Abstract
This article presents exploratory data analytic methodology for visualizing and summarizing data that can be represented as individual-specific curves. We propose a simplified form of functional data analysis. A nonparametric scatterplot smooth is applied to each individual’s data, followed by a principal components analysis of the smoothed data. We then display the individual smooth curves in an array organized by principal component scores. The display suggests interpretable summary measures. The methodology is applied to the measurement of proliferative activity, a biomarker for colon cancer risk. We use the summary measures in the analysis of a pilot study clinical trial.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 813-821 |
| Number of pages | 9 |
| Journal | Journal of the American Statistical Association |
| Volume | 90 |
| Issue number | 431 |
| DOIs | |
| State | Published - 1995 |
| Externally published | Yes |
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Keywords
- Descriptive statistics
- LOESS
- Principal components
- Proliferative index
- Scatterplot smoothing
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
Cite this
Modeling the labeling index distribution : An application of functional data analysis. / Grambsch, Patricia M.; Randall, Bryan L.; Bostick, Roberd M.; Potter, John D.; Louis, Thomas.
In: Journal of the American Statistical Association, Vol. 90, No. 431, 1995, p. 813-821.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Modeling the labeling index distribution
T2 - An application of functional data analysis
AU - Grambsch, Patricia M.
AU - Randall, Bryan L.
AU - Bostick, Roberd M.
AU - Potter, John D.
AU - Louis, Thomas
PY - 1995
Y1 - 1995
N2 - This article presents exploratory data analytic methodology for visualizing and summarizing data that can be represented as individual-specific curves. We propose a simplified form of functional data analysis. A nonparametric scatterplot smooth is applied to each individual’s data, followed by a principal components analysis of the smoothed data. We then display the individual smooth curves in an array organized by principal component scores. The display suggests interpretable summary measures. The methodology is applied to the measurement of proliferative activity, a biomarker for colon cancer risk. We use the summary measures in the analysis of a pilot study clinical trial.
AB - This article presents exploratory data analytic methodology for visualizing and summarizing data that can be represented as individual-specific curves. We propose a simplified form of functional data analysis. A nonparametric scatterplot smooth is applied to each individual’s data, followed by a principal components analysis of the smoothed data. We then display the individual smooth curves in an array organized by principal component scores. The display suggests interpretable summary measures. The methodology is applied to the measurement of proliferative activity, a biomarker for colon cancer risk. We use the summary measures in the analysis of a pilot study clinical trial.
KW - Descriptive statistics
KW - LOESS
KW - Principal components
KW - Proliferative index
KW - Scatterplot smoothing
UR - http://www.scopus.com/inward/record.url?scp=0141812050&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0141812050&partnerID=8YFLogxK
U2 - 10.1080/01621459.1995.10476579
DO - 10.1080/01621459.1995.10476579
M3 - Article
AN - SCOPUS:0141812050
VL - 90
SP - 813
EP - 821
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
SN - 0162-1459
IS - 431
ER -