Statistical description and dimension reduction of continuous time categorical trajectories with multivariate functional principal components

Getting tools that allow simple representations and comparisons of a set of categorical trajectories is of major interest for statisticians. Without loosing any information, we associate to each state a binary random indicator function, taking values in ${0,1}$, and turn the problem of statistical description of the categorical trajectories into a multivariate functional principal components analysis. This viewpoint encompasses experimental frameworks where two or more states can be observed simultaneously. The sample paths being piecewise constant, with a finite number of jumps, this a rare case in functional data analysis in which the trajectories are not supposed to be continuous and can be observed exhaustively. Under the weak hypothesis assuming only continuity in probability of the $0-1$ trajectories, the means and the (multivariate) covariance function are continuous and have interpretations in terms of departure from independence of the joint probabilities. Considering a functional data point of view, we show that the binary trajectories, which are right-continuous functions with left-hand limits, can be seen as random elements in the Hilbert space of square integrable functions. The multivariate functional principal components are simple to interpret and we show that we can get consistent estimators of the mean trajectories and the covariance functions under weak regularity assumptions. The ability of the approach to represent categorical trajectories in a small dimension space is illustrated on a data set of sensory perceptions, considering different gustometer-controlled stimuli experiments.

💡 Research Summary

The paper introduces a novel framework for the statistical analysis and dimension reduction of continuous‑time categorical trajectories. By representing each categorical state S₁,…,S_q with a binary indicator process X_j(t)=1{Y(t)=S_j} (or 1{Y(t)∋S_j} when multiple states can co‑occur), the authors transform a categorical trajectory Y(t) into a q‑dimensional 0‑1 functional vector X(t). This representation is lossless and allows the use of functional data analysis tools even though the trajectories are piecewise constant with a finite number of jumps.

The key probabilistic assumption (H1) is that the process is continuous in probability: lim_{h→0} P