Goodness-of-fit tests based on P—P probability plots
Technometrics
A test of normality with high uniform power
Computational Statistics & Data Analysis
Likelihood-ratio tests for normality
Computational Statistics & Data Analysis
On the choice of the smoothing parameter for the BHEP goodness-of-fit test
Computational Statistics & Data Analysis
Information importance of predictors: Concept, measures, Bayesian inference, and applications
Computational Statistics & Data Analysis
An affine invariant multiple test procedure for assessing multivariate normality
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
Statistical models are often based on normal distributions and procedures for testing such distributional assumption are needed. Many goodness-of-fit tests are available. However, most of them are quite insensitive in detecting non-normality when the alternative distribution is symmetric. On the other hand all the procedures are quite powerful against skewed alternatives. A new test for normality based on a polynomial regression is presented. It is very effective in detecting non-normality when the alternative distribution is symmetric. A comparison between well known tests and this new procedure is performed by simulation study. Other properties are also investigated.