scholarly journals The Spatial Predicting of COVID-19 Incidence and Its Mortality Based On OLS and GWR Models in Iran

2021 ◽  
Author(s):  
Abolfazl Ghanbari ◽  
Behzad Baradaran ◽  
Hamed Ahmadi ◽  
Maryam Ahmadi
Keyword(s):  
Mortality Rate ◽  
Infection Rate ◽  
Large Population ◽  
Information Criterion ◽  
Ordinary Least Squares ◽  
Older Individuals ◽  
Variance Inflation Factor ◽  
Independent Variables ◽  
Inflation Factor ◽  
Substantial Rise

Abstract Background: Within six months of the COVID-19 outbreak, 350279 people were infected, and 20125 people died of COVID-19 in Iran. There is an urgent need to find the most accurate effective indicators on this disease's outbreak in order to control and predict. Methods: We examined the effect of 36 demographic, economic, environmental, health infrastructure, social, and topographic independent variables on the COVID-19 infection and mortality rates using the ordinary least squares (OLS) model in ArcGIS 10.5. Regarding adjusted R-squared>0/7, we selected 20 variables for COVID-19 infection rate and 16 variables for the mortality rate. The collinearity problem between the selected variables resolved after using the variance inflation factor (VIF). Then, we performed the OLS and geographically weighted regression (GWR) models in ArcGIS 10.5.Results: Having a large number of men, having a large population, lack of specialist doctors, lack of hospital, having a large urban population, having a large number of people aged 65 and over or older individuals, and high natural mortality rate had the most prominent impact on the COVID-19 infection increasing rate. Also, lack of ICU beds, low number of insured people, lack of subspecialist physicians, and lack of hospital beds had the most prominent impact on increasing of COVID-19 mortality. Then the variables with VIF above 7.5 were removed and finally, high incoming immigrants rate and lack of nurses were identified as two independent variables to predict COVID-19 infection rate. In addition, high incoming immigrants rate and high number of doctor consultation were recognized as two variables to predict mortality rate due to COVID-19. The results of the Akaike information criterion (AIC) and adj.R2 showed that both models were appropriate for these analyses.Conclusions: Based on our results, there would be a considerable increase in COVID-19 infection in Kerman, Esfahan, and Kermanshah provinces. In addition, there would be a remarkable decrease in COVID-19 infection in Khuzestan, Lorestan, Azarbayjan Shargi, and Tehran provinces. Regarding COVID-19 mortality, there would be a substantial rise in Fars and Khorasan Razavi provinces. Moreover, our analyses predicted a considerable diminish in COVID-19 mortality in Tehran, Ardebil, Zanjan, Gilan, Golestan, Lorestan, Khuzestan, Bushehr, and Hormozgan provinces.

scholarly journals The VIF and MSE in Raise Regression

Mathematics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 605 ◽  
Author(s):  
Román Salmerón Gómez ◽  
Ainara Rodríguez Sánchez ◽  
Catalina García García ◽  
José García Pérez
Keyword(s):  
Least Squares ◽  
Mean Square Error ◽  
Ordinary Least Squares ◽  
Least Squares Estimation ◽  
Mean Square ◽  
Variance Inflation Factor ◽  
Inflation Factor ◽  
The Mean

The raise regression has been proposed as an alternative to ordinary least squares estimation when a model presents collinearity. In order to analyze whether the problem has been mitigated, it is necessary to develop measures to detect collinearity after the application of the raise regression. This paper extends the concept of the variance inflation factor to be applied in a raise regression. The relevance of this extension is that it can be applied to determine the raising factor which allows an optimal application of this technique. The mean square error is also calculated since the raise regression provides a biased estimator. The results are illustrated by two empirical examples where the application of the raise estimator is compared to the application of the ridge and Lasso estimators that are commonly applied to estimate models with multicollinearity as an alternative to ordinary least squares.

scholarly journals A Qualitative Study of Time Overrun of Completed Road Projects Awarded by the Niger Delta Development Commission in the Niger Delta Region of Nigeria

