Journal
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
Volume 30, Issue 4, Pages 1081-1094Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/10618600.2021.1873144
Keywords
Conditional simulation; Copula; Enhanced false discovery rate; Hypothesis testing; Small area data; Wavelets
Categories
Funding
- ROC Ministry of Science and Technology [MOST 105-2119-M-007-032, MOST 106-2118M-001-002-MY3]
- Australian Research Council [DP150104576, DP190100180, DE180100203]
- NSF funded through the NSF-Census Research Network (NCRN) program [SES-1132031]
- NASA ROSES grant [17-OCO2-17-0012]
- Australian Research Council [DE180100203] Funding Source: Australian Research Council
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The study introduces a new EFDR-CS procedure for incomplete data defined on irregular small areas, using conditional simulation to estimate the signal and combining M p-values using copulas for hypothesis testing. This procedure is demonstrated through a simulation study and applications to real data in the Asia-Pacific region and Middle East, Afghanistan, and Pakistan.
There are a number of ways to test for the absence/presence of a spatial signal in a completely observed fine-resolution image. One of these is a powerful nonparametric procedure called enhanced false discovery rate (EFDR). A drawback of EFDR is that it requires the data to be defined on regular pixels in a rectangular spatial domain. Here, we develop an EFDR procedure for possibly incomplete data defined on irregular small areas. Motivated by statistical learning, we use conditional simulation (CS) to condition on the available data and simulate the full rectangular image at its finest resolution many times (M, say). EFDR is then applied to each of these simulations resulting in M estimates of the signal and M statistically dependent p-values. Averaging over these estimates yields a single, combined estimate of a possible signal, but inference is needed to determine whether there really is a signal present. We test the original null hypothesis of no signal by combining the M p-values into a single p-value using copulas and a composite likelihood. If the null hypothesis of no signal is rejected, we use the combined estimate. We call this new procedure EFDR-CS and, to demonstrate its effectiveness, we show results from a simulation study; an experiment where we introduce aggregation and incompleteness into temperature-change data in the Asia-Pacific; and an application to total-column carbon dioxide from satellite remote sensing data over a region of the Middle East, Afghanistan, and the western part of Pakistan. for this article are available online.
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