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| author_facet |
Alzaatreh, Ayman Aljarrah, Mohammad Almagambetova, Ayanna Zakiyeva, Nazgul Alzaatreh, Ayman Aljarrah, Mohammad Almagambetova, Ayanna Zakiyeva, Nazgul |
|---|---|
| author |
Alzaatreh, Ayman Aljarrah, Mohammad Almagambetova, Ayanna Zakiyeva, Nazgul |
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Alzaatreh, Ayman Aljarrah, Mohammad Almagambetova, Ayanna Zakiyeva, Nazgul Entropy On the Regression Model for Generalized Normal Distributions General Physics and Astronomy |
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alzaatreh, ayman |
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Alzaatreh, Ayman Aljarrah, Mohammad Almagambetova, Ayanna Zakiyeva, Nazgul 1099-4300 MDPI AG General Physics and Astronomy http://dx.doi.org/10.3390/e23020173 <jats:p>The traditional linear regression model that assumes normal residuals is applied extensively in engineering and science. However, the normality assumption of the model residuals is often ineffective. This drawback can be overcome by using a generalized normal regression model that assumes a non-normal response. In this paper, we propose regression models based on generalizations of the normal distribution. The proposed regression models can be used effectively in modeling data with a highly skewed response. Furthermore, we study in some details the structural properties of the proposed generalizations of the normal distribution. The maximum likelihood method is used for estimating the parameters of the proposed method. The performance of the maximum likelihood estimators in estimating the distributional parameters is assessed through a small simulation study. Applications to two real datasets are given to illustrate the flexibility and the usefulness of the proposed distributions and their regression models.</jats:p> On the Regression Model for Generalized Normal Distributions Entropy |
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10.3390/e23020173 |
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MDPI AG, 2021 |
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MDPI AG, 2021 |
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1099-4300 |
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2021 |
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MDPI AG |
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| title |
On the Regression Model for Generalized Normal Distributions |
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On the Regression Model for Generalized Normal Distributions |
| title_full |
On the Regression Model for Generalized Normal Distributions |
| title_fullStr |
On the Regression Model for Generalized Normal Distributions |
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On the Regression Model for Generalized Normal Distributions |
| title_short |
On the Regression Model for Generalized Normal Distributions |
| title_sort |
on the regression model for generalized normal distributions |
| topic |
General Physics and Astronomy |
| url |
http://dx.doi.org/10.3390/e23020173 |
| publishDate |
2021 |
| physical |
173 |
| description |
<jats:p>The traditional linear regression model that assumes normal residuals is applied extensively in engineering and science. However, the normality assumption of the model residuals is often ineffective. This drawback can be overcome by using a generalized normal regression model that assumes a non-normal response. In this paper, we propose regression models based on generalizations of the normal distribution. The proposed regression models can be used effectively in modeling data with a highly skewed response. Furthermore, we study in some details the structural properties of the proposed generalizations of the normal distribution. The maximum likelihood method is used for estimating the parameters of the proposed method. The performance of the maximum likelihood estimators in estimating the distributional parameters is assessed through a small simulation study. Applications to two real datasets are given to illustrate the flexibility and the usefulness of the proposed distributions and their regression models.</jats:p> |
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| author | Alzaatreh, Ayman, Aljarrah, Mohammad, Almagambetova, Ayanna, Zakiyeva, Nazgul |
| author_facet | Alzaatreh, Ayman, Aljarrah, Mohammad, Almagambetova, Ayanna, Zakiyeva, Nazgul, Alzaatreh, Ayman, Aljarrah, Mohammad, Almagambetova, Ayanna, Zakiyeva, Nazgul |
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| description | <jats:p>The traditional linear regression model that assumes normal residuals is applied extensively in engineering and science. However, the normality assumption of the model residuals is often ineffective. This drawback can be overcome by using a generalized normal regression model that assumes a non-normal response. In this paper, we propose regression models based on generalizations of the normal distribution. The proposed regression models can be used effectively in modeling data with a highly skewed response. Furthermore, we study in some details the structural properties of the proposed generalizations of the normal distribution. The maximum likelihood method is used for estimating the parameters of the proposed method. The performance of the maximum likelihood estimators in estimating the distributional parameters is assessed through a small simulation study. Applications to two real datasets are given to illustrate the flexibility and the usefulness of the proposed distributions and their regression models.</jats:p> |
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| imprint | MDPI AG, 2021 |
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| institution | DE-Ch1, DE-L229, DE-D275, DE-Bn3, DE-Brt1, DE-Zwi2, DE-D161, DE-Gla1, DE-Zi4, DE-15, DE-Pl11, DE-Rs1, DE-105, DE-14 |
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| spelling | Alzaatreh, Ayman Aljarrah, Mohammad Almagambetova, Ayanna Zakiyeva, Nazgul 1099-4300 MDPI AG General Physics and Astronomy http://dx.doi.org/10.3390/e23020173 <jats:p>The traditional linear regression model that assumes normal residuals is applied extensively in engineering and science. However, the normality assumption of the model residuals is often ineffective. This drawback can be overcome by using a generalized normal regression model that assumes a non-normal response. In this paper, we propose regression models based on generalizations of the normal distribution. The proposed regression models can be used effectively in modeling data with a highly skewed response. Furthermore, we study in some details the structural properties of the proposed generalizations of the normal distribution. The maximum likelihood method is used for estimating the parameters of the proposed method. The performance of the maximum likelihood estimators in estimating the distributional parameters is assessed through a small simulation study. Applications to two real datasets are given to illustrate the flexibility and the usefulness of the proposed distributions and their regression models.</jats:p> On the Regression Model for Generalized Normal Distributions Entropy |
| spellingShingle | Alzaatreh, Ayman, Aljarrah, Mohammad, Almagambetova, Ayanna, Zakiyeva, Nazgul, Entropy, On the Regression Model for Generalized Normal Distributions, General Physics and Astronomy |
| title | On the Regression Model for Generalized Normal Distributions |
| title_full | On the Regression Model for Generalized Normal Distributions |
| title_fullStr | On the Regression Model for Generalized Normal Distributions |
| title_full_unstemmed | On the Regression Model for Generalized Normal Distributions |
| title_short | On the Regression Model for Generalized Normal Distributions |
| title_sort | on the regression model for generalized normal distributions |
| title_unstemmed | On the Regression Model for Generalized Normal Distributions |
| topic | General Physics and Astronomy |
| url | http://dx.doi.org/10.3390/e23020173 |