2021 ◽  
Vol 5 (1) ◽  
pp. 271-280
Author(s):  
F. N. Ogbeide ◽  
J. O. Ehiorobo ◽  
O. C. Izinyon ◽  
I. R. Ilaboya
Keyword(s):  
Qualitative Study ◽  
Niger Delta ◽  
Delta Region ◽  
Variance Inflation Factor ◽  
Independent Variables ◽  
Niger Delta Region ◽  
Review Period ◽  
Skewness Coefficient ◽  
Road Projects ◽  
Inflation Factor

Time overrun of completed road projects awarded by the Niger Delta Development Commission (NDDC) in the Niger Delta Region of Nigeria from its inception in 2000 up to 2015 was studied. Out of 3315 roads awarded, only 1081 roads representing 31.65 percent were completed within the review period. The qualitative study was carried out on randomly selected completed 162 road projects for analysis, and a conceptual model of time series was developed. In developing the regression model, both dependent and independent variables were subjected to normality tests assessed by skewness coefficient, kurtosis value, Jarque-Bera test, residual probability plot, heteroscedasticity test and the variance inflation factor. Also, with knowledge of total road projects awarded by the Commission, it is now possible to predict proportions of roads experiencing schedule overruns.

scholarly journals Two seemingly paradoxical results in linear models: the variance inflation factor and the analysis of covariance

2021 ◽  
Vol 9 (1) ◽  
pp. 1-8
Author(s):  
Peng Ding
Keyword(s):  
Linear Model ◽  
Linear Models ◽  
Ordinary Least Squares ◽  
Analysis Of Covariance ◽  
Variance Inflation Factor ◽  
Standard Linear ◽  
Inflation Factor ◽  
Effect Estimation ◽  
The Cost ◽  
Paradoxical Results

Abstract A result from a standard linear model course is that the variance of the ordinary least squares (OLS) coefficient of a variable will never decrease when including additional covariates into the regression. The variance inflation factor (VIF) measures the increase of the variance. Another result from a standard linear model or experimental design course is that including additional covariates in a linear model of the outcome on the treatment indicator will never increase the variance of the OLS coefficient of the treatment at least asymptotically. This technique is called the analysis of covariance (ANCOVA), which is often used to improve the efficiency of treatment effect estimation. So we have two paradoxical results: adding covariates never decreases the variance in the first result but never increases the variance in the second result. In fact, these two results are derived under different assumptions. More precisely, the VIF result conditions on the treatment indicators but the ANCOVA result averages over them. Comparing the estimators with and without adjusting for additional covariates in a completely randomized experiment, I show that the former has smaller variance averaging over the treatment indicators, and the latter has smaller variance at the cost of a larger bias conditioning on the treatment indicators. Therefore, there is no real paradox.

scholarly journals EP21 An observational study of the predictors of osteoporosis in men

Rheumatology ◽  
2020 ◽  
Vol 59 (Supplement_2) ◽  
Author(s):  
Natalia Kyrtata ◽  
Kristen Davies ◽  
Arvind Nune ◽  
Marwan Bukhari
Keyword(s):  
Lumbar Spine ◽  
Femoral Neck ◽  
Information Criterion ◽  
Fat Percentage ◽  
Variance Inflation Factor ◽  
Future Studies ◽  
Osteoporosis In Men ◽  
Multiple Regression Models ◽  
Spine Model ◽  
Inflation Factor

Abstract Background Osteoporosis is under-recognised and under-treated in men. Previous studies and stratification tools have been limited in their generalisability and while predictors of poor bone density in men have been characterised, evidence has been contradictory. The purpose of this study is to investigate these predictors in the lumbar spine and femoral neck in men. Methods Data were collected from 3,901 men referred for DEXA scanning to the Royal Lancaster Infirmary, between 2004 and 2010. BMD was measured in the lumbar spine and femoral neck. Simple and multiple regression models were fitted to each measurement site, using each of the known predictors for poor bone health. The Variance Inflation Factor was used to quantify the severity of multicollinearity and the Akaike Information Criterion was employed to select co-variates. Results The following predictors had a significant effect in both measurement sites: age; height; weight; Body Mass Index (BMI); X-ray evidence of osteopenia; and previous fracture(s). Body fat percentage had a significant effect at the lumbar spine only, and tobacco use had a significant effect at the femoral neck only. The adjusted R squared values for the models ranged from 0.16 for the lumbar spine model, to 0.28 for the femoral neck model. Conclusion This study demonstrated that further work is needed in assessing the predictors of OP in men. This would open up an interesting observation in clinical practice and provide an opportunity for risk prediction and primary prevention of OP in men, reducing fracture risk. Future studies should include randomised samples and propensity score matching. Disclosures N. Kyrtata None. K. Davies None. A. Nune None. M. Bukhari None.

