Table 1.
Comparison of Nonparametric and Parametric Regressions, Based on Average Prediction Error (Residual Sums of Squares Averaged Over 1,000 Replications), for a Single QTL, with 100 Sib Pairs[Note]
| Candidate Interval | Error in Predictiona | ||
| NP (97.2%) |
P1 (98.5%) |
P2 (98.9%) |
|
| β = 0, p = .5, ρ = .8: | |||
| (1,2) | 100.56 | 95.46 | 92.71 |
| (2,3) | 87.65 | 74.72 | 70.62 |
| (3,4) | 104.29 | 99.55 | 97.68 |
| (4,5) | 117.03 |
110.84 |
107.27 |
| NP (90.7%) |
P1 (82.5%) |
P2 (84.1%) |
|
| β = 2, p = .9, ρ = .7: | |||
| (1,2) | 152.76 | 157.63 | 155.35 |
| (2,3) | 143.37 | 148.54 | 146.72 |
| (3,4) | 152.90 | 160.81 | 157.64 |
| (4,5) | 166.29 |
173.06 |
171.18 |
| NP (75.8%) |
P1 (43.0%) |
P2 (51.5%) |
|
| β = 4, p = .7, ρ = .5: | |||
| (1,2) | 182.45 | 196.74 | 193.27 |
| (2,3) | 180.34 | 194.68 | 191.93 |
| (3,4) | 185.74 | 194.52 | 193.22 |
| (4,5) | 190.59 | 207.02 | 203.51 |
Note.—Simulation parameter values were α=5, σ2=1, and θ1=θ2=θ3=θ4=θ5=.01.
a
NP = nonparametric regression; P1 = parametric regression with the leave-one-out technique; P2 = parametric regression without the leave-one-out technique (i.e., standard parametric regression). Results in parentheses denote the percentages of correct identification of the true interval location.