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The comparison of isotropic and anisotropic semivariogram for Gauss model
Sari R.K.N.a, Pasaribu U.S.a
a Statistics Research Group, Faculty of Mathematics and Natural Science, 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]Semivariogram is one of the models used to study the relationship between the sequence of random variables {Z(s),sεR2} based on the location s. That model is a diagram of variance from the difference between the random variable that distance h, or γ(h) = Var[Z(s)-Z(s+h)]. Experimental variogram can be calculated through observations at several locations. One model that is chosen to be fitted to ̂γ(h) is the Gaussian. In application, ̂γ(h) often considered to depend on the direction (anisotropic semivariogram). This paper develop a nested Gaussian model by considering some angle intervals which is called geometric anisotropy semivariogram. For a case study, the distribution of Bradysia ocellaris insects at a Oyster Mushrooms cultivication is analyzed that the insects fly to follow the direction of light. © 2014 AIP Publishing LLC.[/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]anisotropic,Gaussian model,Geometric anisotropy,isotropic,Semivariograms[/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]and semivariogram,anisotropic,Gaussian model,geometric anisotropy,isotropic[/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/10.1063/1.4868855[/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]