1 Introduction
Weighted Likelihood Estimating Equations (WLEE) are often used with the aim of obtaining robust estimators. Green [1984] is perhaps one of the earliest example, Field and Smith [1994] proposes a WLEE with weights that depends on the tail behavior of the distribution function, Markatou et al. [1997, 1998] defines WLEE with weights derived from the estimating equations of a disparity minimization problems. Kuchibhotla and Basu [2017] further improve this approach by providing a strict connection between WLEE and disparity minimization problem. Biswas et al. [2015] get ideas from both Field and Smith [1994] and Markatou et al. [1998] and provide a similar approach based on distribution functions. Their approach is very natural and easy to implement, however one of the drawback it that the resulting estimators are not affine equivariant.
Statistical data depth was first introduced for multivariate observations, see Liu et al. [2006]
and the reference therein for a review. The main goal is to provide a centeroutward ordering of multivariate observations that can be used for several purposes, e.g. find centers: depth maximumers; regions contains most of the observations: depth regions and quantile depth based regions; comparing distributions: DepthDepth plot and tests based on depth, and many others. One of the main features of a depth is its invariance under affine trasformations. Furthermore, results shows that for a given model both the empirical distribution and the distribution function are completely characterized by the statistical data depth.
Our main goal is to propose a simple WLEE whose weights are based on statistical data depth. Section 2 provides an overview of the weighted likelihood stategies and our new proposal. Section 3 shows how we can use our new method in the multivariate normal model and Section 4 illustrates the methodology with two real examples. Section 5 reports some comments and conclusions.
2 Weighted Likelihood based on Data Depth
Let be a random sample from a
random vector
with unknown distribution function and corresponding density function . We assume a model for by and we denotethe probability density function. Let
be the empirical distribution function. [Lindsay, 1994] introduced the concept of Pearson residuals defined by comparing the true density to the model density asso that, when for a given the Pearson residuals are equal to zero for all , whereas if , in regions where the density is higher than the Pearson residual are large indicating the disagreement between the number of observations in that regions and the expected observations under the model. The finite sample version of the Pearson residuals is given by , which compares , a non parametric estimate of , to the model density . [Lindsay, 1994] studied a class of estimators based on the Pearson residuals for discrete models, while Basu and Lindsay [1994] and Markatou et al. [1998] discussed proposals for continuous models. In particular, Markatou et al. [1997, 1998] introduced weights evaluated by
(1) 
where is the Residual Adjustment Function [RAF, Lindsay, 1994, Park et al., 2002] obtaining a Weighted Likelihood Estimating Equations (WLEE)
(2) 
where denotes the th contribution to the score function. These estimating equations are derived from a disparity measure however, there is no exact link between the two approaches. Recently, Kuchibhotla and Basu [2017] provide a WLEE in the same spirit which corresponds to a disparity measures, they also formally prove the asympotic and robust properties of their approach. In the attempt to avoid the use of non parametric estimators Biswas et al. [2015] proposes to define Pearson residuals as ratio of distribution functions such as
Let be a smooth function defined on , which assumes its maximum value at and descends smoothly in either tail as moves away from , i.e., and and the next higher nonzero derivative at has a negative sign; an example is
where and are constants. Biswas et al. [2015] defines a weight function as
Clearly, also weights in the form of (1) are possible. Their approach is general and can be used in multivariate setting as they show a bivariate example. However, their definition makes the Pearson residuals not affine invariant and hence the final estimates are not affine equivariant. We are going to propose a general approach to construct weights in the same spirit using statistical data depth which is more natural and is affine invariant.
Let be a statistical data depth [Zuo and Serfling, 2000a, Liu et al., 2006] for the point according to the distribution of the r.v. . Let be the finite sample version based on the empirical distribution function of the sample . Denote the class of distributions in , assume that satisfies the following properties [Liu, 1990, Zuo and Serfling, 2000a]
 P1.

Affine Invariance. for any distribution function , any nonsingular matrix and any vector .
 P2.

Maximality at Center. For an having “center” (e.g. the point of symmetry relative to some notion of symmetry), .
 P3.

Monotonicity Relative to Deepest Point. For any having deepest point (i.e., point of maximal depth), , .
 P4.

