A triangular central limit theorem under a new weak dependence condition

By Clémentine Coulon-Prieur, Paul Doukhan. 2000

53 citations recorded by the source corpus.

Subfields: Finance, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/70f904e1350e8a2e65df3bc0451f8f266da75e2b

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Citations in the corpus

Weak dependence beyond mixing and asymptotics for nonparametric regression (2002, methodology): “We proceed as in [27] and more specifically as in [9] for density estimation.”
Dependent Lindeberg central limit theorem and some applications (2007, methodology): “By this way, the C.L.T. is established under the same conditions (but with a more simple a nd genral method) than in Coulon-Prieur and Doukhan (2000) for causal processes.”
Probability and moment inequalities for sums of weakly dependent random variables, with applications (2007, unknown): “For example, versions of a central limit theorem are derived in Doukhan and Louhichi (1999) for sequences of random variables, in Coulon-Prieur and Doukhan (2000) for situations as they appear with nonparametric curve estimators, and in Neumann and Paparoditis (2005) for general triangular schemes.”
A central limit theorem for triangular arrays of weakly dependent random variables, with applications in statistics (2013, methodology): “Under conditions of this type, central limit theorems were given in Corollary A in Doukhan and Louhichi [14] for sequences of stationary random variables and in Theorem 1 in Coulon-Prieur and Doukhan [6] for triangular arrays of asymptotically sparse random variables as they appear with nonparametri”
Normal approximation for quasi-associated random fields (2001, methodology): “…to measure the dependence between a single random variable Xj and other Xq, q ∈ Zd, when ‖q − j‖ is “large” (‖j‖ = max1≤k≤d |jk| for j = (j1, . . . , jk) ∈ Zd), cf. Coulon-Prieur and Doukhan (2000), where for a process (d = 1) the dependence between pairs Xi, Xj and all Xq from the “past” was…”