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P3LS: Point Process Partial Least Squares
Jamshid Namdari1, Robert T Krafty1, Amita Manatunga1
1Department of Biostatistics & Bioinformatics, Emory University, Atlanta, GA 30322, United States.
Biometrics
|July 30, 2026
Summary
This study introduces point process Partial Least Squares (P3LS) for analyzing complex point process data. The novel P3LS method accurately predicts kidney obstruction from renograms, aiding clinical decisions.
Area of Science:
- Statistics
- Biostatistics
- Machine Learning
Background:
- Point processes are common in scientific data.
- Partial Least Squares (PLS) is widely used for prediction and classification.
- Existing PLS methods lack the ability to analyze point process data.
Purpose of the Study:
- Introduce point process Partial Least Squares (P3LS).
- Analyze latent time-varying intensity functions from inhomogeneous point processes.
- Develop and validate a novel P3LS estimation procedure.
Main Methods:
- Utilized log-Gaussian Cox processes for estimation.
- Conducted simulation studies to examine empirical properties.
- Established predictive consistency for P3LS.
Main Results:
- Developed a novel P3LS estimation procedure.
- Demonstrated predictive consistency of P3LS.
- Successfully applied P3LS to predict kidney obstruction from renal imaging data.
Conclusions:
- P3LS offers a new approach for analyzing point process data.
- The method provides a clinically meaningful and interpretable model.
- P3LS has potential for data-driven clinical decision-making in renal studies.
Keywords:
adaptive dimension reductionfunctional linear modellog Gaussian Cox processpartial least squarespoint processMore Related Videos
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