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10.1080/01621459.2015.1006365 http://scihub22266oqcxt.onion/10.1080/01621459.2015.1006365 C4874345!4874345!27212739     free     free     free       J+Am+Stat+Assoc 2016 ; 111 (513): 314-30 Nephropedia Template TP {{tp|p=27212739|t=2016. Stepwise Signal Extraction via Marginal Likelihood |pdf=|usr=}}{{27212739}} gab.com Text Stepwise Signal Extraction via Marginal Likelihood JO: J Am Stat Assoc http://www.kidney.de/pget.php?&tw=1&pmid=27212739 #FOAvip This paper studies the estimation of stepwise signal. To determine the number and locations of change-points of the stepwise signal, we formulate a maximum marginal likelihood estimator, which can be computed with a quadratic cost using dynamic programming. We carry out extensive investigation on the choice of the prior distribution and study the asymptotic properties of the maximum marginal likelihood estimator. We propose to treat each possible set of change-points equally and adopt an empirical Bayes approach to specify the prior distribution of segment parameters. Detailed simulation study is performed to compare the effectiveness of this method with other existing methods. We demonstrate our method on single-molecule enzyme reaction data and on DNA array CGH data. Our study shows that this method is applicable to a wide range of models and offers appealing results in practice. Twit Text FOAVip Stepwise Signal Extraction via Marginal Likelihood ????J Am Stat Assoc http://www.kidney.de/pget.php?&tw=1&pmid=27212739 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27212739 #FOAvip English Wikipedia <ref>{{Cite pmid|27212739}}</ref> Stepwise Signal Extraction via Marginal Likelihood #MMPMID27212739 Du C; Kao CLM; Kou SC J Am Stat Assoc 2016[]; 111 (513): 314-30 PMID27212739show ga This paper studies the estimation of stepwise signal. To determine the number and locations of change-points of the stepwise signal, we formulate a maximum marginal likelihood estimator, which can be computed with a quadratic cost using dynamic programming. We carry out extensive investigation on the choice of the prior distribution and study the asymptotic properties of the maximum marginal likelihood estimator. We propose to treat each possible set of change-points equally and adopt an empirical Bayes approach to specify the prior distribution of segment parameters. Detailed simulation study is performed to compare the effectiveness of this method with other existing methods. We demonstrate our method on single-molecule enzyme reaction data and on DNA array CGH data. Our study shows that this method is applicable to a wide range of models and offers appealing results in practice. äStepwise Signal Extraction via Marginal Likelihood (epmc).pdf DeepDyve Pubget Overpricing