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NOTE: Beginning with SAS/STAT 13.1 in SAS 9.4 TS1M1, the functionality of these macros has been updated and added to the ICLIFETEST procedure. For details, see the ICLIFETEST documentation.
PURPOSE:
These macros compute nonparametric maximum likelihood estimates (NPMLEs) of survival curves from interval-censored data. Confidence intervals for survival curves and log-rank tests comparing survival curves from several groups are also provided.</description><link>https://support.sas.com/kb/24/980.html</link><dc:creator>benzsab1</dc:creator><dc:date>2026-07-29T15:10:12+02:00</dc:date><dc:subject>estimation nonparametric </dc:subject><content:encoded>&lt;span itemprop=&#034;description&#034;&gt;These macros compute nonparametric survival curve estimates from interval-censored data. Confidence intervals for survival curves and log-rank tests comparing survival curves from several groups are also provided.
NOTE: Beginning with SAS/STAT 13.1 in SAS 9.4 TS1M1, the functionality of these macros has been updated and added to the ICLIFETEST procedure. For details, see the ICLIFETEST documentation.
PURPOSE:
These macros compute nonparametric maximum likelihood estimates (NPMLEs) of survival curves from interval-censored data. 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data-versiondate=&#034;2023-12-19T17:30:13+01:00&#034; href=&#034;https://sites.google.com/site/doingbayesiandataanalysis/home&#034; rel=&#034;nofollow&#034; class=&#034;description-link&#034;&gt;https://sites.google.com/site/doingbayesiandataanalysis/home&lt;/a&gt;</content:encoded><taxo:topics><rdf:Bag><rdf:li rdf:resource="https://www.bibsonomy.org/tag/bayesian"/></rdf:Bag></taxo:topics></item><item rdf:about="https://maxhalford.github.io/blog/bayesian-linear-regression/"><title>Bayesian linear regression for practitioners • Max Halford</title><description></description><link>https://maxhalford.github.io/blog/bayesian-linear-regression/</link><dc:creator>becker</dc:creator><dc:date>2023-11-12T11:30:51+01:00</dc:date><dc:subject>bayesian linear regression tutorial explained theory </dc:subject><content:encoded>&lt;a itemprop=&#034;url&#034; data-versiondate=&#034;2023-11-12T11:30:51+01:00&#034; href=&#034;https://maxhalford.github.io/blog/bayesian-linear-regression/&#034; rel=&#034;nofollow&#034; 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data-versiondate=&#034;2022-12-13T14:44:45+01:00&#034; href=&#034;https://gregorygundersen.com/blog/2020/02/04/bayesian-linear-regression/&#034; rel=&#034;nofollow&#034; class=&#034;description-link&#034;&gt;https://gregorygundersen.com/blog/2020/02/04/bayesian-linear-regression/&lt;/a&gt;</content:encoded><taxo:topics><rdf:Bag><rdf:li rdf:resource="https://www.bibsonomy.org/tag/bayesian"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/linear"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/background"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/bayes"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/regression"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/knowledge"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/integration"/></rdf:Bag></taxo:topics></item><item rdf:about="https://github.com/cornellius-gp/gpytorch"><title>GitHub - cornellius-gp/gpytorch: A highly efficient and modular implementation of Gaussian Processes in PyTorch</title><description></description><link>https://github.com/cornellius-gp/gpytorch</link><dc:creator>becker</dc:creator><dc:date>2022-04-04T22:04:13+02:00</dc:date><dc:subject>bayesian process processes python inference pytorch learning gpu gaussian torch </dc:subject><content:encoded>&lt;a itemprop=&#034;url&#034; 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Active learning aims at red&lt;/span&gt;</content:encoded><taxo:topics><rdf:Bag><rdf:li rdf:resource="https://www.bibsonomy.org/tag/bayesian"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/machine-learning"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/2021"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/springer"/></rdf:Bag></taxo:topics></item><item rdf:about="https://www.estimationstats.com/#/"><title>Estimation Stats</title><description></description><link>https://www.estimationstats.com/#/</link><dc:creator>becker</dc:creator><dc:date>2021-04-20T18:25:54+02:00</dc:date><dc:subject>matlab estimation r library plot python size effect statistic statistics </dc:subject><content:encoded>&lt;a itemprop=&#034;url&#034; data-versiondate=&#034;2021-04-20T18:25:54+02:00&#034; href=&#034;https://www.estimationstats.com/#/&#034; rel=&#034;nofollow&#034; class=&#034;description-link&#034;&gt;https://www.estimationstats.com/#/&lt;/a&gt;</content:encoded><taxo:topics><rdf:Bag><rdf:li rdf:resource="https://www.bibsonomy.org/tag/matlab"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/estimation"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/r"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/library"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/plot"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/python"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/size"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/effect"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/statistic"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/statistics"/></rdf:Bag></taxo:topics></item><item rdf:about="http://web.eecs.umich.edu/~hero/Preprints/hero_duke_SAHD15.pdf"><title>Large scale correlation mining: fundamental performance limits</title><description></description><link>http://web.eecs.umich.edu/~hero/Preprints/hero_duke_SAHD15.pdf</link><dc:creator>becker</dc:creator><dc:date>2021-01-07T18:16:09+01:00</dc:date><dc:subject>estimation statistical mining correlation test paper:fastcor </dc:subject><content:encoded>&lt;a itemprop=&#034;url&#034; 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