scholarly journals The comparison of the ordinal logistic model with the classical regression model

2021 ◽  
Vol 1731 ◽  
pp. 012033
Author(s):  
N A Yensy
Keyword(s):  
Regression Model ◽  
Logistic Model ◽  
Ordinal Logistic Model ◽  
Classical Regression

scholarly journals Forecasting COVID-19 cases in Algeria using logistic growth and polynomial regression models

2021 ◽  
Author(s):  
Mohamed LOUNIS ◽  
Babu Malavika
Keyword(s):  
Regression Model ◽  
Logistic Model ◽  
Polynomial Regression ◽  
Logistic Growth ◽  
The Novel ◽  
Logistic Growth Model ◽  
Polynomial Regression Models ◽  
Using Data ◽  
Novel Coronavirus ◽  
Polynomial Regression Model

Abstract The novel Coronavirus respiratory disease 2019 (COVID-19) is still expanding through the world since it started in Wuhan (China) on December 2019 reporting a number of more than 84.4 millions cases and 1.8 millions deaths on January 3rd 2021.In this work and to forecast the COVID-19 cases in Algeria, we used two models: the logistic growth model and the polynomial regression model using data of COVID-19 cases reported by the Algerian ministry of health from February 25th to December 2nd, 2020. Results showed that the polynomial regression model fitted better the data of COVID-19 in Algeria the Logistic model. The first model estimated the number of cases on January, 19th 2021 at 387673 cases. This model could help the Algerian authorities in the fighting against this disease.

scholarly journals A note non Nonparametric Regression Modeling using a Density Function

2020 ◽  
Vol 7 (2) ◽  
pp. 993-1000
Author(s):  
Jakperik Dioggban
Keyword(s):  
Regression Model ◽  
Density Function ◽  
Nonparametric Regression ◽  
Model Simulation ◽  
Smoothing Splines ◽  
Bias Reduction ◽  
Simulation Studies ◽  
Lack Of Fit ◽  
Classical Regression ◽  
Better Than

The nonparametric regression offers alternative to classical regression analysis when the data are not well behaved or when the classical regression model shows significant lack of fit. In recent years, It has been applied using Kernel estimators and the smoothing splines, but these methods wields some bias of estimation. In this study, a semi-parametric multiplicative bias reduction density function was used to develop a non parametric regression model. Simulation studies conducted showed that the proposed estimator performs better than both the Kernel and the smoothing splines estimators especially with large samples

scholarly journals Sexual dimorphism of viscerocranium-A logistic model

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Lucas del Vigna Peixoto ◽  
Stefany de Lima Gomes ◽  
Ana Amelia Barbieri ◽  
Francisco Carlos Groppo ◽  
Cristhiane Martins Schmidt ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Sexual Dimorphism ◽  
Regression Model ◽  
Logistic Model ◽  
Quantitative Method ◽  
Logistic Regression Model ◽  
Sexually Dimorphic ◽  
Qualitative And Quantitative ◽  
The Right

Introduction: Sex estimates are generally based on the evaluation of qualitative and quantitative aspects of anatomic structures, however, the latter has better reproducibility and reliability. Objective: Aiming to evaluate the viscerocranium as a tool for sexual prediction and verify the possibility of creation of a logistic regression model for sexual prediction. Materials and Methods: 167 craniums - 100 male and 67 female between 22 and 85 years old from a Brazilian university´s Biobank - were evaluated. Results: It was observed that of the measures carried out were presented as sexually dimorphic, except for the measures of the right frontozygomatic point – right zygion; left frontozygomatic point – left zygion. Besides, it was possible to create a logistic regression model Sex = [logits/Sex = -24.5 + (0.20 * Nasion - Naso spine) + (0.18 * Right zygion - Naso spine)]. Conclusion: It was concluded that the measures of the viscerocranium present themselves as a factor of sexual dimorphism and the quantitative method developed was 81.4% accurate.

scholarly journals Detection of Influential Observations in Spatial Regression Model Based on Outliers and Bad Leverage Classification

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2030
Author(s):  
Ali Mohammed Baba ◽  
Habshah Midi ◽  
Mohd Bakri Adam ◽  
Nur Haizum Abd Rahman
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Spatial Regression ◽  
Spatial Models ◽  
Influential Observations ◽  
Leverage Points ◽  
Cook’S Distance ◽  
Spatial Regression Models ◽  
Cook's Distance ◽  
Classical Regression