Vanishing at Infinity. as , for each .
We define the Pearson residual as
and the finite sample version as
This Pearson residual have the desired behaviour being equal to whenever for some , and of attaining large values in regions where the two distributions are mismatched; furthermore, because of the invariance property P1. of , the Pearson residual is also invariant to affine transformations. For most depths a uniform convergence of to holds almost surely; hence we expect that, at the model, the proposed method is highly efficient. Using weights based on (1) or (2) leads to a WLEE that can be solved by an iterative reweigthing algorithm.
3 Application to the Multivariate Normal model
Consider be a Multivariate Normal model where , is the mean vector and
is the variancecovariance matrix. We discuss how to evaluate the proposed Pearson residual using the halfspace depth. We first review the concept of halfspace depth
[Tukey, 1975, Donoho and Gasko, 1992]. Let be the closed halfspace . Here is a vector satisfying . Note thatis the positive side of the hyperplane
. The negative side of is similarly defined. Halfspace depth is the minimum probability of all halfspaces including .Definition 1
The halfspace depth maps to the minimum probability, according to the random vector , of all closed halfspaces including , that is
This statistical data depth is particularly usefull since its properties. In particular, Struyf and Rousseeuw [1999, Theorem 1] show that the finite sample halfspace depth characterizes the empirical distribution. Kong and Zuo [2010, Corollary 3.1] show that the halfspace depth characterizes the underlying distribution. Furthermore, Zuo and Serfling [2000b, Theorem 3.3, Corollay 4.3] shows that the halfspace depth for a multivariate normal model can be easily obtained since where is the squared Mahalanobis distance and is the distribution function of a chisquared with degrees of freedom random variable. Finally, calculation of the finite sample halfspace depth in a dimension can be performed efficiently using the algorithms proposed in Liu [2017] and Dyckerhoff and Mozharovskyi [2016] and available in the R package ddalpha by Pokotylo et al. [2016]. Similar results are available for any model belonging to the elliptically symmetric family of distributions.
4 Examples
We consider the data set pb52 available in the R package phonTools Barreda [2015] which contains the Vowel Recognition Data considered in Peterson and Barney [1952], see also Boersma and Weenink [2012]. In a first example a bivariate data set is illustrated, where we consider the vowels “u” (close back rounded vowel) and “æ” (nearopen front unrounded vowel, “{” as xsampa symbol) with a sample of size equally divided for each vowel and the log transformed F1 and F2 frequencies measured in Hz. Our procedure use the following settings , , , and uses subsamples of size as starting values for finding the roots. We also consider the Maximum Likelihood (MLE), the Minimum Covariance Determinant (MCD), the Minimum Volume Ellipsoid (MVE) and the SEstimates (S) as implemented in the R package rrcov by Todorov and Filzmoser [2009], the last three procedures were used with the exaustive subsampling explorations. Figure 1 in the left panel reports the three estimates found by our methods. While the first root coincides with the MLE, the other two nicely identify the two subgroups. This is not the case for all the other investigated methods, see Figure 1 right panel, where the estimates are approximately all coincident with the MLE.
In a second example a trivariate data set is used considering vowels “u” (close back rounded vowel) and “” (openmid central unrounded vowel, “3” or “” as xsampa symbol) with a sample of size equally divided for each vowel and the log transformed F1, F2 and F3 frequencies measured in Hz. Figure (2) shows the results. For the classical robust procedures only MCD is able to somehow recover the structure of the observations for the vowels “”, while the other behave like the MLE. Our procedure finds nicely all the two substructure. For this data our procedure was set using , , , and subsamples of size as starting values for finding the roots. Using the setting of the first examples only the first two roots are found.
5 Conclusions
We have outlined a new form of weighted likelihood estimating equations where weights are based on comparing statistical data depth of the sample with that of the model. This approach avoids the use of nonparametric density estimates which can lead to problems for multivariate data, while retains nice characteristics of the classical WLEE approach, that are high efficiency at the model, affine equivariance and robustness. In the future, we hope to formally establish the theoretical properties of the proposed estimator.
References
 Barreda [2015] S. Barreda. phonTools: Functions for phonetics in R., 2015. R package version 0.22.1.
 Basu and Lindsay [1994] A. Basu and B.G. Lindsay. Minimum disparity estimation for continuous models: efficiency, distributions and robustness. Annals of the Institute of Statistical Mathematics, 46(4):683–705, 1994.
 Biswas et al. [2015] A. Biswas, T. Roy, S. Majumder, and A. Basu. A new weighted likelihood approach. Stat, 4(1):97–107, 2015.
 Boersma and Weenink [2012] P. Boersma and D. Weenink. Praat: doing phonetics by computer [computer program]. version 5.3.19. http://www.praat.org/, 2012. retrieved 24 June 2012.
 Donoho and Gasko [1992] D.L. Donoho and M. Gasko. Breakdown properties of location estimates based on halfspace depth and projected outlyingness. The Annals of Statistics, 20:1808–1827, 1992.
 Dyckerhoff and Mozharovskyi [2016] R. Dyckerhoff and P. Mozharovskyi. Exact computation of the halfspace depth. Computational Statistics & Data Analysis, 98:19–30, 2016.
 Field and Smith [1994] C. Field and B. Smith. Robust estimation – a weighted maximum likelihood approach. International Statistical Review, 62:405–424, 1994.
 Green [1984] P.J. Green. Iteratively reweighted least squares for maximum likelihood estimation, and some robust and resistent alternatives. Journal of the Royal Statistical Society: Series B, 46:149–192, 1984.

