas Yanev
Chr. Jacob, Applied Mathematics and Informatics unity,
INRA,
78352 Jouy-en-Josas Cedex, France
N. Lalam, EURANDOM, P.O.Box 513, 5600 MB Eindhoven, The Netherlands
Nickolay Yanev, Institute of Mathematics and Informatics, BAS,
Acad. G. Bontchev Str. 1113 Sofia, Bulgaria
email: cj@banian.jouy.inra.fr yanev@math.bas.bg
We consider a process {Zn}{n in N},
recursively defined by Zn = f(Fn-1,En) +
h n, where Fn-1={Zk}k
£ n-1, E{n}={Ck}k£
n, {Cn}n is an observed exogenous process and {h
n}n is a martingale difference sequence for the
filtration generated by (Fn-1, En) such that Var(h
n|Fn-1,En)g(Fn-1,En)
< ¥ , a.s. for some known function {g(Fn-1,En)}n.
This class of models covers a very broad range of models such as regression
models, ANOVA models, autoregressive processes, branching processes,
regenerative processes, ... We assume that f(Fn-1,En)
depends on an unknown parameter m 0 and
that by notation f(.)= fm0(.)
may be decomposed according to fm0(.)=f(1)q0(.)
+ f(2)m0(.), where
q 0 in R d, dz <
¥ , is asymptotically identifiable in f(1)q0(.)
as n ® ¥ at some
rate v(.) whereas f(2)m0(.)v(.)
is asymptotically negligible. We build the Conditional Least Squares Estimator
of q 0 based on the observation of a
single trajectory of {Zk,Ck}k, and give
conditions ensuring its strong consistency. The particular case of general
linear models according to m 0=(q0,n0)
and among them, regenerative processes, are studied more particularly. In this
frame, we may also prove the consistency of the estimator of n
0 although it belongs to an asymptotic negligible part of the model,
and the asymptotic law of the estimator may also be calculated.
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