PARAMETERS ESTIMATON OF ROBUST VARIOGRAM ON PROPERTY INSURANCE IN BANDUNG USING GAUSS- NEWTON METHOD AND GENETIC ALGORITHM
Variogram analysis is a method that widely used in various fields to describe the nature of spatial correlation of data between locations. One of interesting observation is the distribution of claim values of property insurance based on the location. In general, the claim values of property insur...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/46573 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Variogram analysis is a method that widely used in various fields to describe the
nature of spatial correlation of data between locations. One of interesting
observation is the distribution of claim values of property insurance based on the
location. In general, the claim values of property insurance is a highly skewed data,
so natural logarithmic transformations need to be done to reduce the skewness and
variance of the data. Besides that, a robust variogram approach is needed in the
modeling. One important step in modeling a variogram is parameter estimation.
Unfortunately, simultaneous parameter estimation is not easy to do because of the
nonlinearity of variogram model functions
.
In this thesis the Gauss-Newton method and genetic algorithm are used as a method
of estimating parameters that can estimate variogram parameters simultaneously.
The Gauss-Newton method provides a parameter estimate quickly, a maximum of
51 iterations, but the results are possible to get out of bond of variogram
parameters. For example, there is negative number for estimated nugget value and
this is out of bond because nugget is a measure of variability that is not naturally
negative. In genetic algorithms we can limit the value of estimated parameters so
this method is more reliable. However, one of the difficulties of genetic algorithms
is to determine the stopping criteria based on the mean square error (MSE) between
the variogram model and the experimental variogram.
Both methods can estimate variogram parameters well, based on MSE values that
are not significantly different, with a level of accuracy of 10?3
. Then validation of
the best model is using the Jackknife Kriging method, and the spherical model that
estimated by Gauss-Newton method was selected as the best model with the root
mean square error (RMSE) of 58.4 million rupiah. |
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