Browsing by Author "Çankaya, Mehmet Niyazi"

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Forecast of the energy consumption of Turkiye commerce sector: m-estimation model application
(INCE, 2022) Çankaya, Mehmet Niyazi; Özen, Ercan
Net electricity consumption use continue to be significant issues. There are various forms of energy use and production. This work uses robust and form of Mestimation by using the grid-search algorithm. Thus, since we use form of Huber Mestimation, the prediction performance can be increased; because, the data is tried to be modelled by using the different values of tuning parameter determined by the gridsearch which can be used to carry out the optimization proving the M-estimates of the parameters of regression model. The data sets which are year and the net electricity consumption are modelled by regression model in order to predict and forecast how much electricity consumption will be necessary for the commercial purpose firm used the electricity at the highest amount. The statistical inference for the regression model and its estimators of parameters in the model is also provided. Further, the illustrative results used for the grid-search and the analytical expression of regression model are given. Due to the fact that polynomial regression showing an increment in the polynomial trend can model the dependent variable well, the net electrical consumption in commerce at Turkiye increases and the bandwidths for the forecasting in the year 2021 and 2022 are given to conduct a planning in energy sector.
Risk rating from road traffic accident fatalities for the world insurance sector
(INCE, ASEM, 2024) Çankaya, Mehmet Niyazi; Özen, Ercan
Traffic insurance is a system that compensates the insured for various damages that may arise due to accidents. Insurance companies finance these damages with the premiums received from the insured. Suitable insurance premium tariff. It keeps the customer demand in optimum balance and at the same time affects the financial success of the insurance company. Therefore, there is a need for optimal pricing. In order for insurance companies to establish the optimal risk-pricing balance, they need to rate the risks. Rating the mortality risks due to traffic accidents also allows insurance companies to price appropriate premiums. For this reason, the aim of this study is to rate the global traffic accident related mortality risks by countries. This study examines the global distribution of road traffic mortality by analyzing data from 231 countries using the DBSCAN algorithm. By classifying the number of deaths per 100,000 population, we identify patterns of similarity and show that countries can be grouped into 27, 8 or even as few as 7 distinct classes, depending on the tuning parameters of the DBSCAN algorithm. These results suggest that, despite the large number of countries, road traffic fatalities show similar patterns that can be attributed primarily to human factors. The classification highlights the importance of focusing on vehicle and driver-related issues rather than infrastructure, which appears to be less of a differentiating factor between countries. This findings have important implications for policy makers and insurance companies aiming to reduce the number of road deaths through targeted interventions.