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Table 2 Estimating Parameters in a Four-Component Mixture Model

From: Thinking outside the curve, part I: modeling birthweight distribution

Quantity p1 p2 p3 p4
[average of 25 estimates] .007 .182 .758 .052
[standard deviation of 25 estimates] .001 .039 .037 .008
[bias adjustment] .001 .041 .032 .009
Confidence interval (.005, .010) (.092, .272) (.681, .836) (.033, .071)
Quantity μ1 μ2 μ3 μ4
[average of 25 estimates] 832 2772 3170 3804
[standard deviation of 25 estimates] 46 103 7 25
[bias adjustment] 34 80 9 38
Confidence interval (741, 924) (2565, 2979) (3152, 3187) (3735, 3873)
Quantity σ1 σ2 σ3 σ4
[average of 25 estimates] 210 740 417 413
[standard deviation of 25 estimates] 28 23 10 38
[bias adjustment] 30 23 7 46
Confidence interval (146, 274) (688, 792) (398, 436) (321, 506)
  1. Parameters in a 4-component normal mixture model for birthweight distribution are estimated, based on 25 samples of size 50000 from the population of white singletons born to heavily smoking mothers. Interval estimates are constructed using Equations (7) and (8) with C 0 = 2.5 and φ = .2465.