scholarly journals Aggregate loss model with Poisson-Tweedie frequency

2021 ◽  
Vol 6 (0) ◽  
pp. 56-73
Author(s):  
S. Chen ◽  
◽  
Z. Wang ◽  
M. Kelly ◽  
Keyword(s):  
Loss Model ◽  
Aggregate Loss

scholarly journals Analysis of an aggregate loss model in a Markov renewal regime

2021 ◽  
Vol 396 ◽  
pp. 125869
Author(s):  
Pepa Ramírez-Cobo ◽  
Emilio Carrizosa ◽  
Rosa E. Lillo
Keyword(s):  
Loss Model ◽  
Aggregate Loss

Bayesian analysis in an aggregate loss model: validation of the structure functions

2017 ◽  
Vol 11 (3) ◽  
pp. 19-47
Author(s):  
Agustin Hernandez Bastida ◽  
Jose Maria Perez Sanchez ◽  
Pilar Fernandez-Sanchez
Keyword(s):  
Bayesian Analysis ◽  
Model Validation ◽  
Structure Functions ◽  
Loss Model ◽  
Aggregate Loss

scholarly journals PERHITUNGAN UKURAN RISIKO UNTUK MODEL KERUGIAN AGREGAT

2020 ◽  
Vol 14 (1) ◽  
pp. 21
Author(s):  
Nadya Pratiwi ◽  
Aprida Siska Lestia ◽  
Nur Salam
Keyword(s):  
Monte Carlo ◽  
Standard Deviation ◽  
Monte Carlo Method ◽  
Value At Risk ◽  
Risk Measure ◽  
Random Variable ◽  
Insurance Companies ◽  
Loss Model ◽  
Aggregate Loss ◽  
Conditional Tail Expectation

In the case of nonlife insurance, insurance companies are very potential to get losses if claims submitted by customers (policyholders) exceeds the reserves of budgeted claims. It is the risk that have to managed properly by insurance companies . One possible disadvantage is the aggregate loss model. The aggregate loss model is a random variable that states the total of all losses incurred in an insurance policy block. This kind of loss can be modeled using a collective risk approach where the number of claims is a discrete random variable and the size of claim is a continuous random variable. The purpose of this study is to determine risk measure of standard deviation premium principle, value at risk (VaR), and conditional tail expectation (CTE) of the aggregate loss model. Standard deviation premium principle risk measure of aggregate loss model is determined analytically by substituted it expected value and varians. Meanwhile, VaR risk measure is determined using numerical method by Monte Carlo method, then the quantile value and it confidence interval for the actual value will estimate. In the CTE calculation, based on the loss data obtained in the Monte Carlo method, the CTE value is estimated by calculating the average loss that exceeds the VaR value. If the data size is large enough, the CTE value estimation will converge to the actual value.Keywords: Aggregate Loss Model, Standard Deviation Premium Principle, Value at Risk (VaR), Conditional Tail Expectation (CTE).

Path Loss Model in Crowded Outdoor Environments Considering Multiple Human Body Shadowing of Multipath at 4.7GHz and 26.4GHz

2019 ◽  
Vol E102.B (8) ◽  
pp. 1676-1688 ◽  
Author(s):  
Mitsuki NAKAMURA ◽  
Motoharu SASAKI ◽  
Wataru YAMADA ◽  
Naoki KITA ◽  
Takeshi ONIZAWA ◽  
...  
Keyword(s):  
Human Body ◽  
Path Loss ◽  
Loss Model ◽  
Path Loss Model ◽  
Outdoor Environments

Geometrical Loss Model

2009 ◽  
Author(s):  
Charaf Ech-Chatbi
Keyword(s):  
Loss Model

A new path loss model based on the volumetric occupancy rate for the pine forests at 5G frequency band

2020 ◽  
pp. 1-10
Author(s):  
Abdullah Genc
Keyword(s):  
Path Loss ◽  
Forest Area ◽  
Occupancy Rate ◽  
Loss Model ◽  
Path Loss Model ◽  
Model Based ◽  
Proposed Model ◽  
Pine Trees ◽  
Foliage Density ◽  
Reference Measurements

Abstract In this paper, a new empirical path loss model based on frequency, distance, and volumetric occupancy rate is generated at the 3.5 and 4.2 GHz in the scope of 5G frequency bands. This study aims to determine the effect of the volumetric occupancy rate on path loss depending on the foliage density of the trees in the pine forest area. Using 4.2 GHz and the effect of the volumetric occupancy rate contributes to the literature in terms of novelty. Both the reference measurements to generate a model and verification measurements to verify the proposed models are conducted in three different regions of the forest area with double ridged horn antennas. These regions of the artificial forest area consist of regularly sorted and identical pine trees. Root mean square error (RMSE) and R-squared values are calculated to evaluate the performance of the proposed model. For 3.5 and 4.2 GHz, while the RMSEs are 3.983 and 3.883, the values of R-squared are 0.967 and 0.963, respectively. Additionally, the results are compared with four path loss models which are commonly used in the forest area. The proposed one has the best performance among the other models with values 3.98 and 3.88 dB for 3.5 and 4.2 GHz.

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