Research Article

Some New Robust Estimators for Circular Logistic Regression Model with Applications on Meteorological and Ecological Data

Table 2

Bias and of all methods for data with various percentages of misclassified errors (5%, 10%, and 20%).

% of misc errorMethods
BiasBiasBias

151.45702.19181.55702.20181.65812.3018
0.14000.30730.25100.41620.13670.3612
0.51480.44820.46480.34720.46360.3371
0.13440.37650.12330.27560.11440.2565
0.17450.38420.08650.39450.08410.2951
21.55702.29181.65702.30181.75812.4018
0.24000.40730.35100.51620.23670.4612
0.61480.54820.56480.44720.56360.4371
0.23440.47650.22330.37560.21440.3565
0.27450.48420.18650.49450.18410.3951
61.05702.18181.05702.20181.05812.3018
0.14000.31730.25100.62620.12670.5512
0.50480.42820.47480.33720.45360.2271
0.02440.32650.01330.26560.01440.2465
0.06450.34420.07650.38450.07410.2851
101.55802.20181.66802.31281.76912.4128
0.03100.21830.14000.30520.02570.2502
0.40380.33720.35380.23620.35260.2261
0.02340.26550.01230.16460.00340.1455
0.06450.27320.07550.28450.07310.1841
151.56812.20291.66812.31291.76922.4129
0.13110.31620.24010.40510.12560.3501
0.50370.43710.45370.33610.45250.3260
0.12330.36540.11220.26450.10330.2454
0.16340.37310.07540.38340.07300.2840
1102.46486.10132.35376.00022.25266.1113
0.34840.20980.23930.10870.12920.0076
0.89331.45980.99220.94970.98110.9386
0.25420.17160.14310.06050.03200.0504
0.05650.11200.04540.01100.03430.0121
22.36486.00132.25376.01022.15266.0113
0.24840.10980.13930.00870.02920.0066
0.69330.75980.89220.84970.88110.8386
0.15420.07160.04310.05050.02200.0404
0.04650.01200.03540.00100.02430.0021
62.35486.00132.24376.01122.14266.0013
0.23840.11980.12930.01870.01920.0176
0.9833.64980.88220.83970.87110.8286
0.14420.06160.03310.15050.12200.1404
0.14650.00200.13540.10100.12430.1021
102.57586.21232.46476.11122.14166.2223
0.23740.11880.12830.00770.01820.0166
0.88230.54880.88120.83870.87010.8276
0.14320.06060.03210.15150.12100.1414
0.04550.00300.03440.00000.02330.0011
152.57596.21242.46486.11132.36376.2224
0.23730.10870.12820.00760.01810.0065
0.68220.74870.88110.83860.87000.8275
0.14310.06050.03210.05040.02100.0403
0.04540.00100.03430.00000.02320.0010
1202.72887.47732.61777.36622.50667.2551
0.43090.64670.32080.53560.21070.4245
1.66031.80311.55022.70201.44012.6010
0.76881.32570.65771.21460.54661.1035
0.07030.32170.06020.21060.05010.1005
23.72888.47733.61778.36623.50668.2551
0.53090.74670.42080.63560.31070.5245
2.66033.80312.55023.70202.44013.6010
0.86882.32570.75772.21460.64662.1035
0.17030.42170.16020.31060.15010.2005
62.83887.58732.72777.47622.61667.3651
0.52090.75670.43080.64560.32070.5345
1.77032.91311.66022.81201.55012.7110
0.87881.43570.76771.32460.65661.2135
0.18030.43170.17020.21060.16010.2105
103.93888.68733.82778.57623.51668.4651
0.41090.64670.32080.53560.21070.4245
0.67031.81310.56021.71200.45011.6110
0.76880.53570.65770.22460.54660.1135
0.07030.32170.06020.10060.05010.1005
154.04889.79734.93779.68624.62669.5751
0.40080.63560.31070.52450.20060.4134
0.66021.80200.55011.70100.44001.6000
0.75770.52460.64660.21350.53550.1024
0.06020.31060.05010.10050.04000.1004