Estimation of the Order of Non-Parametric Hidden Markov Models using the Singular Values of an Integral Operator

TL;DR

This paper introduces a novel, data-driven method to estimate the order of non-parametric Hidden Markov Models by analyzing the singular values of an integral operator derived from pairs of observations, overcoming limitations of spectral methods.

Contribution

The paper presents a new order estimation procedure for non-parametric HMMs that does not require basis function choices or prior upper bounds, and is applicable to various data types.

Findings

Method accurately estimates HMM order with high probability.

Provides a consistent estimator with data-driven parameter selection.

Handles diverse data types including continuous and circular data.

Abstract

We are interested in assessing the order of a finite-state Hidden Markov Model (HMM) with the only two assumptions that the transition matrix of the latent Markov chain has full rank and that the density functions of the emission distributions are linearly independent. We introduce a new procedure for estimating this order by investigating the rank of some well-chosen integral operator which relies on the distribution of a pair of consecutive observations. This method circumvents the usual limits of the spectral method when it is used for estimating the order of an HMM: it avoids the choice of the basis functions; it does not require any knowledge of an upper-bound on the order of the HMM (for the spectral method, such an upper-bound is defined by the number of basis functions); it permits to easily handle different types of data (including continuous data, circular data or multivariate continuous data) with a suitable choice of kernel. The method relies on the fact that the order of the HMM can be identified from the distribution of a pair of consecutive observations and that this order is equal to the rank of some integral operator (\emph{i.e.} the number of its singular values that are non-zero). Since only the empirical counter-part of the singular values of the operator can be obtained, we propose a data-driven thresholding procedure. An upper-bound on the probability of overestimating the order of the HMM is established. Moreover, sufficient conditions on the bandwidth used for kernel density estimation and on the threshold are stated to obtain the consistency of the estimator of the order of the HMM. The procedure is easily implemented since the values of all the tuning parameters are determined by the sample size.