Robust Regression and Outlier Detection
Устойчивые регрессии и обнаружение выбросов
1989-05-01
SCID: 54.1/sjbvrj8s
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Multiple regressionOutlier detectionOutlier diagnosticsRobust regressionSimple regression
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Abstract (AI)
1. Introduction. 2. Simple Regression. 3. Multiple Regression. 4. The Special Case of One-Dimensional Location. 5. Algorithms. 6. Outlier Diagnostics. 7. Related Statistical Techniques. References. Table of Data Sets. Index.
Key Findings
1
A special case analysis is presented for one-dimensional location problems, suggesting tailored robust solutions for that scenario.
2
Algorithms for implementing the robust regression and outlier detection methods are provided, implying practical computational procedures.
3
Connections to related statistical techniques are explored, situating the proposed methods within broader statistical methodology.
4
Outlier diagnostics are discussed, offering tools to identify and assess influential or anomalous observations.
5
The paper addresses robust regression methods and techniques for detecting outliers in regression analysis.
6
The work covers both simple and multiple regression settings, indicating methods applicable to one-dimensional and multivariate cases.
Research Object
Regression models and data sets used for regression analysis
Research Subject
Robust estimation methods and outlier detection diagnostics for simple and multiple regression (including one-dimensional location), their algorithms and related statistical techniques
Publication Details
Publication Date
1989-05-01
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