Kong and Zuo [2010]
L. Kong and Y. Zuo.
Smooth depth contours characterize the underlying distribution.
Journal of Multivariate Analysis
, 101:2222–2226, 2010.  Kuchibhotla and Basu [2017] A.K. Kuchibhotla and A. Basu. A minimum distance weighted likelihood method of estimation. Technical report, Interdisciplinary Statistical Research Unit (ISRU), Indian Statistical Institute, Kolkata, India, 2017.
 Lindsay [1994] B.G. Lindsay. Efficiency versus robustness: The case for minimum hellinger distance and related methods. The Annals of Statistics, 22:1018–1114, 1994.
 Liu [1990] R.Y. Liu. On a notion of data depth based on random simplices. The Annals of Statistics, 18(1):405–414, 1990.
 Liu et al. [2006] R.Y. Liu, R.J. Serfling, and D.L. Souvaine. Data depth: robust multivariate analysis, computational geometry, and applications. AMS Bookstore, 2006.
 Liu [2017] X. Liu. Fast implementation of the tukey depth. Computational Statistics, 32(4):1395–1410, 2017.

Markatou et al. [1997]
M. Markatou, A. Basu, and B.G. Lindsay.
Weighted likelihood estimating equations: the discrete case with applications to logistic regression.
Journal of Statistical Planning and Inference, 57:215–232, 1997.  Markatou et al. [1998] M. Markatou, A. Basu, and B.G. Lindsay. Weighted likelihood equations with bootstrap root search. Journal of the American Statistical Association, 93(442):740–750, 1998.
 Park et al. [2002] C. Park, A. Basu, and B.G. Lindsay. The residual adjustment function and weighted likelihood: a graphical interpretation of robustness of minimum disparity estimators. Computational Statistics & Data Analysis, 39(1):21–33, 2002.
 Peterson and Barney [1952] G.E. Peterson and H.L. Barney. Control methods used in a study of the vowels. Journal of the Acoustical Society of America, 24:175–184, 1952.
 Pokotylo et al. [2016] O. Pokotylo, P. Mozharovskyi, and R. Dyckerhoff. Depth and depthbased classification with rpackage ddalpha. arXiv:1608.04109, 2016.
 Struyf and Rousseeuw [1999] A. Struyf and P.J. Rousseeuw. Halfspace depth and regression depth characterize the empirical distribution. Journal of Multivariate Analysis, 69:135–153, 1999.
 Todorov and Filzmoser [2009] V. Todorov and P. Filzmoser. An objectoriented framework for robust multivariate analysis. Journal of Statistical Software, 32(3):1–47, 2009.
 Tukey [1975] J.W. Tukey. Mathematics and picturing of data. In Proceedings of International Congress of Mathematics, volume 2, pages 523–531, 1975.
 Zuo and Serfling [2000a] Y. Zuo and R.J. Serfling. General notions of statistical depth function. The Annals of Statistics, 28(2):461–482, 2000a.
 Zuo and Serfling [2000b] Y. Zuo and R.J. Serfling. Structual properties and convergence results for contours of sample statistical depth functions. The Annals of Statistics, 28(2):483–499, 2000b.
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