Quality quandaries: Predicting a population of curves

KEY POINT

A random effects spline regression model based on splines provides an integrated approach for analyzing functional data, i.e., curves, when the shape of the curves is not parametrically specified. An analysis using this model is presented that makes inferences about a population of curves as well as features of the curves.

About the authors

M. Fugate is a Scientist at Los Alamos National Laboratory and holds a Ph.D. in Mathematics, with an emphasis in Statistics, from the University of New Mexico.

M. S. Hamada is a Scientist at Los Alamos National Laboratory and holds a Ph.D. in Statistics from the University of Wisconsin-Madison. He is a Fellow of the American Statistical Association and of the American Society for Quality.

B. P. Weaver is a Scientist at Los Alamos National Laboratory and holds a Ph.D. in Statistics from Iowa State University.

Acknowledgments

We thank C.C. Essix for her support and encouragement of this work.

Additional information

Funding

Maternal and Child Health Bureau [H34MC19347, H34MC26199].