A comparison of sampling grids, cut-off distance and type of residuals in parametric variogram estimation

In spatial statistics, the correct identification of a variogram model when fitted to an empirical variogram depends on many factors. Here, simulation experiments show fitting based on the variogram cloud is preferable to that based on Matheron's and Cressie-Hawkins empirical variogram estimato...

Full description

Saved in:
Bibliographic Details
Published in:Communications in statistics. Simulation and computation 2017-03, Vol.46 (3), p.1781-1795
Main Authors: Jin, Renhao, Kelly, Gabrielle E.
Format: Article
Language:English
Subjects:
Computer simulation
Cut-off
Estimators
Fittings
Matérn
Practical range
Samples
Sampling
Specifications
Statistics
Studentized residuals
Variogram cloud
Variogram model fitting
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c418t-6ee29f6d24e1e00277f2b668319540905b7c0075a00c139a99f556c2500332323
cites cdi_FETCH-LOGICAL-c418t-6ee29f6d24e1e00277f2b668319540905b7c0075a00c139a99f556c2500332323
container_end_page 1795
container_issue 3
container_start_page 1781
container_title Communications in statistics. Simulation and computation
container_volume 46
creator Jin, Renhao
Kelly, Gabrielle E.
description In spatial statistics, the correct identification of a variogram model when fitted to an empirical variogram depends on many factors. Here, simulation experiments show fitting based on the variogram cloud is preferable to that based on Matheron's and Cressie-Hawkins empirical variogram estimators. For correct model specification, a number of models should be fitted to the empirical variogram using a grid of cut-off values, and recommendations are given for best choice. A design where roughly half the maximum distance between points equals the practical range works well for correct variogram identification of any model, with varying nugget sizes and sample sizes.
doi_str_mv 10.1080/03610918.2015.1011785
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1904243709</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1904243709</sourcerecordid><originalsourceid>FETCH-LOGICAL-c418t-6ee29f6d24e1e00277f2b668319540905b7c0075a00c139a99f556c2500332323</originalsourceid><addsrcrecordid>eNp9kU9rHSEUxaU00NckHyEgdNNFJrlXxxndNYT-CQS6adZiHH0YZnSqMwnv28fhJZsuiouL8rvneu4h5ALhCkHCNfAOQaG8YoCiPiH2UnwgOxScNS22-JHsNqbZoE_kcylPAMBlK3ck3FCbptnkUFKkydNipnkMcU_3OQzlktp1aZL3dAhlMdE6auJAl8PsNji7EobVjIWGSKuImdySg6XPVS_t65W6soTJLCHFM3LiK-nO3-opefjx_c_tr-b-98-725v7xrYol6ZzjinfDax16ABY33v22HWSoxItKBCPvQXohQGwyJVRygvRWSaqI87qOSVfj7pzTn_XOl9PoVg3jia6tBaNClrW8h5URb_8gz6lNcf6O42ylxL6jkOlxJGyOZWSnddzrp7yQSPoLQD9HoDeAtBvAdS-b8e-EH3Kk3lJeRz0Yg5jyj7XXYai-f8lXgHBHosH</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1878807630</pqid></control><display><type>article</type><title>A comparison of sampling grids, cut-off distance and type of residuals in parametric variogram estimation</title><source>Taylor and Francis Science and Technology Collection</source><creator>Jin, Renhao ; Kelly, Gabrielle E.</creator><creatorcontrib>Jin, Renhao ; Kelly, Gabrielle E.</creatorcontrib><description>In spatial statistics, the correct identification of a variogram model when fitted to an empirical variogram depends on many factors. Here, simulation experiments show fitting based on the variogram cloud is preferable to that based on Matheron's and Cressie-Hawkins empirical variogram estimators. For correct model specification, a number of models should be fitted to the empirical variogram using a grid of cut-off values, and recommendations are given for best choice. A design where roughly half the maximum distance between points equals the practical range works well for correct variogram identification of any model, with varying nugget sizes and sample sizes.</description><identifier>ISSN: 0361-0918</identifier><identifier>EISSN: 1532-4141</identifier><identifier>DOI: 10.1080/03610918.2015.1011785</identifier><language>eng</language><publisher>Philadelphia: Taylor &amp; Francis</publisher><subject>Computer simulation ; Cut-off ; Estimators ; Fittings ; Matérn ; Practical range ; Samples ; Sampling ; Specifications ; Statistics ; Studentized residuals ; Variogram cloud ; Variogram model fitting</subject><ispartof>Communications in statistics. Simulation and computation, 2017-03, Vol.46 (3), p.1781-1795</ispartof><rights>2017 Taylor &amp; Francis Group, LLC 2017</rights><rights>2017 Taylor &amp; Francis Group, LLC</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c418t-6ee29f6d24e1e00277f2b668319540905b7c0075a00c139a99f556c2500332323</citedby><cites>FETCH-LOGICAL-c418t-6ee29f6d24e1e00277f2b668319540905b7c0075a00c139a99f556c2500332323</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27900,27901</link.rule.ids></links><search><creatorcontrib>Jin, Renhao</creatorcontrib><creatorcontrib>Kelly, Gabrielle E.