Expected Loss Ratio Model and the Notion of Psychological Distances

1985 ◽  
Vol 98 (2) ◽  
pp. 305
Author(s):  
Dan Zakay
Keyword(s):  
Loss Ratio ◽  
Ratio Model ◽  
Expected Loss

scholarly journals How To Find The Right Subdivision Into Tariff Classes

1975 ◽  
Vol 8 (2) ◽  
pp. 257-263 ◽  
Author(s):  
H. Schmitter ◽  
E. Straub
Keyword(s):  
A Priori ◽  
Statistical Criterion ◽  
Loss Ratio ◽  
Expected Loss ◽  
Type B ◽  
Insurance Portfolio ◽  
The Right ◽  
Number Of Classes ◽  
Vague Statement ◽  
Better Than

By intuition, the subdivision of an insurance portfolio into a number of classes is said to be good if it reflects the heterogeneity of the portfolio in an efficient way. To illustrate this rather vague statement we take the following very simple example:The portfolio consists of 20 independent risks, 10 of them producing an expected loss ratio of say 30% each (type A risks) and 80% each (type B risks) respectively.This “natural” subdivision is certainly better than, for instance, no subdivision at all;or, the finest possible subdivision with 20 classes consisting of only 1 risk each (because there is no point in differentiating between risks of the same type);or, 5 classes each containing two A- and two B-risks (here the number of classes is unnecessarily high and the heterogeneity has been completely wiped out; statistics based on this subdivision would even make us believe that the portfolio is totally homogeneous).As a matter of fact, the above “natural” subdivision is—of course!—the best of all subdivisions, it is the optimal subdivision in this case.In practice, however, as we all know, it is not easy to find the optimal subdivision. For one thing, the inherent structure or “natural” subdivision is not known a priori and secondly, for many different reasons, we can only choose from a limited number of subdivisions and not from all theoretically possible solutions. Note that even with only 20 risks there are 58.1012 possibilities of subdividing the portfolio. Thus, in practice, there is only a relatively small number of admissible subdivisions and the optimal one may not be among them, but we still need some sort of statistical criterion to choose the best one from these admissible subdivisions.

The Measurement of Performance in Probabilistic Diagnosis IV. Utility Considerations in Therapeutics and Prognostics

1981 ◽  
Vol 20 (02) ◽  
pp. 80-96 ◽  
Author(s):  
J. D. F. Habbema ◽  
J. Hilden
Keyword(s):  
Decision Theory ◽  
Figure Of Merit ◽  
Acute Abdominal Pain ◽  
Theory Approach ◽  
Expected Loss ◽  
Patient Benefit ◽  
Evaluation Approach ◽  
Average Loss ◽  
Actual Decision ◽  
Probabilistic Diagnosis

It is argued that it is preferable to evaluate probabilistic diagnosis systems in terms of utility (patient benefit) or loss (negative benefit). We have adopted the provisional strategy of scoring performance as if the system were the actual decision-maker (not just an aid to him) and argue that a rational figure of merit is given by the average loss which patients would incur by having the system decide on treatment, the treatment being selected according to the minimum expected loss principle of decision theory.A similar approach is taken to the problem of evaluating probabilistic prognoses, but the fundamental differences between treatment selection skill and prognostic skill and their implications for the assessment of such skills are stressed. The necessary elements of decision theory are explained by means of simple examples mainly taken from the acute abdomen, and the proposed evaluation tools are applied to Acute Abdominal Pain data analysed in our previous papers by other (not decision-theoretic) means. The main difficulty of the decision theory approach, viz. that of obtaining good medical utility values upon which the analysis can be based, receives due attention, and the evaluation approach is extended to cover more realistic situations in which utility or loss values vary from patient to patient.

Prediction of Technical Reserves Based on Grey Model — GM(1,1): Evidence from Non-life Egyptian Insurance Market

2019 ◽  
Vol 10 (9) ◽  
pp. 852-860
Author(s):  
Mahmoud Elsayed ◽  
◽  
Amr Soliman ◽  
Keyword(s):  
System Theory ◽  
Grey System Theory ◽  
Insurance Market ◽  
Grey Model ◽  
Loss Ratio ◽  
Grey System ◽  
Mathematical Technique ◽  
Supervisory Authority ◽  
Error Ratio ◽  
Using Data

Grey system theory is a mathematical technique used to predict data with known and unknown characteristics. The aim of our research is to forecast the future amount of technical reserves (outstanding claims reserve, loss ratio fluctuations reserve and unearned premiums reserve) up to 2029/2030. This study applies the Grey Model GM(1,1) using data obtained from the Egyptian Financial Supervisory Authority (EFSA) over the period from 2005/2006 to 2015/2016 for non-life Egyptian insurance market. We found that the predicted amounts of outstanding claims reserve and loss ratio fluctuations reserve are highly significant than the unearned premiums reserve according to the value of Posterior Error Ratio (PER).

