2019
DOI: 10.1186/s40488-019-0098-y
|View full text |Cite
|
Sign up to set email alerts
|

A new extended normal regression model: simulations and applications

Abstract: Various applications in natural science require models more accurate than well-known distributions. In this context, several generators of distributions have been recently proposed. We introduce a new four-parameter extended normal (EN) distribution, which can provide better fits than the skew-normal and beta normal distributions as proved empirically in two applications to real data. We present Monte Carlo simulations to investigate the effectiveness of the EN distribution using the Kullback-Leibler divergenc… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
5
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
3

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(5 citation statements)
references
References 14 publications
0
5
0
Order By: Relevance
“…The EG family presents wide ranges and various forms of tails such as heavier and lighter thanks to these two additional parameters. It is emphasized that EG can be applied to censored data and it can be used in various fields such as engineering and biology thanks to structure of the flexibility of EG 14 …”
Section: Exponentiated Generalized Tobitmentioning
confidence: 99%
See 3 more Smart Citations
“…The EG family presents wide ranges and various forms of tails such as heavier and lighter thanks to these two additional parameters. It is emphasized that EG can be applied to censored data and it can be used in various fields such as engineering and biology thanks to structure of the flexibility of EG 14 …”
Section: Exponentiated Generalized Tobitmentioning
confidence: 99%
“…It is emphasized that EG can be applied to censored data and it can be used in various fields such as engineering and biology thanks to structure of the flexibility of EG. 14 The PDF is given as:…”
Section: Restricted Parameters Probability Density Functionmentioning
confidence: 99%
See 2 more Smart Citations
“…The data are analyzed on the basis of the following skew-LN regression model where the error terms are independent random variables that assumed to follow the skew-LN distribution, and , are the standardized covariates, which are considered because of the fact that some covariates are measured using different scales. Additionally, the fit under the skew-LN regression model is compared with several regression models, including the regression model based on the beta-normal (BN) distribution [ 7 ], the regression model based on the skewed-normal (SN) distribution [ 26 ], and the extended normal (EN) regression model [ 28 ]. Furthermore, the skew-LN regression model is compared with its nested models, including LN, Exp-N, and normal regression.…”
Section: Applicationsmentioning
confidence: 99%