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Smyth, G. K., and Hawkins, D. M. (2000). Robust frequency estimation using elemental sets. Journal of Computational and Graphical Statistics 9, 196-214.

# Robust Frequency Estimation Using Elemental Sets

Gordon K. Smyth, Department of Mathematics, University of Queensland
and Doug M. Hawkins, Department of Applied Statistics, University of Minnesota

## Abstract

The extraction of sinusoidal signals from time-series data is a classic problem of ongoing interest in the statistics and signal processing literatures. Obtaining least squares estimates is difficult because the sum of squares has local minima O(1/n) apart in the frequencies. In practice the frequencies are often estimated using ad hoc and inefficient methods. Problems of data quality have received little attention. An elemental set is a subset of the data containing the minimum number of points such that the unknown parameters in the model can be identified. This paper shows that, using a variant of the classical method of Prony, parameter estimates for a sum of sinusoids can be obtained algebraically from an elemental set. Elemental set methods are used to construct finite algorithm estimators which approximately minimize the least squares, least trimmed sum of squares or least median of squares criteria. The elemental set estimators prove able in simulations to resolve the frequencies to the correct local minima of the objective functions. When used as the first stage of an MM estimator, the constructed estimators based on the trimmed sum of squares and least median of squares criteria produce final estimators which have high breakdown properties and which are simultaneously efficient when no outliers are present. The approach can also be applied to sums of exponentials, and sums of damped sinusoids. The paper includes simulations with one and two sinusoids and two data examples.

Keywords: frequency estimation, sums of exponential functions, elemental sets, least trimmed sum of squares, least median of squares, MM estimators, high breakdown, high efficiency.

## Introduction

### A Special Nonlinear Regression Problem

The normal-error nonlinear regression model is

yt = m(t) + et,  m(t) = g(xt,b),   et ~ N(0,s2)

t = 1,...,n, where the mean function m(t) depends nonlinearly on some unknown coefficient vector b and some covariate vector xt, and we assume normal homoscedastic errors et. One special but important subclass of nonlinear regression models is that in which g(xt,b) is a sum of sinusoids in t with the frequencies, phases and amplitudes constituting the b.

Fitting these sinusoidal models can involve daunting computational problems. The maximum likelihood estimators are ordinary least squares estimators, and the least squares function has local minima spaced O(n-1) apart, making the gradient-based search methods of general non-linear regression ineffective without excellent starting values.

This problem is further complicated by the possibility of outliers in the data. These can be incorporated in the distributional model by assuming that the majority of the et follow the N(0,s2) distribution but that a minority are "contaminated''. This possibility multiplies the difficulties; not only do we have to find elusive estimators, but have to do so in the face of data that disrupt the usual fitting criteria.

In this paper we develop methods which, with high probability, resolve the frequencies to the correct local minima of the objective function, and which have high breakdown points against outliers.

### The Elemental Set Method

The method of elemental sets is widely used in linear regression as a way of getting reasonable parameter estimates in data sets that may have substantial contamination, even by extreme and badly-placed outliers (Marazzi, 1991). It involves performing many fits to a given data set, each fit made to a subsample just large enough to identify the parameters. For example, in a linear regression with p coefficients, the method involves sampling p of the cases and performing an exact fit to get values for the p coefficients. Repeating this with different subset of p cases leads to different coefficient vectors. A key characteristic of these coefficient vectors is that, by definition, each has some support in the data. See Hawkins (1993) for a discussion of the statistical properties of these coefficient vectors, and Rousseeuw (1984), Hawkins, Bradu and Kass (1984), and Rousseeuw and Leroy (1986) for illustrations of the approach for handling linear regression outliers.

The extension of elemental set methods to get high breakdown estimators in nonlinear regression has been considered recently by Stromberg and Ruppert (1992) and by Stromberg (1993, 1995). One difficulty is that the computation of exact fits to elemental sets is far from trivial in general nonlinear regression. This paper shows that this difficulty does not apply to sinusoidal regressions when the observations are at equi-spaced times as the elemental set estimates can be computed algebraically. The approach actually applies to any function m(t) which solves a homogeneous differential equation with constant coefficients, notably sums of exponential functions and damped sinusoids as well as sums of sinusoids, although we concentrate on the sinusoidal problem here. Even when the times are not equi-spaced, it is sometimes possible to interpolate to equi-spaced times for the purposes of the elemental sets as we show in Section 5.2.

The elemental set method is useful for optimizing criteria which are not smooth, have many local minima or are otherwise not amenable to global optimization by refinement in the parameter space. There are other classes of algorithms, notably simulated annealing and genetic algorithms, which can also deal with multiple minima, but these have so far been less used than elemental sets for detecting outliers in linear models. Atkinson and Weisberg (1991) present some performance figures showing the simulated annealing is not competitive with the other approaches. Genetic algorithms are more closely competitive on the linear problem, and are therefore candidates for the nonlinear problem also. However they would be more complex to program than the elemental set method and seem in general more suited to problems where the objective function has more structure around the local minima.

### Criterion Functions

The method of elemental sets involves many repeated fits using different elemental sets, each fit providing an estimate of the parameters and a set of estimated residuals. We can use these different fits in two ways. One is to ``average'' the parameter estimates from the different fits in some sensible way. The other is to look across the different elemental sets for that one yielding the minimum of some criterion function. This is logically related to the random search for the least squares frequency estimate suggested by Rosenblatt and Rice (1988), with the difference that each estimate considered has some support in the data so that the candidates are relatively dense in the neighborhood of the true values. In either approach, we can then use the estimate produced by the elemental sets as the starting point for some further iterative refinement.

Write m(t) for some estimate of the regression function of case t. This corresponds to the estimate of the residual

et = yt - m(t)

Conventional estimates are found by the LS criterion -- minimizing the sum of squares of all n of these residuals. This criterion is motivated by ideas of statistical efficiency but is inappropriate if some of the residuals may be contaminated. The wish to protect the estimate from such contamination leads to the minimization of outlier-insensitive criteria. Two well-known examples are the ``least trimmed squares'' or LTS criterion and ``least median of squares'' or LMS criterion. LTS aims to minimize the sum of squares of the h smallest absolute residuals while LMS aims to minimize the hth smallest squared residual. The coverage h is chosen to reflect the amount of protection against outliers that is felt appropriate, with choices slightly above one-half the data set size reflecting the default of seeking the maximum possible protection.

For any criterion, an estimator can be defined with which minimizes the criterion over a large number of elemental sets. In this paper we report on simulations with the LS, LTS and LMS criteria.

### Multistage Estimators

A feature of the elemental sets method is that it is finite. It can therefore be used to generate preliminary estimators with which to initialize iterative estimation schemes such as maximum likelihood or M estimation. In particular, an estimator which combines high breakdown with high efficiency under normal errors can be achieved by using a high breakdown elemental estimator to initialize the MM estimation scheme of Yohai (1987) and Stromberg (1993). We use our elemental set estimators as part of a multistage estimator similar to that of Stromberg (1993). We find that the elemental LTS and LMS estimators are successful in resolving the frequencies to the correct local minima of the objective function even in the presence of outliers. We find that both criterion are successful as stage one estimators for an MM estimation scheme, and that in simulations the final estimators have both high breakdown properties again outliers and high efficiency when there are no outliers.

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