Robust Estimator and Resocialization: A Review
Robust estimator and resocialization strategies are two important concepts in the field of statistics. Robust estimator refers to a method of estimating parameters which produces reliable results even when the underlying data are affected by outliers or noise. Resocialization strategies, on the other hand, refer to the various interventions and methods used to address the social problems of individuals or groups. This article reviews the two concepts and discusses their relevance to the field of statistics.
Robust estimator is an estimation procedure that is capable of providing reliable estimates even when the underlying data is affected by outliers or noise. Robust estimators are characterized by having low sensitivity to outliers, a property which allows them to be more reliable in the presence of such extreme values. Robust estimators also have the advantage of being more efficient in terms of computational complexity, since they require fewer computations than other estimation strategies. Examples of robust estimators include the median, Huber estimator, least trimmed squares, and the M-estimator.
Resocialization strategies, on the other hand, are interventions used to address the social problems of individuals or groups. These strategies may include counseling, education, job training, and other interventions that aim to improve the social functioning of individuals or groups. Resocialization strategies are used to address a variety of social problems such as delinquency, substance abuse, and gang activity. The effectiveness of such strategies depends on the nature of the problem and the individual or group being addressed.
Robust estimator and resocialization strategies are important concepts in the field of statistics and have relevance in many different areas. Robust estimators are useful in situations where data may be affected by outliers or noise, while resocialization strategies are important for addressing social problems. Both of these concepts have the potential to improve the accuracy of statistical analyses and the well-being of individuals and groups.
References
Chen, C. (2020). Robust Estimators. In M. A. Hamilton (Ed.), Encyclopedia of Research and Statistical Analysis (pp. 851-855). Thousand Oaks, CA: Sage.
Kendall, D. E., & Schreck, C. J. (Eds.). (2018). Resocialization: Strategies for Intervention and Change. Los Angeles, CA: SAGE.