This paper proposes a new factor rotation for the context of functional principal components analysis. This rotation seeks to re-express a functional subspace in terms of directions of decreasing smoothness as represented by a generalized smoothing metric. The rotation can be implemented simply and we show on two examples that this rotation can improve the interpretability of the leading components
Dimension reduction techniques are at the core of the statistical analysis of high-dimensional and f...
Dimension reduction techniques are at the core of the statistical analysis of high-dimensional and f...
In Functional Data Analysis (FDA), the underlying structure of a raw observation is functional and d...
This paper proposes a new factor rotation for the context of functional principal components analysi...
Dimension reduction techniques play a key role in analysing functional data that possess temporal or...
Dimension reduction techniques play a very important role in analyzing a set of functional data that...
Analyzing functional data often leads to finding common factors, for which functional principal comp...
In the finite-dimensional setting, Li and Chen (1985) proposed a method for principal components ana...
We address the problem of dimension reduction for time series of functional data (Xt:t∈Z). Such func...
While multivariate data analysis is concerned with data in the form of random vectors, functional da...
In functional principal component analysis (PCA), we treat the data that consist of functions not of...
Functional data analyses typically proceed by smoothing, followed by functional PCA. This paradigm i...
This master thesis discusses selected topics of Functional Data Analysis (FDA). FDA deals with the r...
<p>Extraction Method: Principal Component Analysis.</p><p>Rotation Method: Oblimin with Kaiser Norma...
Gradient projection rotation (GPR) is an openly available and promising tool for factor and componen...
Dimension reduction techniques are at the core of the statistical analysis of high-dimensional and f...
Dimension reduction techniques are at the core of the statistical analysis of high-dimensional and f...
In Functional Data Analysis (FDA), the underlying structure of a raw observation is functional and d...
This paper proposes a new factor rotation for the context of functional principal components analysi...
Dimension reduction techniques play a key role in analysing functional data that possess temporal or...
Dimension reduction techniques play a very important role in analyzing a set of functional data that...
Analyzing functional data often leads to finding common factors, for which functional principal comp...
In the finite-dimensional setting, Li and Chen (1985) proposed a method for principal components ana...
We address the problem of dimension reduction for time series of functional data (Xt:t∈Z). Such func...
While multivariate data analysis is concerned with data in the form of random vectors, functional da...
In functional principal component analysis (PCA), we treat the data that consist of functions not of...
Functional data analyses typically proceed by smoothing, followed by functional PCA. This paradigm i...
This master thesis discusses selected topics of Functional Data Analysis (FDA). FDA deals with the r...
<p>Extraction Method: Principal Component Analysis.</p><p>Rotation Method: Oblimin with Kaiser Norma...
Gradient projection rotation (GPR) is an openly available and promising tool for factor and componen...
Dimension reduction techniques are at the core of the statistical analysis of high-dimensional and f...
Dimension reduction techniques are at the core of the statistical analysis of high-dimensional and f...
In Functional Data Analysis (FDA), the underlying structure of a raw observation is functional and d...
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