REGRESSION MODELING USING PRINCIPAL COMPONENTS
نویسندگان
چکیده
منابع مشابه
Estimating 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 کاملNonlinear 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 کاملPersian Handwriting Analysis Using Functional Principal Components
Principal components analysis is a well-known statistical method in dealing with large dependent data sets. It is also used in functional data for both purposes of data reduction as well as variation representation. On the other hand "handwriting" is one of the objects, studied in various statistical fields like pattern recognition and shape analysis. Considering time as the argument,...
متن کاملPrincipal Components Regression With Data Chosen Components and Related Methods
Multiple regression with correlated predictor variables is relevant to a broad range of problems in the physical, chemical, and engineering sciences. Chemometricians, in particular, have made heavy use of principal components regression and related procedures for predicting a response variable from a large number of highly correlated predictors. In this paper we develop a general theory that gu...
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: Conference on Applied Statistics in Agriculture
سال: 1992
ISSN: 2475-7772
DOI: 10.4148/2475-7772.1408