Kniha Parameter Cascading Method for Functional Data Analysis Jiguo Cao

Parameter Cascading Method for Functional Data Analysis

Adaptive Penalized Smoothing, Estimating Semiparametric Additive Models and Inferring Differential Equation Models

Autor: Jiguo Cao
Jazyk: Angličtina
Väzba: Brožovaná
Dostupnosť: Skladom u dodávateľa
Odosielame za 8-11 dní
60.43
Many statistical models involve three distinct groups of variables: local or nuisance parameters, gl...

Informácie o knihe

Autor
Jazyk
Angličtina
Väzba
Kniha - Brožovaná
Vydalo
2017
Stránok
240
EAN
9783330072381
Enbook ID
16307770
Hmotnosť
375
Rozmery
150 x 220 x 14

Kompletný popis

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.

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