Linear_SVM_Census_32235_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:  545.9463 


 Estimated parameters:
       scale        shape 
9.219799e+01 1.008230e-08 

 Standard Error Estimates:
     scale      shape 
4.29967168 0.07637127 

 Estimated parameter covariance matrix.
          scale        shape
scale 18.487177 -0.102484959
shape -0.102485  0.005832571

 AIC = 1095.893 

 BIC = 1101.083 
[1] "the length of array 0 is 240"
[1] "threshold_0 is :  1245.945"
[1] "num. items over threshold t :  99"
[1] "computation time 2.435302734375"
[1] "The mean and std=: 827.329166666667" "The mean and std=: 603.969180352749"
[1] "The error for mean and std: 38.9860429521457" "The error for mean and std: 27.5672953430922"
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     1560.184 1737.339     1914.495
2.73972602739726-1 unit = 365 sample return level     1548.251 1801.246     2054.241
5.47945205479452-1 unit = 365 sample return level     1552.506 1865.153     2177.800
13.6986301369863-1 unit = 365 sample return level     1560.194 1949.633     2339.072
27.3972602739726-1 unit = 365 sample return level     1563.641 2013.540     2463.439
54.7945205479452-1 unit = 365 sample return level     1524.063 2077.447     2630.830
136.986301369863-1 unit = 365 sample return level     1503.701 2161.927     2820.153

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     1560.184 1737.339     1914.495
2.73972602739726-1 unit = 365 sample return level     1548.251 1801.246     2054.241
5.47945205479452-1 unit = 365 sample return level     1552.506 1865.153     2177.800
13.6986301369863-1 unit = 365 sample return level     1560.194 1949.633     2339.072
27.3972602739726-1 unit = 365 sample return level     1563.641 2013.540     2463.439
54.7945205479452-1 unit = 365 sample return level     1524.063 2077.447     2630.830
136.986301369863-1 unit = 365 sample return level     1503.701 2161.927     2820.153

[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] 140
> print(max_index)
[1] 48
> print(max_observed)
[1] 1670
> 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] 1990
[1] 1000
[1] 2504
[1] 2000
[1] 2504
[1] 3000
[1] 2504
[1] 4000
[1] 2504
[1] 5000
[1] 2504
[1] 10000
[1] 2673