Linear_SVM_Census_19536_results.txt

 Negative Log-Likelihood Value:  261.9041 


 Estimated parameters:
        scale         shape 
 4.653923e+01 -3.987256e-09 

 Standard Error Estimates:
     scale      shape 
3.37557866 0.06615379 

 Estimated parameter covariance matrix.
            scale        shape
scale 11.39453126 -0.045482613
shape -0.04548261  0.004376324

 AIC = 527.8082 

 BIC = 531.9291 
[1] "the length of array 0 is 201"
[1] "threshold_0 is :  757.400000000002"
[1] "num. items over threshold t :  58"
[1] "computation time 2.076171875"
[1] "The mean and std=: 480.646766169154" "The mean and std=: 353.429015865579"
[1] "The error for mean and std: 24.9289606858005" "The error for mean and std: 17.6274371488624"
fevd(x = aa, threshold = t, type = type_name, units = "Time", 
    time.units = "days", period.basis = "1 unit = 365 sample")

[1] "Normal Approx."

                                                  95% lower CI  Estimate 95% upper CI
1.36986301369863-1 unit = 365 sample return level     924.1843  988.8131     1053.442
2.73972602739726-1 unit = 365 sample return level     930.5001 1021.0716     1111.643
5.47945205479452-1 unit = 365 sample return level     930.7077 1053.3301     1175.953
13.6986301369863-1 unit = 365 sample return level     928.3255 1095.9736     1263.622
27.3972602739726-1 unit = 365 sample return level     955.2759 1128.2321     1301.188
54.7945205479452-1 unit = 365 sample return level     936.2265 1160.4907     1384.755
136.986301369863-1 unit = 365 sample return level     949.4851 1203.1341     1456.783

fevd(x = aa, threshold = t, type = type_name, units = "Time", 
    time.units = "days", period.basis = "1 unit = 365 sample")

[1] "Normal Approx."

                                                  95% lower CI  Estimate 95% upper CI
1.36986301369863-1 unit = 365 sample return level     924.1843  988.8131     1053.442
2.73972602739726-1 unit = 365 sample return level     930.5001 1021.0716     1111.643
5.47945205479452-1 unit = 365 sample return level     930.7077 1053.3301     1175.953
13.6986301369863-1 unit = 365 sample return level     928.3255 1095.9736     1263.622
27.3972602739726-1 unit = 365 sample return level     955.2759 1128.2321     1301.188
54.7945205479452-1 unit = 365 sample return level     936.2265 1160.4907     1384.755
136.986301369863-1 unit = 365 sample return level     949.4851 1203.1341     1456.783

[1] "--------------------------"
There were 50 or more warnings (use warnings() to see the first 50)
> 
> 
> print("Bayesian Factor Method:")
[1] "Bayesian Factor Method:"
> print(ind)
[1] 101
> print(max_index)
[1] 9
> print(max_observed)
[1] 1117
> print("--------------------------")
[1] "--------------------------"
> print("Actual observed Time:")
[1] "Actual observed Time:"
> # what are actual timing after these steps:
> for(i in c(500, 1000, 2000, 3000, 4000, 5000, 10000))
+ {
+   print(i)
+   print(max(a$V1[1:i], na.rm = TRUE))
+ }
[1] 500
[1] 1117
[1] 1000
[1] 1235
[1] 2000
[1] 1235
[1] 3000
[1] 1235
[1] 4000
[1] 1235
[1] 5000
[1] 1235
[1] 10000
[1] 1235