Sentence examples for outlier approach from inspiring English sources

Missing observations in ARIMA models: Skipping approach versus additive outlier approach (joint with V. Gomez and D. Pena), Journal of Econometrics, 88(2), 341 363, 1998.

Three typical outlier algorithms, respectively the statistics-based approach, the distance-based approach, and the density-based local outlier approach, are described with respect to the principle, the characteristics and the time complexity of the algorithms.

Since genetic loci with unusual degrees of differentiation often provide indications of selection [15], we used the outlier approach to detect positive selection.

When this method was applied to the HapMap data, it was able to identify the candidates of population-specific strong selective sweeps more efficiently than the outlier approach that depends on the empirical distribution.

Consequently, while sampling large genomic SNP databases, empirical distributions can be constructed and genes subjected to the local forces, such as selection, as opposed to the genome-wide forces, like demography, which can be identified by the outlier approach [30], including the one used in our study and others (e.g. Voight et al. [19], and Wang et al. [20]).

To identify genomic footprints of artificial selection, we used an outlier approach looking for genetic bottlenecks.

However, recent studies have suggested that such outlier approaches provide a number of false positive loci because of the large size of the human genome [26], [27].

Therefore, recent genome-wide scans for selection have resorted to outlier approaches based on empirical distribution, where a certain threshold is set (i.e., 1st percentile of the empirical distribution) [13], [15], [18], [19].

As described in previous report [ 10], we used two outlier approaches to detect signals of artificial selection.

Note that previous studies using outlier approaches sometimes restricted their analyses to autosomes because the differentiation level of the X-chromosome is higher due to its reduced effective size [e.g. [ 27, 79, 80]].

Finally, it should be noted that by using an FST-outlier approach, the number of loci under selection might be underestimated.