Loading…
Spatial Statistical Models: an overview under the Bayesian Approach
Spatial documentation is exponentially increasing given the availability of Big IoT Data, enabled by the devices miniaturization and data storage capacity. Bayesian spatial statistics is a useful statistical tool to determine the dependence structure and hidden patterns over space through prior know...
Saved in:
| Published in: | arXiv.org 2020-09 |
|---|---|
| Main Authors: | , , |
| Format: | Article |
| Language: | English |
| Subjects: | |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | |
|---|---|
| cites | |
| container_end_page | |
| container_issue | |
| container_start_page | |
| container_title | arXiv.org |
| container_volume | |
| creator | Louzada, Francisco Nascimento, Diego C Osafu, Augustine Egbon |
| description | Spatial documentation is exponentially increasing given the availability of Big IoT Data, enabled by the devices miniaturization and data storage capacity. Bayesian spatial statistics is a useful statistical tool to determine the dependence structure and hidden patterns over space through prior knowledge and data likelihood. Nevertheless, this modeling class is not well explored as the classification and regression machine learning models given their simplicity and often weak (data) independence supposition. In this manner, this systematic review aimed to unravel the main models presented in the literature in the past 20 years, identify gaps, and research opportunities. Elements such as random fields, spatial domains, prior specification, covariance function, and numerical approximations were discussed. This work explored the two subclasses of spatial smoothing global and local. |
| format | article |
| fullrecord | <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2447715721</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2447715721</sourcerecordid><originalsourceid>FETCH-proquest_journals_24477157213</originalsourceid><addsrcrecordid>eNqNykELgjAYxvERBEn5HQadBX03W3QrKbp0srsMfcWJONs7jb59O_QBOv0f-D0rFoEQWXKUABsWE_VpmsJBQZ6LiBXlpL3RAy99KHlTh_2wDQ504nrkdkG3GHzzeWzQcd8hv-gPkgl2niZndd3t2LrVA2H865btb9dncU8Cv2YkX_V2dmOgCqRUKssVZOK_1xftvToI</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2447715721</pqid></control><display><type>article</type><title>Spatial Statistical Models: an overview under the Bayesian Approach</title><source>Publicly Available Content Database</source><creator>Louzada, Francisco ; Nascimento, Diego C ; Osafu, Augustine Egbon</creator><creatorcontrib>Louzada, Francisco ; Nascimento, Diego C ; Osafu, Augustine Egbon</creatorcontrib><description>Spatial documentation is exponentially increasing given the availability of Big IoT Data, enabled by the devices miniaturization and data storage capacity. Bayesian spatial statistics is a useful statistical tool to determine the dependence structure and hidden patterns over space through prior knowledge and data likelihood. Nevertheless, this modeling class is not well explored as the classification and regression machine learning models given their simplicity and often weak (data) independence supposition. In this manner, this systematic review aimed to unravel the main models presented in the literature in the past 20 years, identify gaps, and research opportunities. Elements such as random fields, spatial domains, prior specification, covariance function, and numerical approximations were discussed. This work explored the two subclasses of spatial smoothing global and local.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Bayesian analysis ; Covariance ; Data storage ; Electronic devices ; Fields (mathematics) ; Machine learning ; Miniaturization ; Spatial smoothing ; Statistical analysis ; Statistical models ; Storage capacity</subject><ispartof>arXiv.org, 2020-09</ispartof><rights>2020. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2447715721?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>780,784,25752,37011,44589</link.rule.ids></links><search><creatorcontrib>Louzada, Francisco</creatorcontrib><creatorcontrib>Nascimento, Diego C</creatorcontrib><creatorcontrib>Osafu, Augustine Egbon</creatorcontrib><title>Spatial Statistical Models: an overview under the Bayesian Approach</title><title>arXiv.org</title><description>Spatial documentation is exponentially increasing given the availability of Big IoT Data, enabled by the devices miniaturization and data storage capacity. Bayesian spatial statistics is a useful statistical tool to determine the dependence structure and hidden patterns over space through prior knowledge and data likelihood. Nevertheless, this modeling class is not well explored as the classification and regression machine learning models given their simplicity and often weak (data) independence supposition. In this manner, this systematic review aimed to unravel the main models presented in the literature in the past 20 years, identify gaps, and research opportunities. Elements such as random fields, spatial domains, prior specification, covariance function, and numerical approximations were discussed. This work explored the two subclasses of spatial smoothing global and local.