SIMEST: An Algorithm for Simulation Based Estimation of Parameters Characterizing a Stochastic Process

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dc.contributor.authorThompson, James R.en_US
dc.contributor.authorAtkinson, E. Neelyen_US
dc.contributor.authorBrown, Barryen_US
dc.date.accessioned2018-06-18T17:27:11Zen_US
dc.date.available2018-06-18T17:27:11Zen_US
dc.date.issued1986-08en_US
dc.date.noteAugust 1986en_US
dc.description.abstractThe axioms defining stochastic processes are generally simple. However, estimation of the parameters of a process from data is extremely difficult if customary techniques are used. This is due to the complexities involved in obtaining closed forms of likelihoods and evaluating them. The authors develop an estimation technique which selects those parameters which produce simulations that best mimic the data. SIMEST makes stochastic process modelling in oncology (and other fields) an attractive alternative to such currently popular alternatives as ad hoc regression models.en_US
dc.format.extent52 ppen_US
dc.identifier.citationThompson, James R., Atkinson, E. Neely and Brown, Barry. "SIMEST: An Algorithm for Simulation Based Estimation of Parameters Characterizing a Stochastic Process." (1986) <a href="https://hdl.handle.net/1911/101607">https://hdl.handle.net/1911/101607</a>.en_US
dc.identifier.digitalTR86-20en_US
dc.identifier.urihttps://hdl.handle.net/1911/101607en_US
dc.language.isoengen_US
dc.titleSIMEST: An Algorithm for Simulation Based Estimation of Parameters Characterizing a Stochastic Processen_US
dc.typeTechnical reporten_US
dc.type.dcmiTexten_US
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