Influential observations (IOs), which are outliers in the x direction, y direction or both, remain a problem in the classical regression model fitting. Spatial regression models have a peculiar kind of outliers because they are local in nature. Spatial regression models are also not free from the effect of influential observations. Researchers have adapted some classical regression techniques to spatial models and obtained satisfactory results. However, masking or/and swamping remains a stumbling block for such methods. In this article, we obtain a measure of spatial Studentized prediction residuals that incorporate spatial information on the dependent variable and the residuals. We propose a robust spatial diagnostic plot to classify observations into regular observations, vertical outliers, good and bad leverage points using a classification based on spatial Studentized prediction residuals and spatial diagnostic potentials, which we refer to as and . Observations that fall into the vertical outliers and bad leverage points categories are referred to as IOs. Representations of some classical regression measures of diagnostic in general spatial models are presented. The commonly used diagnostic measure in spatial diagnostics, the Cook’s distance, is compared to some robust methods, (using robust and non-robust measures), and our proposed and plots. Results of our simulation study and applications to real data showed that the Cook’s distance, non-robust and robust were not very successful in detecting IOs. The suffered from the masking effect, and the robust suffered from swamping in general spatial models. Interestingly, the results showed that the proposed plot, followed by the plot, was very successful in classifying observations into the correct groups, hence correctly detecting the real IOs.

scholarly journals Rethinking Westernization in Destination: Tourists’ Perception of a Touristic City

2021 ◽  
Vol 12 (23) ◽  
pp. 1-25
Author(s):  
Niranjan Devkota ◽  
Udaya Raj Poudel ◽  
Iveta Hamarneh ◽  
Udbodh Bhandari
Keyword(s):  
Regression Model ◽  
Logistic Model ◽  
Descriptive Study ◽  
Local Culture ◽  
Cross Sectional ◽  
Cultural Traditions ◽  
Logit Regression ◽  
Tourism Destinations ◽  
Logit Regression Model

The impacts of westernization are increasing globally in the tourism entrepreneurship practices. Understanding it contributes to the growth and sustainability of the business even in local touristic cities. This paper aims to judge tourists’ perception of westernization about one of the most important touristic cities – Pokhara, Nepal. Purposive sampling was used to collect responses from 248 tourists in Pokhara, which included both open and closed-ended questionnaires. In order to understand the perception of tourists and check the determinants about the prevalence of westernization among tourists, the cross-sectional descriptive study has been used, and Logit Regression Model is applied. The study reveals that 78.22% of the respondents find westernization has influenced tourism entrepreneurship up to a certain extent in Pokhara. Similarly, a majority (89.11%) of tourists reveal that they expect and enjoy local culture than their own culture in tourism destinations, where 95.56% of the tourists suggest preserving the local culture for the sustainability of tourism business in Pokhara. Results from the Ordered Logistic model show that westernization, problems faced in destination, the similarity of destination as per their expectation and level of tourists’ existence at destination play significant roles in their preferences to visit touristic destinations. This study indicates that the first two reduce tourists’ preferences while the latter two stimulate their preferences to visit Pokhara, Nepal. Therefore, entrepreneurs in Pokhara should identify, conserve, encourage, and maintain local socio-cultural traditions to have long-term tourism prosperity and development.

scholarly journals Relationship Between Perceived In-Cabin Air Quality and Truck Drivers' Self-Reported Health and Alertness

2010 ◽  
Vol 47 (4) ◽  
Author(s):  
Richard Tay ◽  
Zhongchao Tan ◽  
Xiaoying Cheng
Keyword(s):  
Air Quality ◽  
Logistic Model ◽  
Truck Drivers ◽  
Reported Health ◽  
Cabin Air Quality ◽  
Ordinal Logistic Model ◽  
Medical Symptoms

This study surveyed 253 truck drivers and found that many drivers scored poorly on the Stanford and Epworth sleepiness scales indicating that they may not be as alert as they should be while driving. Moreover, those who rated the air in their truck cabins as fresh reported less irritation to their eyes, noses, throats, and skin, scored better in both sleepiness scales, and reported fewer sleep-related medical symptoms. Finally, the results of the ordinal logistic model indicate that drivers' perceptions of the air quality in their truck cabins are significantly related to their alertness during a trip.

