43,028 research outputs found
Robust estimation for zero-inflated Poisson regression
ABSTRACT. The zero-inflated Poisson regression model is a special case of finite mixture models that is useful for count data containing many zeros. Typically, maximum likelihood (ML) estimation is used for fitting such models. However, it is well known that the ML estimator is highly sensitive to the presence of outliers and can become unstable when mixture components are poorly separated. In this paper, we propose an alternative robust estimation approach, robust expectation-solution (RES) estimation. We compare the RES approach with an existing robust approach, minimum Hellinger distance (MHD) estimation. Simulation results indicate that both methods improve on ML when outliers are present and/or when the mixture components are poorly separated. However, the RES approach is more efficient in all the scenarios we considered. In addition, the RES method is shown to yield consistent and asymptotically normal estimators and, in contrast to MHD, can be applied quite generally
Uncoupled isotonic regression via minimum Wasserstein deconvolution
Isotonic regression is a standard problem in shape-constrained estimation
where the goal is to estimate an unknown nondecreasing regression function
from independent pairs where . While this problem is well understood both statistically and
computationally, much less is known about its uncoupled counterpart where one
is given only the unordered sets and . In this work, we leverage tools from optimal transport theory to derive
minimax rates under weak moments conditions on and to give an efficient
algorithm achieving optimal rates. Both upper and lower bounds employ
moment-matching arguments that are also pertinent to learning mixtures of
distributions and deconvolution.Comment: To appear in Information and Inference: a Journal of the IM
The Importance of Being Clustered: Uncluttering the Trends of Statistics from 1970 to 2015
In this paper we retrace the recent history of statistics by analyzing all
the papers published in five prestigious statistical journals since 1970,
namely: Annals of Statistics, Biometrika, Journal of the American Statistical
Association, Journal of the Royal Statistical Society, series B and Statistical
Science. The aim is to construct a kind of "taxonomy" of the statistical papers
by organizing and by clustering them in main themes. In this sense being
identified in a cluster means being important enough to be uncluttered in the
vast and interconnected world of the statistical research. Since the main
statistical research topics naturally born, evolve or die during time, we will
also develop a dynamic clustering strategy, where a group in a time period is
allowed to migrate or to merge into different groups in the following one.
Results show that statistics is a very dynamic and evolving science, stimulated
by the rise of new research questions and types of data
Moment-based Estimation of Mixtures of Regression Models
Finite mixtures of regression models provide a flexible modeling framework
for many phenomena. Using moment-based estimation of the regression parameters,
we develop unbiased estimators with a minimum of assumptions on the mixture
components. In particular, only the average regression model for one of the
components in the mixture model is needed and no requirements on the
distributions. The consistency and asymptotic distribution of the estimators is
derived and the proposed method is validated through a series of simulation
studies and is shown to be highly accurate. We illustrate the use of the
moment-based mixture of regression models with an application to wine quality
data.Comment: 17 pages, 3 figure
Modelling Background Noise in Finite Mixtures of Generalized Linear Regression Models
In this paper we show how only a few outliers can completely break down EM-estimation of mixtures of regression models. A simple, yet very effective way of dealing with this problem, is to use a component where all regression parameters are fixed to zero to model the background noise. This noise component can be easily defined for different types of generalized linear models, has a familiar interpretation as the empty regression model, and is not very sensitive with respect to its own parameters
Robust EM algorithm for model-based curve clustering
Model-based clustering approaches concern the paradigm of exploratory data
analysis relying on the finite mixture model to automatically find a latent
structure governing observed data. They are one of the most popular and
successful approaches in cluster analysis. The mixture density estimation is
generally performed by maximizing the observed-data log-likelihood by using the
expectation-maximization (EM) algorithm. However, it is well-known that the EM
algorithm initialization is crucial. In addition, the standard EM algorithm
requires the number of clusters to be known a priori. Some solutions have been
provided in [31, 12] for model-based clustering with Gaussian mixture models
for multivariate data. In this paper we focus on model-based curve clustering
approaches, when the data are curves rather than vectorial data, based on
regression mixtures. We propose a new robust EM algorithm for clustering
curves. We extend the model-based clustering approach presented in [31] for
Gaussian mixture models, to the case of curve clustering by regression
mixtures, including polynomial regression mixtures as well as spline or
B-spline regressions mixtures. Our approach both handles the problem of
initialization and the one of choosing the optimal number of clusters as the EM
learning proceeds, rather than in a two-fold scheme. This is achieved by
optimizing a penalized log-likelihood criterion. A simulation study confirms
the potential benefit of the proposed algorithm in terms of robustness
regarding initialization and funding the actual number of clusters.Comment: In Proceedings of the 2013 International Joint Conference on Neural
Networks (IJCNN), 2013, Dallas, TX, US
Entropic optimal transport is maximum-likelihood deconvolution
We give a statistical interpretation of entropic optimal transport by showing
that performing maximum-likelihood estimation for Gaussian deconvolution
corresponds to calculating a projection with respect to the entropic optimal
transport distance. This structural result gives theoretical support for the
wide adoption of these tools in the machine learning community
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