Asymptotic normality of frequency polygons for random fields

DOI10.1016/j.jspi.2009.07.028zbMath1177.62111OpenAlexW2046412645MaRDI QIDQ1039489

Michel Carbon, Christian Francq, Lanh Tat Tran

Publication date: 30 November 2009

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: http://crest.science/RePEc/wpstorage/2008-09.pdf

zbMATH Keywords

asymptotic normalitybandwidthrandom fieldfrequency polygons

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Random fields; image analysis (62M40) Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20)

Related Items (15)

Uniformly complete consistency of frequency polygon estimation for dependent samples and an application ⋮ Asymptotic behaviour of frequency polygons under φ-mixing samples ⋮ Uniformly strong consistency and Berry-Esseen bound of frequency polygons for α-mixing samples ⋮ On the asymptotic normality of frequency polygons for strongly mixing spatial processes ⋮ Frequency polygon estimation of density function for dependent samples ⋮ Consistency of the frequency polygon estimators of density mode for strongly mixing processes ⋮ Frequency polygons for continuous random fields ⋮ Uniformly strong consistency and the rates of asymptotic normality for the edge frequency polygons ⋮ Uniform strong consistency of histogram density estimation for dependent process ⋮ Histograms for stationary linear random fields ⋮ On the uniform consistency of frequency polygons for ρ^--mixing samples ⋮ On the Berry-Esséen bound of frequency polygons for \(\phi\)-mixing samples ⋮ Piecewise linear density estimation for sampled data ⋮ Uniformly strong consistency of frequency polygons for negatively associated samples ⋮ On the uniform consistency of frequency polygons for \(\psi\)-mixing samples

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• Frequency Polygons: Theory and Application
• An Approach to Proving Limit Theorems for Dependent Random Variables
• Kernel density estimation for random fields: TheL₁Theory
• Nonparametric estimation of density, regression and dependence coefficients
• The Invariance Principle for Stationary Processes

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