A MCMC Bayesian approach to binary logistic model for Ka-band propagation effect by rain over Iran

Author(s):  
Farkhondeh Kiaee ◽  
Reza Bahri ◽  
Mohammad Hossein Kiaee ◽  
Toseef Azid
2014 ◽  
Vol 6 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Shabnam Fazli Aghghaleh ◽  
Zakiah Muhammaddun Mohamed .

The current research studies the usefulness of Cressey’s fraud risk factor framework adopted from SAS No. 99 to prevent fraud from occurring. In accordance with Cressey’s theory, pressure, opportunity and rationalization are existing when fraud occurs. The study suggests variables as proxy measures for pressure and opportunity, and test these variables using publicly available information relating to a set of fraud firms and a sample of no-fraud firms. Two pressure proxies and two opportunity proxies are identified and suggested to be significantly related to financial statement fraud. We find that leverage and sale to account receivable are positively related to the likelihood of fraud. Audit committee size and board of directors’ size are also linked to decrease the level of financial statement fraud. A binary logistic model based on examples of fraud risk factors of fraud triangle model measures the likelihood of financial statement fraud and can assist experts.


2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Ellis Kobina Paintsil ◽  
Akoto Yaw Omari-Sasu ◽  
Matthew Glover Addo ◽  
Maxwell Akwasi Boateng

Malaria is the leading cause of morbidity in Ghana representing 40-60% of outpatient hospital attendance with about 10% ending up on admission. Microscopic examination of peripheral blood film remains the most preferred and reliable method for malaria diagnosis worldwide. But the level of skills required for microscopic examination of peripheral blood film is often lacking in Ghana. This study looked at determining the extent to which haematological parameters and demographic characteristics of patients could be used to predict malaria infection using logistic regression. The overall prevalence of malaria in the study area was determined to be 25.96%; nonetheless, 45.30% of children between the ages of 5 and 14 tested positive. The binary logistic model developed for this study identified age, haemoglobin, platelet, and lymphocyte as the most significant predictors. The sensitivity and specificity of the model were 77.4% and 75.7%, respectively, with a PPV and NPV of 52.72% and 90.51%, respectively. Similar to RDT this logistic model when used will reduce the waiting time and improve the diagnosis of malaria.


2011 ◽  
Vol 7 (1) ◽  
pp. 19
Author(s):  
Chandra Yuliana

The purpose of this research is to test empirically the influence of leverage, turnover of CEO and tax motivation to earnings management, based on its discretionary accruals value. The sample of this research is listed manufacturing firms for the period 2004-2008 with 5 different subsectors. Statistical analysis method used in this research is logistic regression with binary logistic model. The result shows leverage does not have significant influence to earnings management while the turnovers of CEO and tax motivation have significant influence to earnings management.Keywords: Earnings Management, Leverage, CEO, Tax


2021 ◽  
Vol 235 ◽  
pp. 02003
Author(s):  
Jing Li ◽  
Qin Chen

Based on the survey data of 300 households in Fujian province, the system of farmers’ choice of forestland management mode was constructed by using the framework of sustainable livelihood analysis, and the impact of livelihood capital on farmers’ choice of forestland management mode was analyzed by using binary logistic model. The research shows that the factors of livelihood capital have different impact on the choice of forestland management mode. On this basis, the author puts forward some countermeasures to encourage farmers to improve their livelihood capital capacity, develop scale management and reduce their dependence on forestland income.


2021 ◽  
Author(s):  
Hongqin Li ◽  
Yongsheng Yang ◽  
Fawei Zhang ◽  
Xiaowei Guo ◽  
Yikang Li ◽  
...  

Abstract The soil seepage is an important component for quantifying hydrological processes while remains unclear in high-altitude alpine meadows. The shallow soil seepage was continuously measured by an auto-logged micro-lysimeter (diameter = 30 cm, depth = 30 cm) from July 2018 to June 2019 in a piedmont summer pasture of alpine meadow on the Northeastern Qinghai-Tibetan Plateau. The results showed that all the shallow soil seepage events occurred during the non-frozen period from April to September and the accumulative amount was 106.8 mm, which consumed about 1/5 annual precipitation. The maximum and minimum monthly soil seepage was 30.7 mm in September and 1.0 mm in April, respectively. The binary Logistic model revealed that daily half-hour rainfall frequency (R2 = 0.37, individual explanatory power) and amount played significant roles in the daily soil seepage odds (R2 = 0.50). The best subsets regression analysis showed that the same-day rainfall amount (R2 = 0.59), the first 3-day rainfall frequency, and the first 4-day solar radiation accounted for 73% of variations in the daily soil seepage amount. Monthly soil seepage correlated with monthly rainfall frequency significantly (R2 = 0.74, p = 0.005). Our results highlighted that precipitation, including its amount and frequency, was the key determinant of the probability and amount of the shallow soil seepage in the piedmont summer pasture of alpine meadow. These findings would be helpful for improving predictions of the water budgets of piedmont alpine meadows.


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