</description><subject>Bayesian analysis</subject><subject>Covariance</subject><subject>Data storage</subject><subject>Electronic devices</subject><subject>Fields (mathematics)</subject><subject>Machine learning</subject><subject>Miniaturization</subject><subject>Spatial smoothing</subject><subject>Statistical analysis</subject><subject>Statistical models</subject><subject>Storage capacity</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNqNykELgjAYxvERBEn5HQadBX03W3QrKbp0srsMfcWJONs7jb59O_QBOv0f-D0rFoEQWXKUABsWE_VpmsJBQZ6LiBXlpL3RAy99KHlTh_2wDQ504nrkdkG3GHzzeWzQcd8hv-gPkgl2niZndd3t2LrVA2H865btb9dncU8Cv2YkX_V2dmOgCqRUKssVZOK_1xftvToI</recordid><startdate>20200930</startdate><enddate>20200930</enddate><creator>Louzada, Francisco</creator><creator>Nascimento, Diego C</creator><creator>Osafu, Augustine Egbon</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20200930</creationdate><title>Spatial Statistical Models: an overview under the Bayesian Approach</title><author>Louzada, Francisco ; Nascimento, Diego C ; Osafu, Augustine Egbon</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_24477157213</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Bayesian analysis</topic><topic>Covariance</topic><topic>Data storage</topic><topic>Electronic devices</topic><topic>Fields (mathematics)</topic><topic>Machine learning</topic><topic>Miniaturization</topic><topic>Spatial smoothing</topic><topic>Statistical analysis</topic><topic>Statistical models</topic><topic>Storage capacity</topic><toplevel>online_resources</toplevel><creatorcontrib>Louzada, Francisco</creatorcontrib><creatorcontrib>Nascimento, Diego C</creatorcontrib><creatorcontrib>Osafu, Augustine Egbon</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Louzada, Francisco</au><au>Nascimento, Diego C</au><au>Osafu, Augustine Egbon</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Spatial Statistical Models: an overview under the Bayesian Approach</atitle><jtitle>arXiv.org</jtitle><date>2020-09-30</date><risdate>2020</risdate><eissn>2331-8422</eissn><abstract>Spatial documentation is exponentially increasing given the availability of Big IoT Data, enabled by the devices miniaturization and data storage capacity. Bayesian spatial statistics is a useful statistical tool to determine the dependence structure and hidden patterns over space through prior knowledge and data likelihood. Nevertheless, this modeling class is not well explored as the classification and regression machine learning models given their simplicity and often weak (data) independence supposition. In this manner, this systematic review aimed to unravel the main models presented in the literature in the past 20 years, identify gaps, and research opportunities. Elements such as random fields, spatial domains, prior specification, covariance function, and numerical approximations were discussed. This work explored the two subclasses of spatial smoothing global and local.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | EISSN: 2331-8422 |
| ispartof | arXiv.org, 2020-09 |
| issn | 2331-8422 |
| language | eng |
| recordid | cdi_proquest_journals_2447715721 |
| source | Publicly Available Content Database |
| subjects | Bayesian analysis Covariance Data storage Electronic devices Fields (mathematics) Machine learning Miniaturization Spatial smoothing Statistical analysis Statistical models Storage capacity |
| title | Spatial Statistical Models: an overview under the Bayesian Approach |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-11T20%3A49%3A43IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=Spatial%20Statistical%20Models:%20an%20overview%20under%20the%20Bayesian%20Approach&rft.jtitle=arXiv.org&rft.au=Louzada,%20Francisco&rft.date=2020-09-30&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E2447715721%3C/proquest%3E%3Cgrp_id%3Ecdi_FETCH-proquest_journals_24477157213%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2447715721&rft_id=info:pmid/&rfr_iscdi=true |