Linear_SVM_Census_13024_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:  219.9973 


 Estimated parameters:
       scale        shape 
1.732209e+01 5.821479e-08 

 Standard Error Estimates:
    scale     shape 
3.1331312 0.1021802 

 Estimated parameter covariance matrix.
           scale       shape
scale  9.8165109 -0.19733117
shape -0.1973312  0.01044078

 AIC = 443.9946 

 BIC = 448.1155 
[1] "the length of array 0 is 361"
[1] "threshold_0 is :  520.440000000001"
[1] "num. items over threshold t :  58"
[1] "computation time 2.202880859375"
[1] "The mean and std=: 323.747922437673" "The mean and std=: 235.038430646406"
[1] "The error for mean and std: 12.3704437182319" "The error for mean and std: 8.7472246394483" 
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     569.1250 596.4295     623.7341
2.73972602739726-1 unit = 365 sample return level     572.8210 608.4363     644.0517
5.47945205479452-1 unit = 365 sample return level     574.7295 620.4431     666.1566
13.6986301369863-1 unit = 365 sample return level     574.7347 636.3151     697.8956
27.3972602739726-1 unit = 365 sample return level     572.6272 648.3219     724.0166
54.7945205479452-1 unit = 365 sample return level     568.9442 660.3287     751.7131
136.986301369863-1 unit = 365 sample return level     561.4110 676.2007     790.9905

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     569.1250 596.4295     623.7341
2.73972602739726-1 unit = 365 sample return level     572.8210 608.4363     644.0517
5.47945205479452-1 unit = 365 sample return level     574.7295 620.4431     666.1566
13.6986301369863-1 unit = 365 sample return level     574.7347 636.3151     697.8956
27.3972602739726-1 unit = 365 sample return level     572.6272 648.3219     724.0166
54.7945205479452-1 unit = 365 sample return level     568.9442 660.3287     751.7131
136.986301369863-1 unit = 365 sample return level     561.4110 676.2007     790.9905

[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] 261
> print(max_index)
[1] 169
> print(max_observed)
[1] 555
> 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] 630
[1] 1000
[1] 630
[1] 2000
[1] 712
[1] 3000
[1] 778
[1] 4000
[1] 778
[1] 5000
[1] 778
[1] 10000
[1] 844