Gompertz law revisited: Forecasting mortality with a multi-factor exponential model

2021 ◽  
Author(s):  
Hong Li ◽  
Ken Seng Tan ◽  
Shripad Tuljapurkar ◽  
Wenjun Zhu
Keyword(s):  
Exponential Model ◽  
Gompertz Law

Gompertz Law Revisited: Forecasting Mortality with a Multi-factor Exponential Model

2019 ◽  
Author(s):  
Hong Li ◽  
Ken Seng Tan ◽  
Shripad Tuljapurkar ◽  
Wenjun Zhu
Keyword(s):  
Exponential Model ◽  
Gompertz Law

Modelling In-Patient Bed Usage Behaviour in a Department of Geriatric Medicine

1993 ◽  
Vol 32 (01) ◽  
pp. 79-81 ◽  
Author(s):  
P. Millard ◽  
S. McClean
Keyword(s):  
Census Data ◽  
Geriatric Medicine ◽  
Temporal Trends ◽  
Exponential Model ◽  
Bed Occupancy ◽  
Lengths Of Stay ◽  
Geriatric Hospital ◽  
Using Data ◽  
Acute Patients

Abstract:The flow of patients through geriatric hospitals has been previously described in terms of acute and long-stay states where the bed occupancy at a census point is modelled by a mixed exponential model. Using data for sixteen years the model was fitted to successive annual census points, in order to provide a description of temporal trends. While the number of acute patients has remained fairly stable during the period, the model shows that there has been a decrease in the number of long-stay patients. Mean lengths of stay in our geriatric hospital before death or discharge have decreased during the study period for both acute and long-stay patients.Using these fits of the mixed exponential model to census data, a method is provided for predicting future turnover of patients. These predictions are reasonably good, except when the turnover patterns go through a period of flux in which assumption of stability no longer holds. Overall, a methodology is presented which relates census analysis to the behaviour of admission cohorts, thus producing a means of predicting future behaviour of patients and identifying where there is a change in patterns.

Evaluating the innovation of oil and gas enterprises under new circumstances: Evidence from Rosneft Oil Company

2020 ◽  
Vol 8 (8) ◽  
pp. 1444-1458
Author(s):  
N.M. Baranova ◽  
D.S. Loginova ◽  
S.N. Larin
Keyword(s):  
Oil And Gas ◽  
Annual Report ◽  
Statistical Data ◽  
Exponential Model ◽  
Innovative Activity ◽  
Corporate Development ◽  
Power Resources ◽  
Business Operations ◽  
Trade Wars ◽  
Oil Company

Subject. Illustrating the case of Rosneft Oil Company, we herein study how innovation spurs business operations, increases the competitiveness of firms and protects them from risks. Objectives. We model the innovative activity of Rosneft Oil Company and its competitiveness. Methods. We analyze proceedings by the Russian and foreign scholars, materials on program for the innovative and sustainable development of Rosneft Oil Company. Our assessments were based on statistical data of Rosneft’s annual report for 2004–2019. The regression analysis and econometric studies were conducted via Eviews10. Results. We set models to predict the innovative development of the company for the nearest future. We revealed that the linear model was the most appropriate and suitable for forecasting. Properties and estimates of the exponential model turned to be insignificant, on the contrary. Conclusions and Relevance. Currently, it is difficult to forecast the extent to which corporate development, its innovative activity will change in 2020 and in the nearest future. Despite the company’s achievements before 2020, continuous trade wars, geopolitical conflicts, pandemic, OPEC agreements and a consequential drastic drop in the demand for power resources considerably slowed down the pace of the economic growth not only in the company, but also in the country.

Comparison between modified exponential model and common models of light-response curve

2013 ◽  
Vol 36 (12) ◽  
pp. 1277-1285 ◽  
Author(s):  
Wei-Ying CHEN ◽  
Zhen-Yong CHEN ◽  
Fu-Yan LUO ◽  
Zheng-Song PENG ◽  
Mao-Qun YU
Keyword(s):  
Response Curve ◽  
Light Response ◽  
Exponential Model ◽  
Light Response Curve

scholarly journals CO<sub>2</sub> flux determination by closed-chamber methods can be seriously biased by inappropriate application of linear regression

2007 ◽  
Vol 4 (6) ◽  
pp. 1005-1025 ◽  
Author(s):  
L. Kutzbach ◽  
J. Schneider ◽  
T. Sachs ◽  
M. Giebels ◽  
H. Nykänen ◽  
...  
Keyword(s):  
Linear Regression ◽  
Nonlinear Regression ◽  
Co2 Flux ◽  
Exponential Model ◽  
Co2 Uptake ◽  
Co2 Fluxes ◽  
Closed Chamber ◽  
Exponential Regression ◽  
Closure Time ◽  
Over Time

Abstract. Closed (non-steady state) chambers are widely used for quantifying carbon dioxide (CO2) fluxes between soils or low-stature canopies and the atmosphere. It is well recognised that covering a soil or vegetation by a closed chamber inherently disturbs the natural CO2 fluxes by altering the concentration gradients between the soil, the vegetation and the overlying air. Thus, the driving factors of CO2 fluxes are not constant during the closed chamber experiment, and no linear increase or decrease of CO2 concentration over time within the chamber headspace can be expected. Nevertheless, linear regression has been applied for calculating CO2 fluxes in many recent, partly influential, studies. This approach has been justified by keeping the closure time short and assuming the concentration change over time to be in the linear range. Here, we test if the application of linear regression is really appropriate for estimating CO2 fluxes using closed chambers over short closure times and if the application of nonlinear regression is necessary. We developed a nonlinear exponential regression model from diffusion and photosynthesis theory. This exponential model was tested with four different datasets of CO2 flux measurements (total number: 1764) conducted at three peatlands sites in Finland and a tundra site in Siberia. Thorough analyses of residuals demonstrated that linear regression was frequently not appropriate for the determination of CO2 fluxes by closed-chamber methods, even if closure times were kept short. The developed exponential model was well suited for nonlinear regression of the concentration over time c(t) evolution in the chamber headspace and estimation of the initial CO2 fluxes at closure time for the majority of experiments. However, a rather large percentage of the exponential regression functions showed curvatures not consistent with the theoretical model which is considered to be caused by violations of the underlying model assumptions. Especially the effects of turbulence and pressure disturbances by the chamber deployment are suspected to have caused unexplainable curvatures. CO2 flux estimates by linear regression can be as low as 40% of the flux estimates of exponential regression for closure times of only two minutes. The degree of underestimation increased with increasing CO2 flux strength and was dependent on soil and vegetation conditions which can disturb not only the quantitative but also the qualitative evaluation of CO2 flux dynamics. The underestimation effect by linear regression was observed to be different for CO2 uptake and release situations which can lead to stronger bias in the daily, seasonal and annual CO2 balances than in the individual fluxes. To avoid serious bias of CO2 flux estimates based on closed chamber experiments, we suggest further tests using published datasets and recommend the use of nonlinear regression models for future closed chamber studies.

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