The principal problem with principal components regression
نویسندگان
چکیده
منابع مشابه
Nonparametric 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 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 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 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 Versus Principal Axis Factoring
Note that SPSS does not provide statistical significance tests for any of the estimated parameters (such as loadings), nor does it provide confidence intervals. Judgments about the adequacy of a oneor two-component model are not made based on statistical significance tests, but by making arbitrary judgments whether the model that is limited to just one or two components does an adequate job of ...
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: Cogent Mathematics & Statistics
سال: 2019
ISSN: 2574-2558
DOI: 10.1080/25742558.2019.1622190