Robust functional regression based on principal components
نویسندگان
چکیده
منابع مشابه
Robust Principal Component Functional Logistic Regression
In this paper, we discuss the estimation of the parameter function for a functional logistic regression model in the presence of outliers. We consider ways that allow for the parameter estimator to be resistant to outliers, in addition to minimizing multicollinearity and reducing the high dimensionality which is inherent with functional data. To achieve this, the functional covariates and funct...
متن کاملOn convergence of sample and population Hilbertian functional principal components
In this article we consider the sequences of sample and population covariance operators for a sequence of arrays of Hilbertian random elements. Then under the assumptions that sequences of the covariance operators norm are uniformly bounded and the sequences of the principal component scores are uniformly sumable, we prove that the convergence of the sequences of covariance operators would impl...
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 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 کاملBayesian regression based on principal components for high-dimensional data
Motivated by a climate prediction problem, we consider high dimensional Bayesian regression where the number of covariates is much larger than the number of observations. To reduce the dimension of the covariate, the response is regressed on the principal components obtained from the covariates, and it is argued that the PCA regression is equivalent to the original model in terms of prediction....
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: Journal of Multivariate Analysis
سال: 2019
ISSN: 0047-259X
DOI: 10.1016/j.jmva.2019.04.003