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This special issue of Sankhya Series A is dedicated to the memory of Professor Debabrata Basu, popularly known as D. Basu, on the occasion of his birth centenary. We pay tribute to one of the finest statisticians, a great thinker, and a world-renowned teacher. D. Basu is internationally known for his immensely important contributions to understanding the foundations of statistical inference. His work was marked by profound simplicity and unparalleled ability to make statistical theory instructive and enjoyable.D. Basu was born on July 5, 1924, in undivided India in the city of Dhaka, now the capital of Bangladesh. In late 1946, he graduated from Dhaka University with an M.A. in Pure Mathematics. After going through the tragic political turmoil following the partition of India, his family moved to Kolkata as refugees. He briefly returned to Dhaka after being offered a faculty position at Dhaka University. However, due to growing political unrest, he was forced to return to Kolkata almost immediately. He joined the Indian Statistical Institute in September 1950 as a research fellow under the supervision of C. R. Rao. At that time, Abraham Wald was about to visit the Indian Statistical Institute (ISI). Basu started reading Wald's works before his visit. Being a trained mathematician, Basu was fascinated by Wald's decision-theoretic formulation of statistics. He found Wald's definition of the best procedure as admissible and minimax was unlike anything he had seen in the statistical literature then. While reading one of Wald's results about minimaxity, Basu found a flaw in the argument. The argument breaks down if the maximum risk is infinite. When he met Wald, Basu excitedly mentioned that to Wald, who famously replied, "Of course, they are all wrong," jokingly; by that, Wald meant that appropriate conditions are needed to ensure their validity. 0123456789().: V,-vol
This special issue of Sankhya Series A is dedicated to the memory of Professor Debabrata Basu, popularly known as D. Basu, on the occasion of his birth centenary. We pay tribute to one of the finest statisticians, a great thinker, and a world-renowned teacher. D. Basu is internationally known for his immensely important contributions to understanding the foundations of statistical inference. His work was marked by profound simplicity and unparalleled ability to make statistical theory instructive and enjoyable.D. Basu was born on July 5, 1924, in undivided India in the city of Dhaka, now the capital of Bangladesh. In late 1946, he graduated from Dhaka University with an M.A. in Pure Mathematics. After going through the tragic political turmoil following the partition of India, his family moved to Kolkata as refugees. He briefly returned to Dhaka after being offered a faculty position at Dhaka University. However, due to growing political unrest, he was forced to return to Kolkata almost immediately. He joined the Indian Statistical Institute in September 1950 as a research fellow under the supervision of C. R. Rao. At that time, Abraham Wald was about to visit the Indian Statistical Institute (ISI). Basu started reading Wald's works before his visit. Being a trained mathematician, Basu was fascinated by Wald's decision-theoretic formulation of statistics. He found Wald's definition of the best procedure as admissible and minimax was unlike anything he had seen in the statistical literature then. While reading one of Wald's results about minimaxity, Basu found a flaw in the argument. The argument breaks down if the maximum risk is infinite. When he met Wald, Basu excitedly mentioned that to Wald, who famously replied, "Of course, they are all wrong," jokingly; by that, Wald meant that appropriate conditions are needed to ensure their validity. 0123456789().: V,-vol
No abstract
National forest inventory (NFI) programs provide vital information on forest parameters' status, trend, and change. Most NFI designs and estimation methods are tailored to estimate status over large areas but are not well suited to estimate trend and change, especially over small spatial areas and/or over short time periods (e.g., annual estimates). Fine-scale space-time indexed estimates are critical to a variety of environmental, ecological, and economic monitoring efforts. In the United States, for example, NFI data are used to estimate forest carbon status, trend, and change to support national, state, and local user group needs. Increasingly, these users seek finer spatial and temporal scale estimates to evaluate existing land use policies and management practices, and inform future activities. Here we propose a spatio-temporal Bayesian small area estimation modeling framework that delivers statistically valid estimates with complete uncertainty quantification for status, trend, and change. The framework accommodates a variety of space and time dependency structures, and we detail model configurations for different settings. The proposed framework is used to quantify forest carbon dynamics at an annual county-level across a 14 year period for the contiguous United States (CONUS). Also, using an analysis of simulated data, we compare the proposed framework with traditional NFI estimators and offer computationally efficient algorithms, software, and data to reproduce results for benchmarking.
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