Local bandwidth selection for kernel density estimation in a bifurcating Markov chain model

DOI10.1080/10485252.2020.1789125zbMath1465.62061arXiv1706.07034OpenAlexW3042692081MaRDI QIDQ4987541

S. Valère Bitseki Penda, Angelina Roche

Publication date: 3 May 2021

Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1706.07034

zbMATH Keywords

adaptive estimationbinary treesbifurcating autoregressive processesnonparametric kernel estimationGoldenshluger-Lepski methodology

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Asymptotic properties of nonparametric inference (62G20) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05) Central limit and other weak theorems (60F05) Population dynamics (general) (92D25) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Large deviations (60F10) Branching processes (Galton-Watson, birth-and-death, etc.) (60J80)

Related Items (3)

Central limit theorem for kernel estimator of invariant density in bifurcating Markov chains models ⋮ Moderate deviation principles for kernel estimator of invariant density in bifurcating Markov chains ⋮ Statistical estimation in a randomly structured branching population

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