scholarly journals Modeling COVID-19 Pandemic Data with Beta-Double Exponential Model

2020 ◽  
pp. 66-79
Author(s):  
N. I. Badmus ◽  
Faweya Olanrewaju ◽  
A. T. Adeniran
Keyword(s):  
Exponential Model ◽  
Information Criteria ◽  
Cumulative Distribution ◽  
Likelihood Method ◽  
African Countries ◽  
New Model ◽  
Akaike Information Criteria ◽  
Proposed Model ◽  
Double Exponential ◽  
Double Exponential Model

Objective: This paper examines and upgrades a two-parameter double exponential distribution to a four-parameter beta double exponential model by compounding the baseline distribution and beta link function to fits and analyse deaths-cases data set of the recent outbreak of the global pandemic coronavirus disease (COVID-19) for both Africa and Non-Africa countries. The new proposed model, although complex in its mathematical structure, yet flexible to implement and its robustness to accommodate non-normal data is an extra advantage to statistical theory and other fields. Methodology: The statistical properties: the density function, cumulative distribution function, survival function, hazard function, moments, moments generating function, skewness and kurtosis of the developed model were presented. Maximum likelihood method is used for parameters estimation procedure. The new model is validated and compared with some frontier similar extant parametric family of beta distributions using graphs, Kolmogorov Smirnov (KS) Statistic, Log-likelihood and model criteria statistics like Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC) and Consistent Akaike Information Criteria (CAIC) as tools for comparison. Results: The graphs, KS, LogL and model criteria statistics values showed that the proposed model fits the COVID-19 pandemic data better than other competing models since the model has lower values as stated: The values from non-African countries KS = 0.1208, LogL = 278.4168, AIC = 560.8336, BIC = 576.1147 and CAIC = 577.1147. Also, from African countries are: KS = 0.0759, LogL = 144.0245, AIC = 292.0490, BIC = 303.9302 and CAIC = 304.9302. Conclusion: The proposed model showed its applicability and flexibility over other models considered in this work. Therefore, this implies that the new model can be used for modeling other infectious disease data and real data in many fields.

A DOUBLE EXPONENTIAL MODEL FOR TRANSMITTANCE IN SOLAR PONDS

1989 ◽  
Vol 7 (4) ◽  
pp. 227-235 ◽  
Author(s):  
M. N. A. HAWLADER ◽  
J. C. HO ◽  
N. E. WIJEYSUNDERA ◽  
T. H. KHO
Keyword(s):  
Exponential Model ◽  
Solar Ponds ◽  
Double Exponential ◽  
Double Exponential Model

scholarly journals BOD measuring and modelling methods - review

2011 ◽  
Vol 43 (2) ◽  
pp. 143-153
Author(s):  
Tadeusz Siwiec ◽  
Lidia Kiedryńska ◽  
Klaudia Abramowicz ◽  
Aleksandra Rewicka ◽  
Piotr Nowak
Keyword(s):  
Exponential Model ◽  
Second Order ◽  
First Order ◽  
Double Exponential ◽  
Double Exponential Model ◽  
Modelling Methods

BOD measuring and modelling methods - reviewThe article presents the method of measuring BOD in wastewater and characteristic different models which can by used for describing changes of BOD in next days. In the paper described eight models: Moore et al. (1950), Thomas (1950), Navone (1960), Fujimoto (1964), Hewitt et al. (1979), Adrian and Sanders (1992-1993) as well as Young and Clark (1965) used by Adrian and Sanders (1998), Borsuk and Stow (2000) and Manson et al. (2006). Comparison the models suggests that changing of BOD during the time are better describes by models second order or double exponential model (Manson et al. 2006) than models the first order.

Decomposition of aspen (Populustremuloides) leaf litter modified by leaching

1990 ◽  
Vol 20 (7) ◽  
pp. 943-951 ◽  
Author(s):  
William F. J. Parsons ◽  
Barry R. Taylor ◽  
Dennis Parkinson
Keyword(s):  
Mass Loss ◽  
Leaf Litter ◽  
Rocky Mountain ◽  
Exponential Model ◽  
Decay Rates ◽  
Double Exponential ◽  
Exponential Trend ◽  
Aspen Forest ◽  
Double Exponential Model ◽  
Soluble Material

In a Rocky Mountain aspen forest, the detailed pattern of mass loss from decomposing leaf litter of trembling aspen (Populustremuloides Michx.) during the first 6 months of decay was compared with that from aspen leaves modified to produce a more recalcitrant litter type by removal of leachable material (31.7% of original mass). Leaching litter removed substantial quantities of N (24%) and P (54%), but did not change the litter's C/N ratio (77:1); and leached leaves still contained 33% labile (benzene alcohol soluble) material. Decomposition of intact aspen litter was best described by a double exponential model (k1 = −7.91/year, k2 = −0.21/year), except during the first 2 weeks, when an extremely rapid mass loss (14.2%) apparently resulted from leaching. Microbial metabolism was probably responsible for most of the subsequent decay (35% total in 6 months). In contrast, decomposition of leached aspen showed no exponential trend and was best described by a simple linear regression with a slope of −19.7%/year. Additional data from a 2nd year (12–15 months decay) reduced the regression estimates of decay rates but did not alter the best fit models. Fits were improved slightly if temperature sum replaced time in the regressions, especially if 2nd-year data were included.

scholarly journals Price forecasting and Seasonality of Soybean in Amravati District of Maharashtra India

2019 ◽  
Vol 7 (3) ◽  
pp. 417-423
Author(s):  
Priyanka Mallikarjun Kumbhar
Keyword(s):  
Secondary Data ◽  
Exponential Model ◽  
Exponential Smoothing ◽  
Export Market ◽  
Price Forecasting ◽  
Soybean Crop ◽  
Financial Strength ◽  
Double Exponential ◽  
Seasonal Indices ◽  
Double Exponential Model

Soybean crop has contributed to improve the financial strength of the Indian farmers. It usually fetches higher income to the farmers owing to the massive export market for Soybean de-oiled cake. In state of Maharashtra Soybean is cultivated extensively in Amravati district. So the present studies explore the seasonality and price forecasting issue for Soybean crop. The is based on the secondary data. The monthly wholesale prices and arrivals data for the study collected from the agmarknet.gov.in for the period January 2008 to December 2017. To analyze the data we use statistical techniques like seasonality and exponential smoothing for price forecasting. The processing of data is done through MS- Excel and MINITAB Software. The study gives an overview of the different time series analytical methods, which can be used for price forecasting. The present study is undertaken precisely to fill the research gap and results of this study found an inverse relationship between price and market arrivals of soybean. The arrivals were recorded very high from October to January and seasonal indices of price were elevated during August in which arrivals were found stumpy. The assessment of all three Exponential Smoothing models was carried out in the procedure based on the Double Exponential model with MAD (168.3) and MAPE (6.14) values, which were considered in the smallest amount. The accuracy of proportion among the forecasted and actual price value of soybean was found in between 80.52 to 85.55 percent. It was pragmatic that the Double Exponential model was the most appropriate for forecasting the soybean.

Protein Interaction Network. Double Exponential Model

2008 ◽  
Vol 01 (02) ◽  
pp. 061-067 ◽  
Author(s):  
Piotr H. Pawlowski ◽  
Szymon Kaczanowski ◽  
Piotr Zielenkiewicz
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Exponential Model ◽  
Double Exponential ◽  
Double Exponential Model
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