scholarly journals PENANGANAN MULTIKOLINEARITAS DENGAN MENGGUNAKAN ANALISIS REGRESI KOMPONEN UTAMA PADA KASUS IMPOR BERAS DI PROVINSI SULUT

2018 ◽  
Vol 18 (1) ◽  
pp. 18
Author(s):  
Mega Sriningsih ◽  
Djoni Hatidja ◽  
Jantje D Prang
Keyword(s):  
Regression Analysis ◽  
Principal Component Regression ◽  
Principal Component ◽  
Equation Model ◽  
Coefficient Of Determination ◽  
Custom Duty ◽  
Variance Inflation Factor ◽  
Independent Variables ◽  
North Sulawesi ◽  
Inflation Factor

PENANGANAN MULTIKOLINEARITAS DENGAN MENGGUNAKAN ANALISIS REGRESI KOMPONEN UTAMA PADA KASUS IMPOR BERAS DI PROVINSI SULUT                   ABSTRAKMultikolinearitas adalah suatu kondisi dimana terjadi korelasi antara variabel bebas atau antar variabel bebas tidak bersifat saling bebas. Besaran yang dapat digunakan untuk mendeteksi adanya multikolinearitas adalah faktor inflasi ragam (Variance Inflation Factor / VIF). Tujuan dari penelitian ini yakni untuk mengetahui cara mengatasi multikolinearitas, menentukan model persamaan regresi komponen utama, dan mengetahui variabel-variabel yang mempengaruhi impor beras di SULUT. Penelitian menggunakan data impor beras di Sulawesi Utara pada tahun 2006-2015. Data akan di analisis menggunakan analisis regresi komponen utama. Analisis regresi komponen utama dapat mengatasi masalah multikolinearitas pada data impor beras di Sulawesi Utara dimana terlihat nilai VIF pada regresi komponen utama bernilai satu untuk semua variabel komponen utama. Berdasarkan hasil analisis regresi komponen utama diperoleh model  = 48258,1804 + 0,006739247 X1 - 0,92939626 X2 - 0,06475365 X3 - 0,38551398 X4 + 0,0001233267 X5 + 5,365936 X6 + 0,0006384361 X7 + 0.0005029473 X8 - 3,25379897 X9 + 0,01069348 X10 dan koefisien determinasi (R2) = 90,36% dan nilai (Radj) = 85,53%. Semua variabel yaitu Produksi beras Sulawesi Utara , stok beras di Sulawesi Utara , luas panen padi Sulawesi utara , penerimaan beras dari dalam negeri sulawesi utara , devisa impor paid pada bea dan cukai Bitung , produk domestik regional bruto atas dasar harga berlaku menurut lapangan usaha di Sulawesi Utara , pendapatan pajak daerah Sulawesi Utara , penggunaan devisa impor unpaid pada bea dan cukai Bitung , kurs , dan jumlah penduduk Sulawesi Utara  mempengaruhi impor beras di SULUT (Y).Kata Kunci :  Multikolinearitas, Regresi Komponen Utama, Variance Inflation Factor (VIF), Impor Beras. MULTICOLLINEARITY HANDLING USING PRINCIPAL COMPONENTSREFRESSION ON IMPORTED RICE CASE INNORTH SULAWESI PROVINCE ABSTRACT           Multicolinearity is a condition where there is correlation between independent variables or between independent variables that are not mutually free. The quantity that can be used to detect the presence of multicollinearity is Variance Inflation Factor (VIF). The purpose of this research is to determine the equation model of regression principal component, and to know the variables that influence on rice import in SULUT. The study used data of rice imports in North Sulawesi in 2006-2015. The data will be analyzed using regression analysis of principal components. Regression analysis of principal component can overcome the problem of multicollinearity in rice import data in North Sulawesi  where seen VIF value at regression of principal component is one for all principal component variable. Based on the analysis results of regression principal component, has obtained the model Y = 48258,1804 + 0,006739247 X1 - 0,92939626 X2 - 0,06475365 X3 - 0,38551398 X4 + 0,0001233267 X5 + 5,365936 X6 + 0,0006384361 X7 + 0.0005029473 X8 - 3,25379897 X9 + 0,01069348 X10  and coefficient of determination (R2) = 90,36% and value (Radj) = 85,53%. All variables i.e North Sulawesi rice production (X1), rice stock in North Sulawesi (X2), harvested area of North Sulawesi (X3), domestic rice revenues from north Sulawesi (X4), import duty paid of Bitung’s custom duty and excise (X5 ), gross regional domestic product of current prices by business field in North Sulawesi (X6), North Sulawesi tax revenues (X7), unpaid import duties on customs duty and excise (X8), exchange rate (X9), and population North Sulawesi (X10) affects the import of rice in SULUT (Y).Keywords  :  Multicolinearity, Principal Component Regression, Variance Inflation Factor (VIF), Rice Import.

