A Multivariate CLT for Decomposable Random Vectors with Finite Second Moments
By Martin Raič. 2004
47 citations recorded by the source corpus.
Subfields: Management Science and Operations Research, Mathematical Physics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/05b87a3f48a07e744cbe829516dcc71214a4f694
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Management Science and Operations Research
In Mathematical Physics
- Gesine Reinert
- Louis H. Y. Chen
- Adrian Röllin
- Elizabeth Meckes
- Giovanni Peccati
- Mark Newman
- Ivan Nourdin
- Robert E. Gaunt
In Statistics and Probability
- Gesine Reinert
- Adrian Röllin
- Elizabeth Meckes
- Sourav Chatterjee
- Louis H. Y. Chen
- Giovanni Peccati
- Ivan Nourdin
- Robert E. Gaunt
Citations in the corpus
- Multivariate normal approximation with Stein’s method of exchangeable pairs under a general linearity condition (2007, result): “This reflects the fact that we make use of Lemma 2.6, which is motivated by Goldstein and Rinott (1996), whereas Chatterjee and Meckes (2008) prove new bounds on the solutions of (2.4), but only for Σ = Id; see also Raič (2004) for similar results.”
- A NEW METHOD OF NORMAL APPROXIMATION (2006, unknown): “For a different proof of the bound on ‖φ‖∞, see Lemma 1 in [33].”
- On Stein's method for multivariate normal approximation (2009, methodology): “This general definition of Mk is a departure from what was done by Raič in [15]; there, smoothness conditions on functions are also given in coordinate-independent ways, and M1 and M2 are defined as they are here, but in case k = 3, the quantity M3 is defined as the Lipschitz constant of the Hessia”
- Stein couplings for normal approximation (2010, methodology): “(6.6) {83} (see Stein (1972) and Raič (2004)).”
- MULTIVARIATE NORMAL APPROXIMATION USING EXCHANGEABLE PAIRS (2007, unknown): “Introduction Stein’s method was introduced by Charles Stein (1972) as a tool for proving central limit theorems for sums of dependent random variables.”