COVID-19 Data Imputation by Multiple Function-on-Function Principal Component Regression
نویسندگان
چکیده
The aim of this paper is the imputation missing data COVID-19 hospitalized and intensive care curves in several Spanish regions. Taking into account that cases, deceases recovered people are completely observed, a function-on-function regression model proposed to estimate values functional responses associated with curves. estimation coefficient terms principal components’ observed provides prediction equation for unobserved response. An application from first wave Spain developed after properly homogenizing, registering smoothing common interval so become comparable. Finally, Canonical Correlation Analysis performed on components interpret relationship between hospital occupancy rate illness response variables.
منابع مشابه
Generalized multilevel function-on-scalar regression and principal component analysis.
This manuscript considers regression models for generalized, multilevel functional responses: functions are generalized in that they follow an exponential family distribution and multilevel in that they are clustered within groups or subjects. This data structure is increasingly common across scientific domains and is exemplified by our motivating example, in which binary curves indicating phys...
متن کاملcompactifications and function spaces on weighted semigruops
chapter one is devoted to a moderate discussion on preliminaries, according to our requirements. chapter two which is based on our work in (24) is devoted introducting weighted semigroups (s, w), and studying some famous function spaces on them, especially the relations between go (s, w) and other function speces are invesigated. in fact this chapter is a complement to (32). one of the main fea...
15 صفحه اولAutoencoder, Principal Component Analysis and Support Vector Regression for Data Imputation
Data collection often results in records that have missing values or variables. This investigation compares 3 different data imputation models and identifies their merits by using accuracy measures. Autoencoder Neural Networks, Principal components and Support Vector regression are used for prediction and combined with a genetic algorithm to then impute missing variables. The use of PCA improve...
متن کاملSemiparametric principal component poisson regression on clustered data
In modelling count data with multivariate predictors, we often encounter problems with clustering of observations and interdependency of predictors. We propose to use principal components of predictors to mitigate the multicollinearity problem and to abate information losses due to dimension reduction, a semiparametric link between the count dependent variable and the principal components is po...
متن کاملRobust Principal Component Regression
In this note we introduce a method for robust principal component regression. Robust principal components are computed from the predictor variables, and they are used afterwards for estimating a response variable by performing robust linear multiple regression. The performance of the method is evaluated at a test data set from geochemistry. Then it is used for the prediction of censored values ...
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: Mathematics
سال: 2021
ISSN: ['2227-7390']
DOI: https://doi.org/10.3390/math9111237