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- W3206998375 abstract "Data harmonization is the process by which an equivalence is developedbetween two variables measuring a common trait. Our problem is motivated bydementia research in which multiple tests are used in practice to measure thesame underlying cognitive ability such as language or memory. We connect thisstatistical problem to mixing distribution estimation. We introduce and study anon-parametric latent trait model, develop a method which enforces uniquenessof the regularized maximum likelihood estimator, show how a nonparametric EMalgorithm will converge weakly to its maximizer, and additionally propose afaster algorithm for learning a discretized approximation of the latentdistribution. Furthermore, we develop methods to assess goodness of fit for themixing likelihood which is an area neglected in most mixing distributionestimation problems. We apply our method to the National Alzheimer'sCoordination Center Uniform Data Set and show that we can use our method toconvert between score measurements and account for the measurement error. Weshow that this method outperforms standard techniques commonly used in dementiaresearch. Full code is available athttps://github.com/SteveJWR/Data-Harmonization-Nonparametric." @default.
- W3206998375 created "2021-10-25" @default.
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- W3206998375 date "2021-10-12" @default.
- W3206998375 modified "2023-10-05" @default.
- W3206998375 title "Data Harmonization Via Regularized Nonparametric Mixing Distribution Estimation" @default.
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