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  "Title": "Empirical Bayesian Tobit Matrix Estimation",
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  "Date": "2024-05-03",
  "Authors@R": "c(\nperson(\"Alton\", \"Barbehenn\", , \"altonbarbehenn@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0009-0000-3364-7204\")),\nperson(\"Sihai Dave\", \"Zhao\", , \"sdzhao@illinois.edu\", role = \"aut\")\n)",
  "Description": "Estimation tools for multidimensional Gaussian means using\nempirical Bayesian g-modeling. Methods are able to handle fully\nobserved data as well as left-, right-, and interval-censored\nobservations (Tobit likelihood); descriptions of these methods\ncan be found in Barbehenn and Zhao (2023)\n<doi:10.48550/arXiv.2306.07239>. Additional, lower-level\nfunctionality based on Kiefer and Wolfowitz (1956)\n<doi:10.1214/aoms/1177728066> and Jiang and Zhang (2009)\n<doi:10.1214/08-AOS638> is provided that can be used to\naccelerate many empirical Bayes and nonparametric maximum\nlikelihood problems.",
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      "page": "ebTobit",
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        "ebTobit"
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