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Estimation of the parameters of isotropic semivariogram model through bootstrap
Sari K.N.a, Pasaribu U.S.a, Neswan O.a, Permadi A.K.a
a Departments of Mathematics and Natural Sciences, Bandung Institute of Technology, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2015 K.N. Sari et al.In isotropic semivariograms, ordinary least squares can estimate nugget effect and sill by partitioning its range. By conducting simulation, a semivariogram model with previously given parameters will be estimated through bootstrap method. Least square-bootstrap (LS-Bootstrap) will be applied to estimate the parameters of the model after resampling the errors of the model. The selection of the resulting semivariogram model from bootstrap method will be affected by the number of distance lags, the precision level of the range partitions, the number of bootstrap iterations, and the given reference model. The exponential and Gaussian models are sufficiently good in the estimation for the models with the same references. Meanwhile, the estimation yielded from spherical model is quite far from the reference exponential and Gaussian models, with the mean square error value reaching 713. The estimation with bootstrap method which is the same as the reference model will be faster to converge with the maximum iteration of 50. Besides, bootstrap method enables to obtain the point estimates and interval estimates of the nugget effect, sill, and range parameters.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Bootstrap,Confidence interval,Isotropy,Least square,Semivariogram[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.12988/ams.2015.54293[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]