arXiv (OAI Expanded)
open access
Dirichlet kernel density estimation for strongly mixing sequences on the simplex
This paper investigates the theoretical properties of Dirichlet kernel density estimators for compositional data supported on simplices, for the first time addressing scenarios involving time-dependent observations characterized by strong mixing conditions. We establish rigorous results for the asym
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Strongly Consistent Estimation of the Extended $\ell_1$-Sum of $ϕ$-Mixing Coefficients from a Single Trajectory#1062132
- Can we further improve the central limit theorem for stationary $ρ$-mixing sequences $?$#927908
- Exploiting Exact Conditionals Improves Conditioning: Provably Fast Mixing Time Bounds By Sampling from the Marginal#781166
- Exploiting Exact Conditionals Improves Conditioning: Provably Fast Mixing Time Bounds By Sampling from the Marginal#779791
- Hierarchical Exponential-Gaussian Mixtures for Watch-Time Distribution Prediction#482126
Record · ID 1082950
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive