Nonlinear Regression Estimation Using Subset-Based Kernel Principal Components
نویسندگان
چکیده
منابع مشابه
Nonlinear Regression Estimation Using Subset-based Kernel Principal Components
We study the estimation of conditional mean regression functions through the so-called subset-based kernel principal component analysis (KPCA). Instead of using one global kernel feature space, we project a target function into different localized kernel feature spaces at different parts of the sample space. Each localized kernel feature space reflects the relationship on a subset between the r...
متن کاملEstimating Invariant Principal Components Using Diagonal Regression
In this work we apply the method of diagonal regression to derive an alternative version of Principal Component Analysis (PCA). “Diagonal regression” was introduced by Ragnar Frisch (the first economics Nobel laureate) in his paper “Correlation and Scatter in Statistical Variables” (1928). The benefits of using diagonal regression in PCA are that it provides components that are scale-invariant ...
متن کاملNonparametric Principal Components Regression
In ordinary least squares regression, dimensionality is a sensitive issue. As the number of independent variables approaches the sample size, the least squares algorithm could easily fail, i.e., estimates are not unique or very unstable, (Draper and Smith, 1981). There are several problems usually encountered in modeling high dimensional data, including the difficulty of visualizing the data, s...
متن کاملIntrinsic Linearization of Nonlinear Regression by Principal Components Method
Most commonly nonlinear regression models have an important parameter-effect nonlinearity but only a small intrinsic nonlinearity. Hence it is of interest to approximate them linearly. This can be done either by retaining the original parametrization θ, or by choosing a new parametrization β = β(θ). Under a prior weight density π(θ) we propose criterion of optimality of intrinsically linear app...
متن کاملNonlinear Robust Regression Using Kernel Principal Component Analysis and R-Estimators
In recent years, many algorithms based on kernel principal component analysis (KPCA) have been proposed including kernel principal component regression (KPCR). KPCR can be viewed as a non-linearization of principal component regression (PCR) which uses the ordinary least squares (OLS) for estimating its regression coefficients. We use PCR to dispose the negative effects of multicollinearity in ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Statistica Sinica
سال: 2018
ISSN: 1017-0405
DOI: 10.5705/ss.202016.0369