Directory UMM :Data Elmu:jurnal:S:Stochastic Processes And Their Applications:Vol92.Issue2.2001:
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Section 3 discusses the asymptotic distribution of (robust) M-estimators of the underlying regression parameters in linear regression models with innite variance long- memory
In this section we will show (i) that the zero-avoiding transition probabilities (3) are just non-coincidence probabilities of a set of independent and dissimilar Poisson pro-
Using this probabilistic tool, we construct an explicit function v solution of an integral equation which is, under some hypotheses on the regularity of v , equivalent to a
Motivated by Barron (1986, Ann. 140, 339 –371), we prove a version of the Lindeberg–Feller Theorem, showing normal convergence of the normalised sum of independent, not
In this section, we use part (DP1) of the dynamic programming principle stated in Proposition 4.1 in order to prove that the value function v(t; s; y) dened in (2.8) is
In this section, we prove a class of lower bounds for 1 ( L ) and an upper bound of the Cheeger’s constant for a reversible Markov chain (discrete or continuous time) on R n
We rst introduce the class of Markov processes with jumps for which one can apply the result of Picard (1996) for the existence of smooth densities and of Picard (1997b) for
This eliminates the need to impose the stronger functional central limit theorem conditions and implies convergence of Dickey–Fuller type unit root tests under minimal conditions..
