Data driven smooth tests for bivariate normality
type: Item
instance of: scholarly article
title: Data driven smooth tests for bivariate normality
zbMATH Open document ID: 0945.62060
author: Małgorzata Bogdan
published in: Journal of Multivariate Analysis
publication date: 1999-05-31T00:00:00Z
review text: Two versions of data driven smooth tests for bivariate normality are proposed. The author derives the asymptotic null distribution and consistency of the tests. Some results of a simulation study are also given.
Mathematics Subject Classification ID: 62H15
Mathematics Subject Classification ID: 62G10
Mathematics Subject Classification ID: 62E20
Mathematics Subject Classification ID: 62G20
Mathematics Subject Classification ID: 65C05
zbMATH DE Number: 1271180
zbMATH Keywords: Schwarz's BIC criterion
zbMATH Keywords: score test
reviewed by: Songgui Wang
MaRDI profile type: Publication
OpenAlex ID: W1964834657
cites work: Data driven versions of pearson's chisquare test for uniformity
cites work: Adaptive Smoothing and Density-Based Tests of Multivariate Normality
cites work: Testing multivariate normality
cites work: Tests of Linearity, Multivariate Normality and the Adequacy of Linear Scores
cites work: Testing for normality in arbitrary dimension
cites work: Q5603716
cites work: Testing goodness of fit via nonparametric function estimation techniques
cites work: Asymptotic comparison of Cramér-von Mises and nonparametric function estimation techniques for testing goodness-of-fit
cites work: Q4199360
cites work: Q4188597
cites work: Q5344608
cites work: Q4307739
cites work: Data driven smooth tests for composite hypotheses
cites work: Q4246938
cites work: Asymptotic optimality of data-driven Neyman's tests for uniformity
cites work: Consistency and Monte Carlo simulation of a data driven version of smooth goodness-of-fit tests
cites work: Q4884621
cites work: Data-Driven Smooth Tests When the Hypothesis Is Composite
cites work: Data driven smooth tests for composite hypotheses comparison of powers
cites work: Q4384063
cites work: A class of invariant procedures for assessing multivariate normality
cites work: Q3319613
cites work: Assessing multivariate normality: a compendium
cites work: An alternative formulation of Neyman's smooth goodness of fit tests under composite alternatives
cites work: Data-Driven Version of Neyman's Smooth Test of Fit
cites work: Measures of multivariate skewness and kurtosis with applications
cites work: Q4114625
cites work: Q3920454
cites work: Q3998440
cites work: Q5568451
cites work: Estimating the dimension of a model
cites work: An analysis of variance test for normality (complete samples)
cites work: Q3787271
cites work: Comparison of tests for bivariate normality with unknown parameters by transformation to an univariate statistic
DOI: 10.1006/JMVA.1998.1779
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description: scientific article; zbMATH DE number 1271180