![]() ![]() It is also used in the construction of some stock indexes when looking at the range of certain factors (for example growth and value) for particular stocks. Winsorization is a used in the survey methodology context in order to "trim" extreme survey non-response weights. ![]() Winsor strives to consciously create an environment of respect and inclusion and to support ethnic, racial, religious and socio-economic diversity among all the constituencies of the school. More formally, they are distinct because the order statistics are not independent. Winsor prepares young women to pursue their aspirations and contribute to the world. In this case, the winsorized mean can equivalently be expressed as a weighted average of the truncated mean and the 5th and 95th percentiles (for the 10% winsorized mean, 0.05 times the 5th percentile, 0.9 times the 10% trimmed mean, and 0.05 times the 95th percentile) though in general winsorized statistics need not be expressible in terms of the corresponding trimmed statistic. Winsorized estimators are usually more robust to outliers than their more standard forms, although there are alternatives, such as trimming, that will achieve a similar effect. A typical strategy is to set all outliers to a specified percentile of the data for example, a 90% winsorization would see all data below the 5th percentile set to the 5th percentile, and data above the 95th percentile set to the 95th percentile. The distribution of many statistics can be heavily influenced by outliers. The effect is the same as clipping in signal processing. It is named after the engineer-turned-biostatistician Charles P. Winsorizing or winsorization is the transformation of statistics by limiting extreme values in the statistical data to reduce the effect of possibly spurious outliers. ![]()
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