2021
DOI: 10.1007/s11749-021-00786-8
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A test for heteroscedasticity in functional linear models

Abstract: We propose a new test to validate the assumption of homoscedasticity in a functional linear model. We consider a minimum distance measure of heteroscedasticity in functional data, which is zero in the case where the variance is constant and positive otherwise. We derive an explicit form of the measure, propose an estimator for the quantity, and show that an appropriately standardized version of the estimator is asymptotically normally distributed under both the null (homoscedasticity) and alternative hypothese… Show more

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Cited by 2 publications
(3 citation statements)
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“…Despite this issue, multicollinearity does not lead to bias in fit while determining the equation. Where R is high, above 0.80, the two variables are highly correlated and multicollinearity is likely to become an issue (Cameron & Bagchi, 2021). With the heteroscedasticity in the data noted, the Hausman test would be essential in checking the fixed and random effects and it would help decide on the best model to be used (Cameron & Bagchi, 2021).…”
Section: Diagnostic Testsmentioning
confidence: 99%
See 1 more Smart Citation
“…Despite this issue, multicollinearity does not lead to bias in fit while determining the equation. Where R is high, above 0.80, the two variables are highly correlated and multicollinearity is likely to become an issue (Cameron & Bagchi, 2021). With the heteroscedasticity in the data noted, the Hausman test would be essential in checking the fixed and random effects and it would help decide on the best model to be used (Cameron & Bagchi, 2021).…”
Section: Diagnostic Testsmentioning
confidence: 99%
“…Where R is high, above 0.80, the two variables are highly correlated and multicollinearity is likely to become an issue (Cameron & Bagchi, 2021). With the heteroscedasticity in the data noted, the Hausman test would be essential in checking the fixed and random effects and it would help decide on the best model to be used (Cameron & Bagchi, 2021). From the research by Cameron and Bagchi, 95% confidence level is recommended from the analysis of the previous studies in evaluating the significance of the coefficient.…”
Section: Diagnostic Testsmentioning
confidence: 99%
“…Test is used to know the classical assumption existence of heteroscedasticity. If the significant value is above alpha = 5%, then there is no heteroscedasticity problem (Cameron & Bagchi, 2021;Zhao et al, 2021;Sun & Wang, 2022). Tab.…”
Section: Heteroscedasticity Testmentioning
confidence: 99%