01757oam 2200517 450 991070688460332120180213104852.0(CKB)5470000002458618(OCoLC)965753683(OCoLC)896810333(OCoLC)995470000002458618(EXLCZ)99547000000245861820161210d1975 ua 0engurmn|||||||||txtrdacontentcrdamediacrrdacarrierSedimentation and tectonics in the early Tertiary continental borderland of central California /by Tor H. Nilsen and Samuel H. Clarke, JrWashington :United States Department of the Interior, Geological Survey,1975.1 online resource (iv, 64 pages) illustrations, mapsGeological Survey professional paper ;925Includes bibliographical references (pages 54-64).Geology, StratigraphicTertiaryGeologyCaliforniaGeologyfastGeology, StratigraphicfastTertiary Geologic PeriodfastCaliforniafastGeology, StratigraphicGeologyGeology.Geology, Stratigraphic.Tertiary Geologic Period.Nilsen Tor Helge1388950Clarke Samuel H.Geological Survey (U.S.),OCLCEOCLCEOCLCQCOPOCLCFGPOBOOK9910706884603321Sedimentation and tectonics in the early Tertiary continental borderland of central California3495999UNINA03330nam 22006975 450 991030413270332120230810213508.03-658-08393-X10.1007/978-3-658-08393-9(CKB)3710000000324641(EBL)1967643(OCoLC)908087543(SSID)ssj0001407824(PQKBManifestationID)11807636(PQKBTitleCode)TC0001407824(PQKBWorkID)11411345(PQKB)10651393(DE-He213)978-3-658-08393-9(MiAaPQ)EBC1967643(PPN)183151496(EXLCZ)99371000000032464120141226d2015 u| 0engur|n|---|||||txtccrBayesian Analysis of Failure Time Data Using P-Splines /by Matthias Kaeding1st ed. 2015.Wiesbaden :Springer Fachmedien Wiesbaden :Imprint: Springer Spektrum,2015.1 online resource (117 p.)BestMasters,2625-3615Description based upon print version of record.3-658-08392-1 Includes bibliographical references.Relative Risk and Log-Location-Scale Family -- Bayesian P-Splines -- Discrete Time Models -- Continuous Time Models.Matthias Kaeding discusses Bayesian methods for analyzing discrete and continuous failure times where the effect of time and/or covariates is modeled via P-splines and additional basic function expansions, allowing the replacement of linear effects by more general functions. The MCMC methodology for these models is presented in a unified framework and applied on data sets. Among others, existing algorithms for the grouped Cox and the piecewise exponential model under interval censoring are combined with a data augmentation step for the applications. The author shows that the resulting Gibbs sampler works well for the grouped Cox and is merely adequate for the piecewise exponential model. Contents Relative Risk and Log-Location-Scale Family Bayesian P-Splines Discrete Time Models Continuous Time Models Target Groups Researchers and students in the fields of statistics, engineering, and life sciences Practitioners in the fields of reliability engineering and data analysis involved with lifetimes The Author Matthias Kaeding obtained his Master of Science degree at the University of Bamberg in Survey Statistics.BestMasters,2625-3615ProbabilitiesMedicineResearchBiologyResearchBioinformaticsProbability TheoryBiomedical ResearchBioinformaticsProbabilities.MedicineResearch.BiologyResearch.Bioinformatics.Probability Theory.Biomedical Research.Bioinformatics.510519.2570285610724Kaeding Matthiasauthttp://id.loc.gov/vocabulary/relators/aut893250BOOK9910304132703321Bayesian Analysis of Failure Time Data Using P-Splines1995411UNINA