Finally, since the QP used by H.264 is designed to change the quantization step size and the relationship between the QPs is well defined, this relationship can be used to adjust the thresholds of the statistics (residual, variance of means, means of variance, etc).
To obtain the threshold of the detection statistics and the detection probability, 103 Monte Carlo trials are conducted for 100 different target impulse response realizations.
Our method selects features by searching for the desired thresholds of a pair of statistics that are used to filter features.
Because the absolute value of the peak intensity varies under various conditions, it is not appropriate to define the general absolute value as the threshold value; thus, the use of statistics (e.g., means, standard deviations) is useful.
First, a 5% empirical threshold was computed from the distribution of statistics across all non-QTL positions (those that did not fall in the intervals mentioned above).
The probability is 100% for the performance test on the order of the background model, 50% for the tests of statistics across multiple thresholds and finally between 10 90% in the dilution test.
At K = 2, the 133 Taccessionssions were classified into three sublineage groups: TauL1a (50 accessions), TauL1b (74 accessions), and the intermediates (named TauL1x) (9 accessions), using a threshold of 0.85 for the Q statistics (i.e., the estimated membership coefficients for each individual in each genepool).
Threshold t-statistics of the restricted search was 3.46 for P<0.05 with 46 degrees of freedom with a search volume of 0.984 cm3 at an FWHM of 12 mm.
The threshold may be decided based on both the stringency of statistics significance and how muchbiological information is needed.
By varying the thresholds of these two statistics in certain steps within their acceptable ranges, we can achieve various tradeoffs and control the size of the feature sets.
