We propose a new method for estimating common factors of multiple time series. One distinctive feature of the new approach is that it is applicable to some nonstationary time series. The unobservable, nonstationary factors are identified by expanding the white noise space step by step, thereby solving a high-dimensional optimization problem by several low-dimensional sub-problems. Asymptotic properties of the estimation are investigated. The proposed methodology is illustrated with both simulated and real datasets.

In environmental research, missing data are often a challenge for statistical modeling. This paper addressed some advanced techniques to deal with missing values in a data set measuring air quality using a multiple imputation (MI) approach. MCAR, MAR, and NMAR missing data techniques are applied to the data set. Five missing data levels are considered: 5%, 10%, 20%, 30%, and 40%. The imputation method used in this paper is an iterative imputation method, missForest, which is related to the random forest approach. Air quality data sets were gathered from five monitoring stations in Kuwait, aggregated to a daily basis. Logarithm transformation was carried out for all pollutant data, in order to normalize their distributions and to minimize skewness. We found high levels of missing values for $N{O}_2$ (18.4%), $CO$ (18.5%), $P{M}_{10}$ (57.4%), $S{O}_2$ (19.0%), and $O_3$ (18.2%) data. Climatological data (i.e., air temperature, relative humidity, wind direction, and wind speed) were used as control variables for better estimation. The results show that the MAR technique had the lowest RMSE and MAE. We conclude that MI using the missForest approach has a high level of accuracy in estimating missing values. MissForest had the lowest imputation error (RMSE and MAE) among the other imputation methods and, thus, can be considered to be appropriate for analyzing air quality data.

Аbstrасt: Introduction: This study aims to investigate in‐hоsрitаl mоrtаlity in severe асute resрirаtоry syndrоme соrоnаvirus 2 раtients strаtified by serum ferritin levels.Methods: Patients were stratified based on ferritin levels (ferritin levels ≤ 1000 or >1000).Results: Approximately 89% (118) of the patients with ferritin levels > 1000 had pneumonia, and 51% (67) had hypertension. Fever (97, 73.5%) and shortness of breath (80, 61%) were two major symptoms among the patients in this group. Logistic regression analysis indicated that ferritin level (odds ratio [OR] = 0.36, 95% confidence interval [CI] = 0.21–0.62; p < .001), male sex (OR = 2.63, 95% CI = 1.43–5.06; p = .003), hypertension (OR = 4.16, 95% CI = 2.42–7.36; p < .001) and pneumonia (OR = 8.48, 95% CI = 3.02–35.45; p < .001) had significance in predicting in‐hospital mortality. Additionally, the Cox proportional hazards analysis and Kaplan–Meier survival probability plot showed a higher mortality rate among patients with ferritin levels > 1000.Conclusion: In this study, higher levels of serum ferritin were found to be an independent predictor of in‐hоsрitаl mоrtаlity.

Rheumatoid arthritis (RA) is a chronic autoimmune of an unknown etiology. Air pollution has been proposed as one of the possible risk factors associated with disease activity, although has not been extensively studied. In this study, we measured the relationship between exposure to air pollutants and RA activity. Data on RA patients were extracted from the Kuwait Registry for Rheumatic Diseases (KRRD). Disease activity was measured using disease activity score with 28 examined joints (DAS-28) and the Clinical Disease Activity Index (CDAI) during their hospital visits from 2013 to 2017. Air pollution was assessed using air pollution components (PM ${}_{10}$ , NO ${}_2$ , SO ${}_2$ , O ${}_3$ , and CO). Air pollution data were obtained from Kuwait Environmental Public Authority (K-EPA) from six different air quality-monitoring stations during the same period. Multiple imputations by the chained equations (MICE) algorithm were applied to estimate missing air pollution data. Patients data were linked with air pollution data according to date and patient governorate address. Descriptive statistics, correlation analysis, and linear regression techniques were employed using STATA software. In total, 1651 RA patients with 9875 follow-up visits were studied. We detected an increased risk of RA using DAS-28 in participants exposed to SO ${}_2$ and NO ${}_2$ with $\beta =0.003$ (95% CI: 0.0004–0.005, $p<0.01$ ) and $\beta =0.003$ (95% CI: 0.002–0.005, $p<0.01$ ), respectively, but not to PM ${}_{10}$ , O ${}_3$ , and CO concentrations. Conclusively, we observed a strong association between air pollution with RA disease activity. This study suggests air pollution as a risk factor for RA and recommends further measures to be taken by the authorities to control this health problem.