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Activity Details

208 Tue, 8/4/2020, 10:00 AM - 2:00 PM Virtual
Survey Estimation — Contributed Papers
Government Statistics Section
Chair(s): Mary H. Mulry, U.S. Census Bureau
Assessing the Quality of a Coding Process Generated by a Machine Learning Algorithm
Richard Laroche, Statistics Canada; Pier-Olivier Tremblay, Statistics Canada
Maximum Constrained Pseudo-Likelihood Estimation of Income Distributions, Combining Sources
Victor Bustos, INEGI-MEXICO
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
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
Combining Quota and Probability Sub-Sampling Within Enumeration Areas to Produce Reliable Estimates
Isabela Bertolini Coelho, NIC.Br; Marcelo Trindade Pitta,; Pedro Luis do Nascimento Silva, ENCE / Science
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