A new extended normal regression model: simulations and applications

Lima, Maria do Carmo S.; Cordeiro, Gauss M.; Ortega, Edwin M. M.; Nascimento, Abraão D. C.

doi:10.1186/s40488-019-0098-y

Cited by 3 publications

(5 citation statements)

References 14 publications

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“…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%

“…In this context, main objective of this paper is to propose generalized Tobit models based on the extended normal distribution (END). The END is proposed as a generalization of normal distribution 14 . END, based on the exponentiated generalized family, 15 includes the power normal distribution 16 .…”

Section: Introductionmentioning

confidence: 99%

“…The END is proposed as a generalization of normal distribution. 14 END, based on the exponentiated generalized family, 15 includes the power normal distribution. 16 In this way, the generalized Tobit estimator based on the END (hereafter-Exponentiated Generalized Tobit [EGT]) includes the Alpha-power Tobit (APT).…”

Section: Introductionmentioning

confidence: 99%

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New exponentiated generalized censored regression models: Monte Carlo simulation and application

Yenılmez

Kantar

2022

Concurrency and Computation

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Summary The standard maximum likelihood estimator of censored regression models is constructed under the assumption of the normal distribution and it yields inefficient results in case of non‐normal asymmetric data or/and presence of outlier(s). In the analysis of censored real‐life data encountered in many disciplines such as finance, medical sciences, and engineering, there is often a violation of the assumption of normality. In this paper, we propose a generalization of the censored normal regression based on the extended normal distribution (EGT). The generalized model, with two additional shape parameters as a and b, includes classical Tobit model for a = 0 and b = 0 and Alpha‐Power Tobit model for a = 1. It provides very flexible estimation in cases symmetric‐asymmetric data and especially in case non‐normal asymmetric data or/and presence of outlier(s). In addition, Lehmann type II‐G Tobit model (LTII‐GT) is also proposed for b = 1 as a special case of generalization. The performance of proposed two new generalizations of Tobit, namely EGT and LTII‐GT has been compared with Tobit, Alpha‐Power Tobit, and classical estimators by means of a comprehensive Monte Carlo simulation study. The performance of proposed models' ML estimators is illustrated on a real data set. The superiority of the proposed estimators is shown.

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Section: Exponentiated Generalized Tobitmentioning

confidence: 99%

Section: Restricted Parameters Probability Density Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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New exponentiated generalized censored regression models: Monte Carlo simulation and application

Yenılmez

Kantar

2022

Concurrency and Computation

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“…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%

On the Regression Model for Generalized Normal Distributions

Alzaatreh

Al‐Jarrah

Almagambetova

et al. 2021

Entropy

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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.

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Employment demand and generation under MGNREGA in India: examining external and internal shocks with special reference to climate change and COVID-19 pandemic

Mohanty,

Nayak,

Sahoo

2024

IJSE

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PurposeDespite India achieving many milestones under MGNREGA, the external and internal shocks result in below potential outcomes in employment demand and generation. This study examines how these shocks matter and how the migration-prone regions perform.Design/methodology/approachThis study, employing district-level data from 2018 to 2021, investigates how climate change and COVID-19 have affected the employment demand and supply. We applied RE-GLS and IV-2SLS regressions to examine the effects of shocks on employment demand and generation, respectively. The difference in difference panel model is employed to test the spatial effects of the pandemic. Further, we used RE-GLS and extended regression model to examine how external shocks interacting with migration affect unemployment rates.FindingsIt was found that the pandemic increased employment demand and generation. This reflects the adverse effects of the pandemic and the swift action by the government. However, the responses were possibly different during climate shocks. The wage differential increased employment demand. However, demand decreased due to poor support from the support staff. The employment generation was higher in migration-prone districts, indicating that seasonal migration, being a lean-season phenomenon, continues to occur despite employment generation.Originality/valueThis study contributes to the literature in several ways. It captures spatial variations while examining the impact of climate change and COVID-19. It investigates the performance of MGNREGA in migration-prone areas. In effect, the findings provide policymakers with greater insight into the issues.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2024-0132.

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A new extended normal regression model: simulations and applications

Cited by 3 publications

References 14 publications

New exponentiated generalized censored regression models: Monte Carlo simulation and application

New exponentiated generalized censored regression models: Monte Carlo simulation and application

On the Regression Model for Generalized Normal Distributions

Employment demand and generation under MGNREGA in India: examining external and internal shocks with special reference to climate change and COVID-19 pandemic

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