scholarly journals Robust Multicollinearity Diagnostic Measure For Fixed Effect Panel Data Model

2021 ◽  
Vol 17 (5) ◽  
pp. 636-646
Author(s):  
Shelan Saied Ismaeel ◽  
Habshah Midi ◽  
Muhammed Sani
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Ordinary Least Squares ◽  
Fixed Effect ◽  
Diagnostic Methods ◽  
Panel Data Model ◽  
Variance Inflation Factor ◽  
Leverage Points ◽  
Inflation Factor ◽  
Erroneous Interpretation

It is now evident that high leverage points (HLPs) can induce the multicollinearity pattern of a data in fixed effect panel data model. Those observations that are responsible for this phenomenon are called high leverage collinearity-enhancing observations (HLCEO). The commonly used within group ordinary least squares (WOLS) estimator for estimating the parameters of fixed effect panel data model is easily affected by HLCEOs. In their presence, the WOLS estimates may produce large variances and this would lead to erroneous interpretation. Therefore, it is imperative to detect the multicollinearity which is caused by HLCEOs. The classical Variance Inflation Factor (CVIF) is the commonly used diagnostic method for detecting multicollinearity in panel data. However, it is not correctly diagnosed multicollinearity in the presence of HLCEOs. Hence, in this paper three new robust diagnostic methods of diagnosing multicollinearity in panel data are proposed, namely the RVIF (WGM-FIMGT), RVIF (WGM-DRGP) and RVIF (WMM) and compared their performances with the CVIF. The numerical evidences show that the CVIF incorrectly diagnosed multicollinearity but our proposed methods correctly diagnosed no multicollinearity in the presence of HLCEOs where RVIF (WGM-FIMGT) being the best method as it has the least computational running time.

Author response for "Variable selection in support vector regression using angular search algorithm and variance inflation factor"

2020 ◽  
Author(s):  
Gabriely S. Folli ◽  
Márcia H.C. Nascimento ◽  
Ellisson H. de Paulo ◽  
Pedro H.P. da Cunha ◽  
Wanderson Romão ◽  
...  
Keyword(s):  
Variable Selection ◽  
Support Vector Regression ◽  
Search Algorithm ◽  
Author Response ◽  
Support Vector ◽  
Variance Inflation Factor ◽  
Inflation Factor

scholarly journals Comparison of muscle quality and functional capacity between Japanese and Brazilian older individuals

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243589
Author(s):  
Hiroshi Akima ◽  
Akito Yoshiko ◽  
Régis Radaelli ◽  
Madoka Ogawa ◽  
Kaori Shimizu ◽  
...  
Keyword(s):  
Adipose Tissue ◽  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Muscle Quality ◽  
Older Individuals ◽  
Stepwise Multiple Linear Regression ◽  
Independent Variables

