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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