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Spatial Depth-Based Classification for Functional Data
Functional depths Functional outliers Spatial functional depth Supervised func-tional classification
2013/6/14
We enlarge the available number of functional depths by defining two new depth measures for curves. Both depths are based on a spatial approach: the functional spatial depth (FSD), that shows an inter...
Variance estimation and asymptotic confidence bands for the mean estimator of sampled functional data with high entropy unequal probability sampling designs
covariance function finite population Hajek approximation Horvitz-Thompso estimator Kullback-Leibler divergence rejective sampling unequal probability sampling without replacement
2012/11/23
For fixed size sampling designs with high entropy it is well known that the variance of the Horvitz-Thompson estimator can be approximated by the H\'ajek formula. The interest of this asymptotic varia...
Ancestral Inference from Functional Data: Statistical Methods and Numerical Examples
comparative analysis Ornstein-Uhlenbeck process non-parametric Bayesian infer-ence functional phylogenetics ancestral reoncon-struction
2012/9/17
Many biological characteristics of evolutionary inter-est are not scalar variables but continuous functions.Here we use phylogenetic Gaussian process regres-sion to model the evolution of simulated fu...
General notions of depth for functional data
Multivariate functional depth central regions trimmed regions -depth graph depth location-slope depth grid depth principal component depth
2012/9/17
A data depth measures the centrality of a point with respect to an empirical distribution. Postulates are formulated, which a depth for functional data should satisfy, and a general approach is propos...
Revealing spatial variability structures of geostatistical functional data via Dynamic Clustering
functional data clustering geostatistics variogram
2011/7/6
In several environmental applications data are functions of time, essentially con- tinuous, observed and recorded discretely, and spatially correlated. Most of the methods for analyzing such data are ...
Resistant estimates for high dimensional and functional data based on random projections
Resistant estimates high dimensional functional data based
2011/7/5
In this paper we propose a new robust estimation method based on random projections which is adaptive, produces an automatic robust estimate, while being easy to compute for high or infinite dimension...
Confidence bands for Horvitz-Thompson estimators using sampled noisy functional data
CLT functional data local polynomial smoothing maximal inequalities space of continuous functions suprema of Gaussian processes survey sampling weighted crossvalidation
2011/6/17
When collections of functional data are too large to be exhaustively observed, survey
sampling techniques provide an e ective way to estimate global quantities such as
the population mean function. ...
Semi-supervised logistic discrimination for functional data
EM algorithm Functional data analysis Model selec-tion Regularization Semi-supervised learning
2011/3/24
Multi-class classification methods based on both labeled and unlabeled functional data sets are discussed. We present semi-supervised logistic models for classification in the context of functional da...
A Hierarchical Model for Aggregated Functional Data
Bayes'theorem B-splines Covariance function Gaussian process
2011/3/21
In many areas of science one aims to estimate latent sub-population mean curves based only on observations of aggregated population curves. By aggregated curves we mean linear combination of functiona...
Functional data often arise from measurements on fine time grids and are obtained by separating an almost continuous time record into natural consecutive intervals, for example, days. The functions th...
Functional data analysis of nonlinear modes of variation
Functional data analysis nonlinear modes of variation analysis of variance Fréchet mean Fréchet variance variation in manifolds
2009/9/16
A set of curves or images of similar shape is an increasingly common functional data set collected in the sciences. Principal Component Analysis (PCA) is the most widely used technique to decompose va...
A powerful test based on tapering for use in functional data analysis
functional data analysis quadratic forms high-dimensional testing rates of testing Fourier decomposition
2009/9/16
A test based on tapering is proposed for use in testing a global linear hypothesis under a functional linear model. The test statistic is constructed as a weighted sum of squared linear combinations o...
Weighted least squares methods for prediction in the functional data linear model
Cross-validation eigenfunction eigenvector functional data analysis mean squared error orthogonal series rate of convergence
2009/9/16
The problem of prediction in functional linear regression is conventionally addressed by reducing dimension via the standard principal component basis. In this paper we show that weighted least-square...
INFERRING GENE DEPENDENCY NETWORKS FROM GENOMIC LONGITUDINAL DATA：A FUNCTIONAL DATA APPROACH
graphical model longitudinal data dynamical correlation gene dependency networks
2009/2/25
A key aim of systems biology is to unravel the regulatory interactions among genes
and gene products in a cell. Here we investigate a graphical model that treats the
observed gene expression over ti...
Weighted least squares methods for prediction in the functional data linear model
Cross-validation eigenfunction eigenvector functional data analysis functional linear regression mean squared error orthogonal series
2010/3/18
The problem of prediction in functional linear regression is conventionally addressed
by reducing dimension via the standard principal component basis. In this paper we
show that an alternative basi...