Muscle quality is well-known to decrease with aging and is a risk factor for metabolic abnormalities. However, there is a lack of information on race-associated differences in muscle quality and other neuromuscular features related to functional performance. This study aimed to compare muscle quality, function, and morphological characteristics in Japanese and Brazilian older individuals. Eighty-four participants aged 65–87 years were enrolled in the study (42 Japanese: 23 men, 19 women, mean age 70.4 years; 42 Brazilians: 23 men, 19 women, mean age 70.8 years). Echo intensity (EI) and muscle thickness (MT) of the quadriceps femoris were measured using B-mode ultrasonography. A stepwise multiple linear regression analysis with EI as a dependent variable revealed that MT was a significant variable for Japanese participants (R2 = 0.424, P = 0.001), while MT and subcutaneous adipose tissue (SCAT) thickness were significant variables for Brazilian participants (R2 = 0.490, P = 0.001). A second stepwise multiple linear regression analysis was performed after excluding MT and SCAT thickness from the independent variables. Sex and age for Japanese participants (R2 = 0.381, P = 0.001) and lean body mass and body mass index for Brazilian participants (R2 = 0.385, P = 0.001) were identified as significant independent variables. The present results suggest that MT is closely correlated with muscle quality in Japanese and Brazilian older individuals. Increases in muscle size may induce decreases in intramuscular adipose tissue and/or connective tissues, which are beneficial for reducing the risks of metabolic impairments in Japanese and Brazilian older individuals.

scholarly journals The level and trend of road traffic injuries attributable mortality rate in Iran, 1990–2015: a story of successful regulations and a roadmap to design future policies

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Mehran Shams ◽  
Farnam Mohebi ◽  
Kimiya Gohari ◽  
Masoud Masinaei ◽  
Bahram Mohajer ◽  
...  
Keyword(s):  
Mortality Rate ◽  
Road Traffic ◽  
National Level ◽  
Mortality Rates ◽  
Road User ◽  
Attributable Mortality ◽  
Older Individuals ◽  
Registration System ◽  
Helmet Use ◽  
Road Traffic Injuries

Abstract Background Road-Traffic-Injuries (RTIs) are predicted to rise up to the fifth leading cause of worldwide death by 2030 and Iran has the third highest RTIs mortality among higher-middle income countries. Although the high mortality of RTI in Iran is a warning, it provides the opportunity to indirectly assess the implemented RTI-related regulations’ effectiveness via high-resolution relevant statistics and, hence, Iran could serve as a guide for countries with similar context. In order to do so, we utilized this study to report the time and spatial trends of RTIs-related mortality in different age and sex groups and road user classes in Iran. Methods Based on the national death-registration-system (DRS), cemeteries data, and the demographic characteristics, and after addressing incompleteness, we estimated mortality rates using spatiotemporal and Gaussian process regression models. We assessed Pearson seatbelt and helmet use and RTIs-attributable Age-Standardized-Morality-Rate (ASMR) associations. We also predicted RTIs-death-numbers, 2012–2020, by fitting a Generalized Additive Model to assess the status of achieving relevant sustainable development goal (SDG), namely reducing the number of RTIs-related deaths by half. Results Overall RTIs-attributable death and ASMR at the national level increased from 12.64 [95% UI, 9.52–16.86] to 29.1 [22.76–37.14] per 100,000 people in the time period of 1990–2015. The trend consisted of an increasing segment in 1990–2003 followed by a decreasing part till 2015. The highest percentage of death belonged to the three-or-more-wheels motorized vehicles. Pedestrian injuries percentage increased significantly and the highest mortality rate occurred in 85 years and older individuals. Low prevalence of seatbelt and helmet use were observed in provinces with higher than the median ASMR due to the relevant cause of each. RTIs-attributable death number is expected to reduce by 15.99% till 2020 which is lower than the established SDG goal. Conclusions Despite the observed substantial moderation in the RTI-ASMR, Iran is till among the leading countries in terms of the highest mortality rates in the world. The enforced regulations including speed limitations (particularly for elder pedestrians) and mandatory use of seatbelt and helmet (for young adult and male drivers) had a considerable effect on ASMR, nevertheless, the RTI burden reduction needs to be sustained and enhanced.

Sign in / Sign up

Export Citation Format

Share Document