</creatorcontrib><title>A comparison of sampling grids, cut-off distance and type of residuals in parametric variogram estimation</title><title>Communications in statistics. Simulation and computation</title><description>In spatial statistics, the correct identification of a variogram model when fitted to an empirical variogram depends on many factors. Here, simulation experiments show fitting based on the variogram cloud is preferable to that based on Matheron's and Cressie-Hawkins empirical variogram estimators. For correct model specification, a number of models should be fitted to the empirical variogram using a grid of cut-off values, and recommendations are given for best choice. A design where roughly half the maximum distance between points equals the practical range works well for correct variogram identification of any model, with varying nugget sizes and sample sizes.</description><subject>Computer simulation</subject><subject>Cut-off</subject><subject>Estimators</subject><subject>Fittings</subject><subject>Matérn</subject><subject>Practical range</subject><subject>Samples</subject><subject>Sampling</subject><subject>Specifications</subject><subject>Statistics</subject><subject>Studentized residuals</subject><subject>Variogram cloud</subject><subject>Variogram model fitting</subject><issn>0361-0918</issn><issn>1532-4141</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp9kU9rHSEUxaU00NckHyEgdNNFJrlXxxndNYT-CQS6adZiHH0YZnSqMwnv28fhJZsuiouL8rvneu4h5ALhCkHCNfAOQaG8YoCiPiH2UnwgOxScNS22-JHsNqbZoE_kcylPAMBlK3ck3FCbptnkUFKkydNipnkMcU_3OQzlktp1aZL3dAhlMdE6auJAl8PsNji7EobVjIWGSKuImdySg6XPVS_t65W6soTJLCHFM3LiK-nO3-opefjx_c_tr-b-98-725v7xrYol6ZzjinfDax16ABY33v22HWSoxItKBCPvQXohQGwyJVRygvRWSaqI87qOSVfj7pzTn_XOl9PoVg3jia6tBaNClrW8h5URb_8gz6lNcf6O42ylxL6jkOlxJGyOZWSnddzrp7yQSPoLQD9HoDeAtBvAdS-b8e-EH3Kk3lJeRz0Yg5jyj7XXYai-f8lXgHBHosH</recordid><startdate>20170316</startdate><enddate>20170316</enddate><creator>Jin, Renhao</creator><creator>Kelly, Gabrielle E.</creator><general>Taylor &amp; Francis</general><general>Taylor &amp; Francis Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20170316</creationdate><title>A comparison of sampling grids, cut-off distance and type of residuals in parametric variogram estimation</title><author>Jin, Renhao ; Kelly, Gabrielle E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c418t-6ee29f6d24e1e00277f2b668319540905b7c0075a00c139a99f556c2500332323</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Computer simulation</topic><topic>Cut-off</topic><topic>Estimators</topic><topic>Fittings</topic><topic>Matérn</topic><topic>Practical range</topic><topic>Samples</topic><topic>Sampling</topic><topic>Specifications</topic><topic>Statistics</topic><topic>Studentized residuals</topic><topic>Variogram cloud</topic><topic>Variogram model fitting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jin, Renhao</creatorcontrib><creatorcontrib>Kelly, Gabrielle E.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Communications in statistics. Simulation and computation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jin, Renhao</au><au>Kelly, Gabrielle E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A comparison of sampling grids, cut-off distance and type of residuals in parametric variogram estimation</atitle><jtitle>Communications in statistics. Simulation and computation</jtitle><date>2017-03-16</date><risdate>2017</risdate><volume>46</volume><issue>3</issue><spage>1781</spage><epage>1795</epage><pages>1781-1795</pages><issn>0361-0918</issn><eissn>1532-4141</eissn><abstract>In spatial statistics, the correct identification of a variogram model when fitted to an empirical variogram depends on many factors. Here, simulation experiments show fitting based on the variogram cloud is preferable to that based on Matheron's and Cressie-Hawkins empirical variogram estimators. For correct model specification, a number of models should be fitted to the empirical variogram using a grid of cut-off values, and recommendations are given for best choice. A design where roughly half the maximum distance between points equals the practical range works well for correct variogram identification of any model, with varying nugget sizes and sample sizes.</abstract><cop>Philadelphia</cop><pub>Taylor &amp; Francis</pub><doi>10.1080/03610918.2015.1011785</doi><tpages>15</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0361-0918
ispartof Communications in statistics. Simulation and computation, 2017-03, Vol.46 (3), p.1781-1795
issn 0361-0918
1532-4141
language eng
recordid cdi_proquest_miscellaneous_1904243709
source Taylor and Francis Science and Technology Collection
subjects Computer simulation
Cut-off
Estimators
Fittings
Matérn
Practical range
Samples
Sampling
Specifications
Statistics
Studentized residuals
Variogram cloud
Variogram model fitting
title A comparison of sampling grids, cut-off distance and type of residuals in parametric variogram estimation
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-24T13%3A36%3A34IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20comparison%20of%20sampling%20grids,%20cut-off%20distance%20and%20type%20of%20residuals%20in%20parametric%20variogram%20estimation&rft.jtitle=Communications%20in%20statistics.%20Simulation%20and%20computation&rft.au=Jin,%20Renhao&rft.date=2017-03-16&rft.volume=46&rft.issue=3&rft.spage=1781&rft.epage=1795&rft.pages=1781-1795&rft.issn=0361-0918&rft.eissn=1532-4141&rft_id=info:doi/10.1080/03610918.2015.1011785&rft_dat=%3Cproquest_cross%3E1904243709%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c418t-6ee29f6d24e1e00277f2b668319540905b7c0075a00c139a99f556c2500332323%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1878807630&rft_id=info:pmid/&rfr_iscdi=true