The simultaneous assessment of normality and homoscedasticity in linear fixed effects models

DOI10.1080/15598608.2017.1320243zbMath1425.62036OpenAlexW2606318693MaRDI QIDQ2321981

Ye Yang, Thomas Mathew

Publication date: 27 August 2019

Published in: Journal of Statistical Theory and Practice (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/15598608.2017.1320243

zbMATH Keywords

score testLegendre polynomialsunbalanced databalanced dataone-way fixed effects modelsmooth alternative

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Parametric hypothesis testing (62F03) Diagnostics, and linear inference and regression (62J20)

Uses Software

• gvlma

Cites Work

• Unnamed Item
• Data-driven smooth tests for a location-scale family revisited
• Data. A collection of problems from many fields for the student and research worker
• Data driven smooth tests for bivariate normality
• Asymptotic optimality of data-driven Neyman's tests for uniformity
• Data driven smooth tests for composite hypotheses
• Consistency and Monte Carlo simulation of a data driven version of smooth goodness-of-fit tests
• Smooth Tests of Goodness of Fit
• Rao score tests for goodness of fit and independence
• Data-Driven Version of Neyman's Smooth Test of Fit
• Data driven smooth tests for composite hypotheses comparison of powers
• Data-Driven Smooth Tests When the Hypothesis Is Composite
• A revisit to test the equality of variances of several populations
• Global Validation of Linear Model Assumptions

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