Generalised additive mixed models analysis via gammSlice
Summary We demonstrate the use of our R package, gammSlice, for Bayesian fitting and inference in generalised additive mixed model analysis. This class of models includes generalised linear mixed models and generalised additive models as special cases. Accurate Bayesian inference is achievable via s...
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| Veröffentlicht in: | Australian & New Zealand journal of statistics 2018-09, Vol.60 (3), p.279-300 |
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| container_end_page | 300 |
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| container_start_page | 279 |
| container_title | Australian & New Zealand journal of statistics |
| container_volume | 60 |
| creator | Pham, Tung H. Wand, Matt P. |
| description | Summary
We demonstrate the use of our R package, gammSlice, for Bayesian fitting and inference in generalised additive mixed model analysis. This class of models includes generalised linear mixed models and generalised additive models as special cases. Accurate Bayesian inference is achievable via sufficiently large Markov chain Monte Carlo (MCMC) samples. Slice sampling is a key component of the MCMC scheme. Comparisons with existing generalised additive mixed model software shows that gammSlice offers improved inferential accuracy, albeit at the cost of longer computational time.
Accurate generalised additive mixed model analyses is challenging. Solutions are provided via a package in the R language. Several illustrations are provided. |
| doi_str_mv | 10.1111/anzs.12241 |
| format | Article |
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We demonstrate the use of our R package, gammSlice, for Bayesian fitting and inference in generalised additive mixed model analysis. This class of models includes generalised linear mixed models and generalised additive models as special cases. Accurate Bayesian inference is achievable via sufficiently large Markov chain Monte Carlo (MCMC) samples. Slice sampling is a key component of the MCMC scheme. Comparisons with existing generalised additive mixed model software shows that gammSlice offers improved inferential accuracy, albeit at the cost of longer computational time.
Accurate generalised additive mixed model analyses is challenging. Solutions are provided via a package in the R language. Several illustrations are provided.</description><identifier>ISSN: 1369-1473</identifier><identifier>EISSN: 1467-842X</identifier><identifier>DOI: 10.1111/anzs.12241</identifier><language>eng</language><publisher>Hoboken: Wiley Subscription Services, Inc</publisher><subject>Bayesian analysis ; Computer simulation ; Computing time ; generalised additive models ; generalised linear mixed models ; Markov analysis ; Markov chain Monte Carlo ; Markov chains ; Monte Carlo simulation ; penalised splines ; slice sampling ; Statistical inference</subject><ispartof>Australian & New Zealand journal of statistics, 2018-09, Vol.60 (3), p.279-300</ispartof><rights>2018 Australian Statistical Publishing Association Inc. Published by John Wiley & Sons Australia Pty Ltd.</rights><rights>Copyright © 2018 Australian Statistical Publishing Association Inc.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c2601-a51d378a42ee544d04fdece3d9d4fb2fc8191f2668aeb10496f8eb86392873623</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fanzs.12241$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fanzs.12241$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids></links><search><creatorcontrib>Pham, Tung H.</creatorcontrib><creatorcontrib>Wand, Matt P.</creatorcontrib><title>Generalised additive mixed models analysis via gammSlice</title><title>Australian & New Zealand journal of statistics</title><description>Summary
We demonstrate the use of our R package, gammSlice, for Bayesian fitting and inference in generalised additive mixed model analysis. This class of models includes generalised linear mixed models and generalised additive models as special cases. Accurate Bayesian inference is achievable via sufficiently large Markov chain Monte Carlo (MCMC) samples. Slice sampling is a key component of the MCMC scheme. Comparisons with existing generalised additive mixed model software shows that gammSlice offers improved inferential accuracy, albeit at the cost of longer computational time.
Accurate generalised additive mixed model analyses is challenging. Solutions are provided via a package in the R language. Several illustrations are provided.</description><subject>Bayesian analysis</subject><subject>Computer simulation</subject><subject>Computing time</subject><subject>generalised additive models</subject><subject>generalised linear mixed models</subject><subject>Markov analysis</subject><subject>Markov chain Monte Carlo</subject><subject>Markov chains</subject><subject>Monte Carlo simulation</subject><subject>penalised splines</subject><subject>slice sampling</subject><subject>Statistical inference</subject><issn>1369-1473</issn><issn>1467-842X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp9kMFKAzEQhoMoWKsXn2DBm7A1k6TZ5FiKVqHooQriJaSbiaTsdmvSVtend-t6di4zP3wzDB8hl0BH0NWNXX-nETAm4IgMQMgiV4K9HnczlzoHUfBTcpbSilIQlMsBUTNcY7RVSOgy61zYhj1mdfjqYt04rFJm17ZqU0jZPtjs3db1ogolnpMTb6uEF399SF7ubp-n9_n8afYwnczzkkkKuR2D44WygiGOhXBUeIclcqed8EvmSwUaPJNSWVwCFVp6hUsluWaq4JLxIbnq725i87HDtDWrZhe7l5JhVGsmx4UWHXXdU2VsUorozSaG2sbWADUHM-Zgxvya6WDo4c9QYfsPaSaPb4t-5wfiG2Vu</recordid><startdate>201809</startdate><enddate>201809</enddate><creator>Pham, Tung H.</creator><creator>Wand, Matt P.</creator><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>H8D</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>201809</creationdate><title>Generalised additive mixed models analysis via gammSlice</title><author>Pham, Tung H. ; Wand, Matt P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2601-a51d378a42ee544d04fdece3d9d4fb2fc8191f2668aeb10496f8eb86392873623</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Bayesian analysis</topic><topic>Computer simulation</topic><topic>Computing time</topic><topic>generalised additive models</topic><topic>generalised linear mixed models</topic><topic>Markov analysis</topic><topic>Markov chain Monte Carlo</topic><topic>Markov chains</topic><topic>Monte Carlo simulation</topic><topic>penalised splines</topic><topic>slice sampling</topic><topic>Statistical inference</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pham, Tung H.</creatorcontrib><creatorcontrib>Wand, Matt P.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Aerospace 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>Australian & New Zealand journal of statistics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pham, Tung H.</au><au>Wand, Matt P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Generalised additive mixed models analysis via gammSlice</atitle><jtitle>Australian & New Zealand journal of statistics</jtitle><date>2018-09</date><risdate>2018</risdate><volume>60</volume><issue>3</issue><spage>279</spage><epage>300</epage><pages>279-300</pages><issn>1369-1473</issn><eissn>1467-842X</eissn><abstract>Summary
We demonstrate the use of our R package, gammSlice, for Bayesian fitting and inference in generalised additive mixed model analysis. This class of models includes generalised linear mixed models and generalised additive models as special cases. Accurate Bayesian inference is achievable via sufficiently large Markov chain Monte Carlo (MCMC) samples. Slice sampling is a key component of the MCMC scheme. Comparisons with existing generalised additive mixed model software shows that gammSlice offers improved inferential accuracy, albeit at the cost of longer computational time.
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| identifier | ISSN: 1369-1473 |
| ispartof | Australian & New Zealand journal of statistics, 2018-09, Vol.60 (3), p.279-300 |
| issn | 1369-1473 1467-842X |
| language | eng |
| recordid | cdi_proquest_journals_2099265794 |
| source | Wiley Online Library Journals Frontfile Complete |
| subjects | Bayesian analysis Computer simulation Computing time generalised additive models generalised linear mixed models Markov analysis Markov chain Monte Carlo Markov chains Monte Carlo simulation penalised splines slice sampling Statistical inference |
| title | Generalised additive mixed models analysis via gammSlice |
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