Development of Quality Measurement System Using Slip Ratio Model with Homogenization System

2019 ◽  
Vol 18 (0) ◽  
pp. 109-118
Author(s):  
Kazuma MINOTE ◽  
Yuki SAKAMOTO ◽  
Shohei TANE ◽  
Yo NAKAJIMA ◽  
Atsuhiro FURUICHI ◽  
...  
Keyword(s):  
Measurement System ◽  
Quality Measurement ◽  
Ratio Model ◽  
Slip Ratio

On the issue of assessing the adequacy of the reserves of insurance companies with various methods for determining the loss of business

2020 ◽  
pp. 46-53
Author(s):  
Margarita Naslednikova ◽  
Alexandr Zamalov
Keyword(s):  
Comparative Analysis ◽  
Health System ◽  
Financial Reporting ◽  
Accounting Standards ◽  
Insurance Companies ◽  
Insurance Company ◽  
Reporting Standards ◽  
Loss Ratio ◽  
Qualitative Characteristic ◽  
Financial Reporting Standards

The article discusses methods for calculating the loss ratio of insurance companies, including compulsory medical insurance, which is the basis for building a health system; su’ciency of formed reserves, which are created in connection with the possibility of losses. Variants of interpretation of calculated indicators into a qualitative characteristic of the insurance company. A comparative analysis of the calculation of indicators of loss-making of insurance companies and the adequacy of the formation of reserves of insurance companies according to Russian accounting standards and in accordance with the requirements of international financial reporting standards.

scholarly journals Physical and mechanical properties of rubberwood (Hevea brasiliensis) dyed with Lasiodiplodia theobromae

2019 ◽  
Vol 65 (1) ◽  
Author(s):  
Boshi Zhao ◽  
Zhiming Yu ◽  
Yang Zhang ◽  
Chusheng Qi
Keyword(s):  
Mass Loss ◽  
Water Absorption ◽  
Hevea Brasiliensis ◽  
Ph Value ◽  
Energy Dispersive Spectrometer ◽  
Physical And Mechanical Properties ◽  
Blue Staining ◽  
Loss Ratio ◽  
Lasiodiplodia Theobromae ◽  
Infrared Spectrometer

AbstractBlue staining on rubberwood (Hevea brasiliensis) is a common kind of defect. There currently exists much research focused on the prevention and control of blue staining. However, little research has been concentrated on the utilization of blue staining for green dyeing. The research conveyed in this paper primarily used Lasiodiplodia theobromae to dye rubberwood, and used scanning electron microscope (SEM), energy-dispersive spectrometer (EDS), X-ray diffraction (XRD), and fourier transform infrared spectrometer (FTIR) to analyze the commission internationale eclairage (CIE) L*a*b* value of color, the contact angle, the pH value, 24-h water absorption, mass loss ratio, and compressive strength in increments between 5 and 40 days. The results found that the color of rubberwood became darker and more uniform, and that the surface dyed with fungi can reach a super-hydrophobic state. With the increase of time, the pH value of rubberwood changed from acidic to alkaline. Furthermore, hyphae entered the wood mainly through vessels for their large pore diameter, and reduced water absorption. Mass loss ratio increased gradually between 5 and 40 days. The research in this paper concludes that the microorganism was an effective method of wood dyeing, and lays a foundation for further research.

scholarly journals Multivariate Tail Probabilities: Predicting Regional Pertussis Cases in Washington State

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 675
Author(s):  
Xuze Zhang ◽  
Saumyadipta Pyne ◽  
Benjamin Kedem
Keyword(s):  
Disease Modeling ◽  
Empirical Distribution ◽  
Density Ratio ◽  
Washington State ◽  
Ratio Model ◽  
Multiple Sources ◽  
Tail Probabilities ◽  
Data Set ◽  
Density Ratio Model ◽  
Regional Prediction

In disease modeling, a key statistical problem is the estimation of lower and upper tail probabilities of health events from given data sets of small size and limited range. Assuming such constraints, we describe a computational framework for the systematic fusion of observations from multiple sources to compute tail probabilities that could not be obtained otherwise due to a lack of lower or upper tail data. The estimation of multivariate lower and upper tail probabilities from a given small reference data set that lacks complete information about such tail data is addressed in terms of pertussis case count data. Fusion of data from multiple sources in conjunction with the density ratio model is used to give probability estimates that are non-obtainable from the empirical distribution. Based on a density ratio model with variable tilts, we first present a univariate fit and, subsequently, improve it with a multivariate extension. In the multivariate analysis, we selected the best model in terms of the Akaike Information Criterion (AIC). Regional prediction, in Washington state, of the number of pertussis cases is approached by providing joint probabilities using fused data from several relatively small samples following the selected density ratio model. The model is validated by a graphical goodness-of-fit plot comparing the estimated reference distribution obtained from the fused data with that of the empirical distribution obtained from the reference sample only.

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