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Activity Number:
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62
- High-Dimensional Regression Methods
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Type:
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Contributed
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Date/Time:
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Sunday, August 7, 2022 : 4:00 PM to 5:50 PM
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Sponsor:
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Biometrics Section
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Abstract #322757
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Title:
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Monitoring Sequential Structural Changes in Penalized High-Dimensional Linear Models
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Author(s):
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Suthakaran Ratnasingam* and Wei Ning
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Companies:
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California State University, San Bernardino and Bowling, Green State University
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Keywords:
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Sequential changes;
Linear regression model;
High dimension;
Change-point analysis;
Penalized regression
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Abstract:
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We present a procedure to monitor the structural changes in the penalized regression model for high-dimensional data sequentially. Our approach utilizes a given historical data set to perform both variable selection and estimation simultaneously. The asymptotic properties of the test statistics are established under the null and alternative hypotheses. The finite sample behavior of the monitoring procedure is investigated with simulation studies. The proposed method is applied to a real data set to illustrate the detection procedure.
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Authors who are presenting talks have a * after their name.
