A central limit theorem for decomposable random variables with applications to random graphs
Journal of Combinatorial Theory Series B
Generating functionology
Average-case analysis of algorithms and data structures
Handbook of theoretical computer science (vol. A)
Journal of Combinatorial Theory Series A
Inverse descents of r-multipermutations
Discrete Mathematics
P-partitions and q-Stirling numbers
Journal of Combinatorial Theory Series A
On convergence rates in the central limit theorems for combinatorial structures
European Journal of Combinatorics
Hilbert Polynomials in Combinatorics
Journal of Algebraic Combinatorics: An International Journal
CAAP '92 Proceedings of the 17th Colloquium on Trees in Algebra and Programming
Combinatorics of Permutations
Asymptotic degree distribution in random recursive trees
Random Structures & Algorithms - Proceedings of the Eleventh International Conference "Random Structures and Algorithms," August 9—13, 2003, Poznan, Poland
Level of nodes in increasing trees revisited
Random Structures & Algorithms
The height of increasing trees
Random Structures & Algorithms
Analytic Combinatorics
SIAM Journal on Discrete Mathematics
Generalized Stirling permutations, families of increasing trees and urn models
Journal of Combinatorial Theory Series A
Hi-index | 0.00 |
In this work we give a study of generalizations of Stirling permutations, a restricted class of permutations of multisets introduced by Gessel and Stanley [15]. First we give several bijections between such generalized Stirling permutations and various families of increasing trees extending the known correspondences of [20, 21]. Then we consider several permutation statistics of interest for generalized Stirling permutations as the number of left-to-right minima, the number of left-to-right maxima, the number of blocks of specified sizes, the distance between occurrences of elements, and the number of inversions. For all these quantities we give a distributional study, where the established connections to increasing trees turn out to be very useful. To obtain the exact and limiting distribution results we use several techniques ranging from generating functions, connections to urn models, martingales and Stein's method.