Nonparametric Versus Parametric Goodness of Fit

DOI10.1080/02331889808802632zbMath0952.62045OpenAlexW2255717999MaRDI QIDQ3842722

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Publication date: 11 January 2001

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331889808802632

zbMATH Keywords

kernel density estimatormaximum likelihood estimatorlocal alternativesasymptotic powergoodness of fit testsPitman alternativesdensity tests

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Asymptotic properties of nonparametric inference (62G20) Asymptotic properties of parametric tests (62F05)

Related Items (9)

An updated review of goodness-of-fit tests for regression models ⋮ Testing functional inequalities ⋮ Non-parametric \(k\)-sample tests: density functions vs distribution functions ⋮ \(L_2\)-tests for sparse multinomials ⋮ Unnamed Item ⋮ Strong approximation of density estimators from weakly dependent observations by density estimators from independent observations ⋮ Multivariate goodness-of-fit tests based on kernel density estimators ⋮ Goodness-of-Fit Test for Monotone Functions ⋮ Multiscale maximum likelihood analysis of a semiparametric model, with applications.

Cites Work

• Central limit theorem for integrated square error of multivariate nonparametric density estimators
• Semiparametric comparison of regression curves
• The power and optimal kernel of the Bickel-Rosenblatt test for goodness of fit
• A quadratic measure of deviation of two-dimensional density estimates and a test of independence
• Comparing nonparametric versus parametric regression fits
• On some global measures of the deviations of density function estimates

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