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Table 1 Mean Bias (Bias) and root mean squared error (RMSE) for the point estimate and the coverage probability (% CP) and mean length (Length) of the interval estimate in estimating the minimum mortality temperature (MMT) by six different methods (Argmin1, Argmin2, Empirical1, Empirical2strong, Empirical2moderate, and Empirical2minimal) for each of the 4 scenarios; U-shape (Scenario 1), reverse J-shape (Scenario 2), rotated S-shape (Scenario 3) and sector shape (Scenario 4)

From: Monte Carlo simulation-based estimation for the minimum mortality temperature in temperature-mortality association study

   Methods
Argmin 1 Argmin 2 Empirical 1 Empirical 2strong a Empirical 2moderate b Empirical 2minimal c
Scenario 1 (True MMT = 23.889) Bias -0.178 -0.183 -0.203 -0.203 -0.194 -0.152
RMSE 1.046 1.073 0.859 0.836 0.823 0.870
% CP    97.2% 96.4% 96.8% 95.6%
Length    3.385 3.210 3.410 3.387
Scenario 2 (True MMT = 11.274) Bias 2.680 -0.245 4.197 -1.183 -0.772 -0.311
RMSE 8.896 2.243 6.979 1.542 2.279 2.428
% CP    96.4% 98.0% 95.3% 93.8%
Length    10.415 5.631 8.917 10.440
Scenario 3 (True MMT = 29.167) Bias 16.486 0.512 16.683 0.776 0.639 1.240
RMSE 28.011 3.342 21.092 1.126 1.354 2.385
% CP    96.5% 96.4% 95.9% 96.5%
Length    9.758 3.257 5.662 9.054
Scenario 4 (True MMT = −3.333) Bias 4.359 0.099 4.340 -1.069 -2.201 -2.181
RMSE 8.686 3.592 6.259 1.504 2.815 2.835
% CP    95.4% 93.8% 85.9% 84.7%
Length    7.816 6.097 7.719 7.681
  1. aPrior support: 70th -95th percentiles for scenarios 1 & 3, 40th – 65th percentiles for scenario 2, and 1st -10th percentiles for scenario 4
  2. bPrior support: 50th -99th percentiles for scenarios 1 & 3, 30th -80th percentiles for scenario 2, and 1st -50th percentiles for scenario 4
  3. cPrior support: 1st – 99th percentiles for all scenarios