A multivariate logistic regression model based on 3T mpMRI, clinical, and biopsy parameters for the prediction of prostate cancer extracapsular extension.

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 27-27
Author(s):  
Sohrab Afshari Mirak ◽  
Steven Raman
Keyword(s):  
Prostate Cancer ◽  
Radical Prostatectomy ◽  
Regression Model ◽  
Predictive Value ◽  
Logistic Model ◽  
Extracapsular Extension ◽  
Adc Value ◽  
Model Based ◽  
Multivariate Logistic Model ◽  
Patient Size

27 Background: Extracapsular extension (ECE) of prostate cancer (PCa) is a poor prognostic factor associated with progression, recurrence after treatment and increased PCa- related mortality. Accurate staging prior to radical prostatectomy is crucial in avoidance of positive margins and when planning nerve-sparing procedures. This study investigated the predictive value of clinical, biopsy & 3TmpMRI parameters using a multivariate logistic model for per-lesion detection of PCa ECE with wholemount histopathology (WMHP) as reference. Methods: This IRB approved, HIPAA compliant study included 575 patients with 774 true positive PCa lesions, who underwent radical prostatectomy between 7/2010-2/2019. The relationship between pathologic ECE & parameters including clinical; age, prostate specific antigen (PSA) & PSA density (PSAD), biopsy; percentage of positive systematic cores & Gleason score (GS) & 3TmpMRI; prostate volume, number of lesions per patient, size, location, level, PIRADSv2 score, laterality, apparent diffusion coefficient (ADC) value & risk of ECE on MRI was evaluated using bivariate and multivariate analysis. The accuracy of the final model was evaluated using ROC analysis. Results: 27.8% (215/774), 42.9% (332/774) & 29.3% (227/774) of the lesions were PIRADSv2 score 3, 4 & 5 & 59.9% (464/774), 24.7% (191/774) & 17.7% (137/774) were low, intermediate & high risk for ECE, respectively. 23.6% (183/774) of the lesions had ECE on WMHP. On bivariate analysis higher PSA, PSAD, percentage of positive biopsy cores, biopsy GS, size, PIRADSv2 score, ADC value, risk of ECE on MRI, location (posterior), level(midgland & base), bilaterality & lower number of lesions per patient were significant for ECE prediction. The multivariate logistic model included age, PSAD, number of lesions per patient, size, location, level, PIRADSv2 score & risk of ECE on MRI. The AUC for the prediction of ECE for this model was 0.85. Conclusions: This multivariate regression model based on clinical, biopsy & 3TmpMRI parameters have a high predictive value for pathology ECE detection.

Missing Posterior Teeth and Risk of Temporomandibular Disorders

2009 ◽  
Vol 88 (10) ◽  
pp. 942-945 ◽  
Author(s):  
M.Q. Wang ◽  
F. Xue ◽  
J.J. He ◽  
J.H. Chen ◽  
C.S. Chen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Young Women ◽  
Temporomandibular Disorders ◽  
Logistic Model ◽  
Logistic Regression Model ◽  
Age And Gender ◽  
Posterior Teeth ◽  
Men 1 ◽  
And Gender

There is disagreement about the association between missing posterior teeth and the presence of temporomandibular disorders (TMD). Here, the purpose was to investigate whether the number of missing posterior teeth, their distribution, age, and gender are associated with TMD. Seven hundred and forty-one individuals, aged 21–60 years, with missing posterior teeth, 386 with and 355 without TMD, were included. Four variables—gender, age, the number of missing posterior teeth, and the number of dental quadrants with missing posterior teeth—were analyzed with a logistic regression model. All four variables—gender (OR = 1.59, men = 1, women = 2), age (OR = 0.98), the number of missing posterior teeth (OR = 0.51), and the number of dental quadrants with missing posterior teeth (OR = 7.71)—were entered into the logistic model (P < 0.01). The results indicate that individuals who lose posterior teeth, with fewer missing posterior teeth but in more quadrants, have a higher prevalence of TMD, especially young women.

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