NONPARAMETRIC TEST FOR UBACT CLASS OF LIFE DISTRIBUTION BASED ON U-STATISTIC

Based on U-statistic, testing exponentially versus used better than aged in convex tail ordering (UBACT) class of life distribution is introduced for complete and cen-sored data. Convergence of the proposed statistic to the normal distribution is proved. Selected critical values are tabulated for sample sizes 5(5)80 for complete data, and (61)(10)(201) for censored data: The Pitman asymptotic relative e¢ ciency of the pro- posed tests to the other classes is studied. A numerical examples in medical science demonstrates practical application of the proposed test

Analysis of bitcoin prices using a heavy-tailed version of Dagum distribution and machine learning methods

Statistical modeling and forecasting are very important for decision-making in any field of life. This paper has two major objectives, namely, statistical modeling and forecasting of real phenomena. For covering the first aim (i.e., statistical modeling), we introduce a new probabilistic model. The new model is introduced by mixing the Dagum distribution with the weighted TX family approach. The proposed model is called the weighted TX Dagum distribution and possesses heavy-tailed characteristics. The new model is illustrated by analyzing real-life data related to Bitcoin prices. To cover the second aim (i.e., forecasting), we take into account six macroeconomic and financial indicators to investigate their impact on Bitcoin prices such as the Adaptive least absolute shrinkage and selection operator (Alasso), elastic net, and minimax concave penalty. After analyzing the data, it is found that Alasso and MCP have retained all the included predictors, except import, while Enet holds all the predictors. The root means square error and mean absolute error associated with MCP are lower than Alasso and Enet, which reveals that MCP fits the data very well as compared to rival methods

New logarithmic type imputation techniques in presence of measurement errors

Several imputation techniques have been constructed to sort out the missing data issue. However, there are few papers that address the missing data issue in measurement error (ME). This article proposes some logarithmic type imputation techniques to tackle the missing data issue when the data are commingled with ME. The proffered imputation techniques’ mean square error is calculated to a first order approximation. The relative performance of the suggested imputation techniques against the contemporary imputation techniques is performed. Furthermore, the theoretical results are exemplified using an extensive simulation study based on artificially generated population. The appropriate recommendations have been suggested to the surveyors for real-life problems

A new modified biased estimator for Zero inflated Poisson regression model

Zero-inflated Poisson (ZIP) model is widely used for counting data with excessive zeroes. The multicollinearity is the common factor in the explanatory variables of the count data. In this context, typically, maximum likelihood estimation (MLE) generates unsatisfactory results due to inflation of mean square error (MSE). In the solution of this problem usually, ridge parameters are used. In this study, we proposed a new modified zero-inflated Poisson ridge regression model to reduce the problem of multicollinearity. We experimented within the context of a specified simulation strategy and recorded the behavior of proposed estimators. We also apply our proposed estimator to the real-life data set and explore how our proposed estimators perform well in the presence of multicollinearity with the help of ZIP model for count data

Assessment of osteoporosis in patients with chronic obstructive pulmonary disease

Background: COPD is a widely distributed disease with high morbimortality, associated with important pathologies, among which is osteoporosis. However, osteoporosis is often undiagnosed in these patients. Objectives: To evaluate the prevalence of osteoporosis among COPD patients and to determine its relation to demographic data and disease severity. Subjects and methods: This study was conducted on 30 male patients with severe to very severe COPD, in addition to 30 age and sex matched lifelong nonsmoker healthy volunteers. Spirometric indices, serum Ca, phosphorous, ALP, albumin, and PTH were measured. BMD was measured by broadband heel ultrasound method. Results: Corrected Ca was significantly decreased, PTH was significantly increased and ALP showed non-significant increase in the COPD group. As regards BMD; BUA, Z-score and T-score were significantly decreased while RRF was significantly increased in the COPD group. In addition 56.6% of COPD patients had low BMD. Both COPD group either with normal BMD or with low BMD were matched as regards all demographic data. VC%, FVC% and FEV1%, BUA, T-score, and Z-score were significantly decreased while PTH and RRF were significantly increased in the COPD group with low BMD. Z-score was negatively correlated with FEV1 and PTH while BUA was positively correlated with ALP and negatively correlated with FEV1/FVC. Conclusion: Low BMD is prevalent among men with COPD (GOLD stage III–IV) than age matched males. The degree of the loss of BMD has been found to be proportionate to the COPD severity. COPD patients with low BMD have threefold increase in fracture risk