Testing statistical hypothesis on random trees and applications to the protein classification problem

DOI10.1214/08-AOAS218zbMath1166.62079arXivmath/0603378WikidataQ105584241 ScholiaQ105584241MaRDI QIDQ2270660

Ana Georgina Flesia, Sebastian P. Grynberg, Jorge R. Busch, Florencia Leonardi, Pablo A. Ferrari, Ricardo Fraiman

Publication date: 29 July 2009

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/math/0603378

zbMATH Keywords

hypothesis testingrandom treesprotein classificationvariable length Markov chains

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of graph theory (05C90) Random graphs (graph-theoretic aspects) (05C80) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Biochemistry, molecular biology (92C40)

Related Items (10)

A Note on Distinguishing Random Trees Populations ⋮ Overview of object oriented data analysis ⋮ Nonparametric statistical inference for the context tree of a stationary ergodic process ⋮ Limit theorems for sequences of random trees ⋮ Detecting renewal states in chains of variable length via intrinsic Bayes factors ⋮ Coupling and perturbation techniques for categorical time series ⋮ A note on density estimation for binary sequences ⋮ Context tree selection: a unifying view ⋮ Variable length memory chains: characterization of stationary probability measures ⋮ Perfect simulation of processes with long memory: A “coupling into and from the past” algorithm

Uses Software

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Cites Work

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• Limit theorems for sequences of random trees
• The power of amnesia: Learning probabilistic automata with variable memory length
• Metric models for random graphs
• Variable length Markov chains
• Object oriented data analysis: sets of trees
• Alignment Uncertainty and Genomic Analysis
• Context tree estimation for not necessarily finite memory processes, via BIC and MDL
• A universal data compression system
• Exponential inequalities for VLMC empirical trees

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