Linear Processes in Function Spaces: Theory And Applications

By D. Bosq. 2000

894 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f13c0076875d65e382dda47d4ea24256a176bfde

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

The inclusion of exogenous variables in functional autoregressive ozone forecasting (2002, methodology): “The technique -as exposed in Bosq (2000)- proceeds as follows.”
Regression imputation in the functional linear model with missing values in the response (2019, background): “The estimation and forecasting theories of linear processes in function spaces are developed in [1].”
Locally Stationary Functional Time Series (2016, background): “The more general case p “ 8 also includes linear models introduced by Bosq (2000) and Hörmann and Kokoszka (2010) as well as the important class of time-varying functional autoregressive processes, which we discuss in detail in the next section.”
Prediction of functional ARMA processes with an application to traffic data (2016, methodology): “For details and more background we refer to the monographs Bosq [3], Horv`ath and Kokoszka [8] and Hsing and Eubank [9].”
Adaptive functional linear regression (2011, background): “ [35] for applications in economics. The main class of estimation procedures of the slope function studied in the statistical literature is based on principal components regression; see, for example, [2, 6, 9, 17] or [31] in the context of generalized linear models. The second important class of est”
On the optimal reconstruction of partially observed functional data (2017, methodology): “LIEBL Bosq, D. (2000). Linear Processes in Function Spaces: Theory and Applications 149.”
Functional linear regression with functional response (2017, background): “7 in Bosq (2000) and is reproduced here for clarity.”
Testing stationarity of functional time series (2014, methodology): “The main model employed for such series has been the functional autoregressive model of order one, which has received a great deal of attention, see Bosq (2000), Antoniadis and Sapatinas (2003), Antoniadis et al. (2006) and Kargin and Onatski (2008), among many others.”
Generalized Gaussian Process Regression Model for Non-Gaussian Functional Data (2014, methodology): “When the dimension of covariates is small, nonparametric approaches can be applied to estimate the covariance structure; see, for example, Bosq (2000); Yao, Müller, and Wang (2005a, 2005b); and Hall, Müller, and Yao (2008).”
Conditional Quantiles for Dependent Functional Data with Application to the Climatic El Niño Phenomenon (2005, methodology): “Many multivariate statistical techniques, mainly parametric in the functional model terminology, have been extended to functional data and good overviews on this topic can be found in Ramsay and Silverman (1997 and 2002) or Bosq (2000).”
On the Kernel Rule for Function Classification (2006, background): “Key references on this topic are Rice and Silverman [26], Kneip and Gasser [16], Kulkarni and Posner [18], Ramsay and Silverman [25], Bosq [5], Ferraty and Vieu [12], [13], [14], Diabo-Niang and Rhomari [11], Hall, Poskitt and Pres-nell [15], Abraham, Cornillon, Matzner-Løber and Molinari [1], Anton”
Likelihood Ratio Tests for Dependent Data with Applications to Longitudinal and Functional Data Analysis (2014, background): “Mercer’s lemma (see for example Section 1.2 of Bosq, 2000) implies a spectral decomposition of the function .”
COVARIATE ADJUSTED FUNCTIONAL PRINCIPAL COMPONENTS ANALYSIS FOR LONGITUDINAL DATA (2010, background): “…a dense grid of time points [see, e.g., Rao (1958), Dauxois, Pousse and Romain (1982), Besse and Ramsay (1986), Castro, Lawton and Sylvestre (1986), Rice and Silverman (1991), Boente and Fraiman (2000), Bosq (2000), Cardot (2000, 2006), Mas and Menneteau (2003) and Hall and Hosseini-Nasab (2006)].”
CLT in functional linear regression models (2005, methodology): “…regression can be viewed as an ill-conditioned inverse problem since the estimation procedure relies on the inversion of the covariance operator which is compact (Cardot et al. 1999, Bosq 2000, He et al. 2003) and it is well known that poor rates of convergence appear for this kind of problems.”