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Table 2 Likelihood-based parameter estimates for the best-fitting models.

From: Modeling the probability distribution of positional errors incurred by residential address geocoding

Error dataset

Component

Proportion

μ X

μ Y

σ X

σ Y

ρ

v

(a)

1

0.571

-12.1

-10.7

61.6

54.1

-0.05

1.6

 

2

0.253

-4.7

-350.0

75.9

550.0

0.18

6.5

 

3

0.176

352.8

-12.6

540.3

84.9

-0.03

16.7

(b)

1

0.560

-0.8

-14.2

39.4

75.9

0.06

1.8

 

2

0.440

372.1

-6.7

523.6

90.3

-0.10

5.9

(c)

1

0.519

4.9

-5.4

62.3

60.8

-0.10

1.8

 

2

0.292

13.6

-35.0

289.1

54.9

-0.14

2.4

 

3

0.189

14.9

-10.2

62.1

354.4

0.14

2.4

(d)

1

0.700

5.9

-4.3

47.0

100.7

0.06

1.8

 

2

0.300

29.3

-6.2

62.1

419.5

0.16

3.0

  1. Models and the datasets to which they were fitted are: (a) the three-component t mixture model for the automated geocoding positional errors; (b) the two-component t mixture model for the automated geocoding positional errors aligned with axial direction of corresponding street segment; (c) the three-component t mixture model for the E911 positional errors; (d) the two-component t mixture model for the E911 positional errors aligned with axial direction of corresponding street segment. Means are denoted by μ X and μ Y , standard deviations by σ X and σ Y , correlation coefficient by ρ, and degrees of freedom by v. Units of measurement for means and standard deviations are meters.