Handling multi-collinearity using principal component analysis with the panel data model

When designing a statistical model, applied researchers strive to make the model consistent, unbiased, and efficient. Labor productivity is an important economic indicator that is closely linked to economic growth, competitiveness, and living standards within an economy. This paper proposes the one-way error component panel data model for labor productivity. One of the problems that we can encounter in panel data is the problem of multi-collinearity. Therefore, multi-collinearity problem is considered. This problem has been detected. After that, the principal component technique is used to get new good unrelated estimators. For the purposes of our analysis, the multi-collinearity problem between the explanatory variables was examined, using principal component techniques with the application of the panel data model focused on the impact of public capital, private capital stock, labor, and state unemployment rate on gross state products. The analysis was based on three estimation methods: fixed effect, random effect, and pooling effect. The challenge is to get estimators with good properties under reasonable assumptions and to ensure that statistical inference is valid throughout robust standard errors. And after application, fixed effect estimation turned out to play a key role in the estimation of panel data models. Based on the results of hypothesis testing, the real data result showed that the fixed effect model was more accurate compared to the two models of random effect and pooling effect. In addition, robust estimation was used to get more efficient estimators since heteroscedasticity has been confirme

The cerebral network of COVID-19-related encephalopathy: a longitudinal voxel-based 18F-FDG-PET study

International audienc

Low incidence of SARS-CoV-2, risk factors of mortality and the course of illness in the French national cohort of dialysis patients

International audienceThe aim of this study was to estimate the incidence of COVID-19 disease in the French national population of dialysis patients, their course of illness and to identify the risk factors associated with mortality. Our study included all patients on dialysis recorded in the French REIN Registry in April 2020. Clinical characteristics at last follow-up and the evolution of COVID-19 illness severity over time were recorded for diagnosed cases (either suspicious clinical symptoms, characteristic signs on the chest scan or a positive reverse transcription polymerase chain reaction) for SARS-CoV-2. A total of 1,621 infected patients were reported on the REIN registry from March 16th, 2020 to May 4th, 2020. Of these, 344 died. The prevalence of COVID-19 patients varied from less than 1% to 10% between regions. The probability of being a case was higher in males, patients with diabetes, those in need of assistance for transfer or treated at a self-care unit. Dialysis at home was associated with a lower probability of being infected as was being a smoker, a former smoker, having an active malignancy, or peripheral vascular disease. Mortality in diagnosed cases (21%) was associated with the same causes as in the general population. Higher age, hypoalbuminemia and the presence of an ischemic heart disease were statistically independently associated with a higher risk of death. Being treated at a selfcare unit was associated with a lower risk. Thus, our study showed a relatively low frequency of COVID-19 among dialysis patients contrary to what might have been assumed