Parameter Cascading Method for Functional Data Analysis: Adaptive Penalized Smoothing, Estimating Semiparametric Additive Models and Inferring Differential Equation Models. This item is unavailable.Parameter Cascading Method for Functional Data Analysis: Adaptive Penalized Smoothing, Estimating Semiparametric Additive Models and Inferring Differential Equation Models

Many statistical models involve three distinct groups of variables: local or nuisance parameters, global or structural parameters, and complexity parameters. In this book, we introduce the parameter cascading method to estimate these statistical models, which treats one group of parameters as an explicit or implicit function of other parameters. The dimensionality of the parameter space is reduced, and the optimization surface becomes smoother. The Newton-Raphson algorithm is applied to estimate these three distinct groups of parameters in three levels of optimization, with the gradients and Hessian matrices written out analytically by the Implicit Function Theorem if necessary and allowing for different criteria for each level of optimization. Moreover, variances of global parameters are estimated by the Delta method and include the variation coming from complexity parameters. We also propose three applications of the parameter cascading method in functional data analysis, include adaptive penalized smoothing, estimating the generalized semiparametric additive models and inferring parameters in differential equations.

Dr. Jiguo Cao is the Canada Research Chair in Data Science and associate professor at the Department of Statistics and Actuarial Science, Simon Fraser University. His research interests include developing novel statistical methodologies and applications in functional data analysis and estimating parameters in differential equations from real data.