What are the advantages and disadvantages of trimming or winsorizing outliers in regression?

Z scores have been exceptionally effective with the Random Forest Classification algorithms I’ve developed. Personally, I think 3 has been too conservative, and a Z-score of 2.5 has served as a better threshold for outliers. Remember, not everything even follows a normal distribution.

Standard deviation is primarily the fluctuation of values away from the average. Z-score helps determine in tiers how far in either direction whether in the positive or the negative, the value is off from the Standard Deviation Value. If the Z-score: +-3, it's touching the tiniest and the largest values. For instance, by using the box & Whiskers' plot (box-plot). We can easily visualize Zscore 3 values. These may fall under the 20th, and over the 80th percentile. Outliers have to be determined and tuned based on the situation, and changes it brings into the analysis by its modification and imputation. Sanity checks determine if removing the outlier has served its purpose, and Models are predicting acceptable & practical values.

Balance, Analyze, Decide! ???? I'd note that trimming or winsorizing outliers in regression can significantly reduce the impact of extreme values. The advantage is a more robust model that is less skewed by anomalies, potentially improving prediction accuracy. However, the disadvantage is potential loss of valuable information, especially if outliers are meaningful. It also risks altering the data's original structure. Alternatives include using robust regression techniques or transforming variables to reduce the impact of outliers.

Trimming or winsoring outliers can help improve the robustness and interpretability of regression analysis, it's essential to weigh the advantages against the potential disadvantages - Advantages: 1. Reduces the impact of outliers. 2. Improves model fit 3. Increases robustness 4. Simplifies interpretation. Disadvantages: 1. Loss of information 2. Arbitrary cutoffs 3. Potential bias 4. Violation of assumptions 5. Complexity It is generally recommended to also consider if alternative approaches might be more appropriate for addressing outliers in your specific dataset and research question.

The sequence in which alternative methods for handling outliers are used depends on the specific characteristics of the data and the goals of the analysis, however the below may help for the starters : Data stratification: Identify outlier clusters within data subsets. Residual analysis: Assess outliers' impact through residual examination. Transforming variables: Use functions like logarithms or Box-Cox transformations to mitigate outliers. Non-parametric methods: Employ techniques like kernel regression for robustness. Robust regression: Use methods less sensitive to outliers, such as M-estimation. Robust standard errors: Calculate reliable estimates despite outliers. Get in touch if you would like to discuss any of these methods :).