The historical functional linear model
WebMay 12, 2024 · Function‐on‐function linear regression is an essential tool in characterizing the linear relationship between a functional response and a functional predictor. However, most of the estimation methods for this model are based on the least‐squares procedure, which is sensitive to atypical observations. WebFunctional linear regression, one of the most useful tools of functional data analysis, is an example of GFLM where the response variable is continuous and is often assumed to have a Normal distribution. The variance function is a constant function and …
The historical functional linear model
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WebIn the historical linear model, the slope function is exactly a locally sparse function that is zero beyond the cutoff time. A locally sparse estimate then gives rise to an estimate of the cutoff time. We propose a nested group bridge penalty that is able to specifically shrink the tail of a function. WebOct 28, 2024 · The conventional historical functional linear model relates the current value of the functional response at time t to all past values of the functional covariate up to …
WebSep 13, 2024 · In the historical linear model, the slope function is exactly a locally sparse function that is zero beyond the cutoff time. A locally sparse estimate then gives rise to … WebApr 1, 2011 · An intermediate model considered in this paper between the functional linear model with global support and the varying coefficient model with point-wise support is the …
WebDec 18, 2008 · Abstract The authors develop a functional linear model in which the values at time t of a sample of curves yi (t) are explained in a feed-forward sense by the values of covariate curves xi (s) observed at times s ±.t. They give special attention to the case s ± [t — δ, t], where the lag parameter δ is estimated from the data. WebMay 28, 2024 · The historical functional linear model ( 2) is identifiable but not estimable, with effectively an infinite number of covariates, therefore regularization or roughness …
WebDifferent from the rectangular support of functional linear models, the triangular support of the historical functional linear models and the point-wise support of varying coefficient …
WebThe historical linear model, and in fact any linear model involving integration over functional covariates, is identifiable but essentially inestimable since we have a model with … chung sin houseWebApr 8, 2024 · When the response is a discrete or categorical variable, we further extend our proposed functional L-optimality subsampling (FLoS) method to the functional generalized linear model. We establish the asymptotic properties of the estimators by the FLoS method. chung sing chinese restaurant old bridgeWebSep 13, 2024 · In the historical linear model, the slope function is exactly a locally sparse function that is zero beyond the cutoff time. A locally sparse estimate then gives rise to an estimate of... details of bhupendra singh sonwalWebOct 25, 2024 · The varying-coefficient functional linear model is estimated by the penalized maximum likelihood method with the sparsity-inducing penalty. Tuning parameters that controls the degree of the penalization are determined by a model selection criterion. details of biden\u0027s infrastructure plandetails of basic system configurationWebFunctional linear models are used widely in literature to relate vector- or function-valued predictor and response variables. Muller (2005) summarizes the functional linear models … details of beneficiary meaningWebJun 13, 2002 · The historical functional linear model. N. Malfait, J. Ramsay; Mathematics. 2003; The authors develop a functional linear model in which the values at time t of a sample of curves yi (t) are explained in a feed‐forward sense by the values of covariate curves xi(s) observed at ... chung sing old bridge menu