Linear_SVM_Census_26048_results.txt

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

[1] "Estimation Method used: MLE"


 Negative Log-Likelihood Value:  655.7703 


 Estimated parameters:
     scale      shape 
47.2460828  0.1508209 

 Standard Error Estimates:
     scale      shape 
5.57473437 0.08034506 

 Estimated parameter covariance matrix.
           scale        shape
scale 31.0776632 -0.242436156
shape -0.2424362  0.006455328

 AIC = 1315.541 

 BIC = 1321.291 
[1] "the length of array 0 is 344"
[1] "threshold_0 is :  1010.163"
[1] "num. items over threshold t :  131"
[1] "computation time 2.958251953125"
[1] "The mean and std=: 681.531976744186" "The mean and std=: 475.230995388019"
[1] "The error for mean and std: 25.6227380724603" "The error for mean and std: 18.1180118436034"
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     1239.496 1388.373     1537.250
2.73972602739726-1 unit = 365 sample return level     1257.641 1464.573     1671.506
5.47945205479452-1 unit = 365 sample return level     1268.478 1549.171     1829.865
13.6986301369863-1 unit = 365 sample return level     1268.479 1675.478     2082.477
27.3972602739726-1 unit = 365 sample return level     1254.850 1783.318     2311.786
54.7945205479452-1 unit = 365 sample return level     1226.631 1903.042     2579.453
136.986301369863-1 unit = 365 sample return level     1161.708 2081.792     3001.876

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     1239.496 1388.373     1537.250
2.73972602739726-1 unit = 365 sample return level     1257.641 1464.573     1671.506
5.47945205479452-1 unit = 365 sample return level     1268.478 1549.171     1829.865
13.6986301369863-1 unit = 365 sample return level     1268.479 1675.478     2082.477
27.3972602739726-1 unit = 365 sample return level     1254.850 1783.318     2311.786
54.7945205479452-1 unit = 365 sample return level     1226.631 1903.042     2579.453
136.986301369863-1 unit = 365 sample return level     1161.708 2081.792     3001.876

[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] 244
> print(max_index)
[1] 152
> print(max_observed)
[1] 1553
> 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] 1780
[1] 1000
[1] 1780
[1] 2000
[1] 1816
[1] 3000
[1] 1816
[1] 4000
[1] 1906
[1] 5000
[1] 1966
[1] 10000
[1] 2147