FuSSO: Functional Shrinkage and Selection Operator
We present the FuSSO, a functional analogue to the LASSO, that efficiently finds a sparse set of functional input covariates to regress a real-valued response against. The FuSSO does so in a semi-parametric fashion, making no parametric assumptions about the nature of input functional covariates and assuming a linear form to the mapping of functional covariates to the response. We provide a statistical backing for use of the FuSSO via proof of asymptotic sparsistency under various conditions. Furthermore, we observe good results on both synthetic and real-world data.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Prediction of Chronic Kidney Disease - A Machine Learning Perspective
Chronic Kidney Disease is one of the most critical illness nowadays and proper diagnosis is required as soon as possible. Machine learning technique has become reliable for medical treatment. With the help of a machine…
Disease Predictionfeature selectionPredictionregressionCovariance Function Estimation for High-Dimensional Functional Time Series with Dual Factor Structures
We propose a flexible dual functional factor model for modelling high-dimensional functional time series. In this model, a high-dimensional fully functional factor parametrisation is imposed on the observed functional pr…
Time SeriesA Survey of Numerical Algorithms that can Solve the Lasso Problems
In statistics, the least absolute shrinkage and selection operator (Lasso) is a regression method that performs both variable selection and regularization. There is a lot of literature available, discussing the statistic…
regressionVariable SelectionIdentification of feasible pathway information for c-di-GMP binding proteins in cellulose production
In this paper, we utilize a machine learning approach to identify the significant pathways for c-di-GMP signaling proteins. The dataset involves gene counts from 12 pathways and 5 essential c-di-GMP binding domains for 1…
feature selectionForecasting Large Realized Covariance Matrices: The Benefits of Factor Models and Shrinkage
We propose a model to forecast large realized covariance matrices of returns, applying it to the constituents of the S\&P 500 daily. To address the curse of dimensionality, we decompose the return covariance matrix using…