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Activity Details
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208
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Tue, 8/4/2020,
10:00 AM -
2:00 PM
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Virtual
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Survey Estimation — Contributed Papers
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Government Statistics Section
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Chair(s): Mary H. Mulry, U.S. Census Bureau
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Assessing the Quality of a Coding Process Generated by a Machine Learning Algorithm
Richard Laroche, Statistics Canada; Pier-Olivier Tremblay, Statistics Canada
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Maximum Constrained Pseudo-Likelihood Estimation of Income Distributions, Combining Sources
Victor Bustos, INEGI-MEXICO
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Using Machine Learning Algorithms and Impact Scores to Manage Cost, Burden, and Data Quality of the Agricultural Resource Management Survey
Gavin Corral, USDA NASS; Tyler Wilson, USDA NASS; Andrew Dau, USDA NASS; Audra Zakzeski, USDA NASS
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Process-Driven Metrics and Process Evaluation of Bundled Interventions: The Agriculture to Nutrition (ATONU) Trial
Evidence Matangi; George McCabe, Purdue University; Tshilidzi Madzivhandila, FANRPAN; Farai Gwelo, FANRPAN; Bertha Munthali, FANRPAN; Simbarashe Sibanda, FANRPAN; Wafaie Fawzi, Harvard; Nilupa Gunaratna, Purdue University
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Combining Quota and Probability Sub-Sampling Within Enumeration Areas to Produce Reliable Estimates
Isabela Bertolini Coelho, NIC.Br; Marcelo Trindade Pitta, NIC.br; Pedro Luis do Nascimento Silva, ENCE / Science
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Imposing Sparseness in a Bayesian Hierarchical Regression Model with Temporal Smoothing via the Horseshoe Prior with an Application to Estimate Stillbirths for All Countries
zhengfan Wang, umass-Amherst; Leontine Alkema, University of Massachussetts; Miranda Fix, University of Washington; Jon Wakefield, Departments of Biostatistics and Statistics, University of WAshington; Hannah Blencowe, LSHTM; Lucia Hug, UNICEF; Danzhen You, UNICEF; Anupam Mishra, UNICEF
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