Approximation of estimators in the PCA of a stochastic process using B-splines

DOI10.1080/03610919608813336zbMath0937.62602OpenAlexW2086248341MaRDI QIDQ4337286

R. Gutiérrez, Mariano J. Valderrama, Ana M. Aguilera

Publication date: 24 August 1997

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610919608813336

zbMATH Keywords

eigenvalues and eigenfunctionscubic B-splinesprincipal componentscovariance operator

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Non-Markovian processes: estimation (62M09) Inference from stochastic processes (62M99)

Related Items (19)

Functional PCA and base-line logit models ⋮ A survey of functional principal component analysis ⋮ Derivation of a state-space model by functional data analysis ⋮ Unnamed Item ⋮ Penalized spline approaches for functional logit regression ⋮ Regression models for functional data by reproducing kernel Hilbert spaces methods ⋮ On the structure of the stochastic processes of mortgages in Spain ⋮ Functional estimation of the random rate of a Cox process ⋮ Penalized PCA approaches for B-spline expansions of smooth functional data ⋮ Explaining functional principal component analysis to actuarial science with an example on vehicle insurance ⋮ Forecasting PC-ARIMA Models for Functional Data ⋮ Modelling the mean of a doubly stochastic Poisson process by functional data analysis ⋮ Stochastic modeling of random access memories reset transitions ⋮ Functional estimation incorporating prior correlation information ⋮ Computational considerations in functional principal component analysis ⋮ Principal component estimation of functional logistic regression: discussion of two different approaches ⋮ Generalized linear models for geometrical current predictors: An application to predict garment fit ⋮ Functional PLS logit regression model ⋮ Forecasting with unequally spaced data by a functional principal component approach

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Asymptotic theory for the principal component analysis of a vector random function: Some applications to statistical inference
• Principal components analysis of sampled functions
• Kalman filtering on approximate state-space models
• Splines in Statistics
• Data analysis for numerical and categorical individual time-series
• Principal Modes of Variation for Processes with Continuous Sample Curves

This page was built for publication: Approximation of estimators in the PCA of a stochastic process using B-splines