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2002a), “Statistical Analysis of a Telephone Call Center: A Queueing Science Perspective,” technical report, University of Pennsylvania, downloadable at http://iew3.technion.ac.il/serveng/References/references.html
"... A call center is a service network in which agents provide telephonebased services. Customers who seek these services are delayed in telequeues. This article summarizes an analysis of a unique record of call center operations. The data comprise a complete operational history of a small banking cal ..."
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A call center is a service network in which agents provide telephonebased services. Customers who seek these services are delayed in telequeues. This article summarizes an analysis of a unique record of call center operations. The data comprise a complete operational history of a small banking call center, call by call, over a full year. Taking the perspective of queueing theory, we decompose the service process into three fundamental components: arrivals, customer patience, and service durations. Each component involves different basic mathematical structures and requires a different style of statistical analysis. Some of the key empirical results are sketched, along with descriptions of the varied techniques required. Several statistical techniques are developed for analysis of the basic components. One of these techniques is a test that a point process is a Poisson process. Another involves estimation of the mean function in a nonparametric regression with lognormal errors. A new graphical technique is introduced for nonparametric hazard rate estimation with censored data. Models are developed and implemented for forecasting of Poisson arrival rates. Finally, the article surveys how the characteristics deduced from the statistical analyses form the building blocks for theoretically interesting and practically useful mathematical models for call center operations.
On Comparison of Clustering Techniques for Histogram PDF
 Estimation, Pattern Recognit. Image Anal
"... —This paper discusses the problem of finding the number of component clusters in graylevel image histograms. These histograms are often modeled using a standard mixture of univariate normal densities. The problem, however, is that the number of components in the mixture is an unknown variable that ..."
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—This paper discusses the problem of finding the number of component clusters in graylevel image histograms. These histograms are often modeled using a standard mixture of univariate normal densities. The problem, however, is that the number of components in the mixture is an unknown variable that must be estimated, together with the means and the variances. Computing the number of components in a mixture usually requires “unsupervised learning”. This problem is denoted as “cluster validation ” in the cluster analysis literature. The aim is to identify subpopulations believed to be present in a population. A wide variety of methods have been proposed for this purpose. In this paper, we intend to compare two methods, each belonging to a typical approach. The first, somewhat classical method, is based on criterion optimization. We are particularly interested in the Akaike’s information criterion. The second method is based on a direct approach that makes use of a cluster’s geometric properties. In this paper, we develop an algorithm to generate nonoverlapped test vectors, allowing the generation of a large set of verified vectors that can be used to perform objective evaluation and comparison.
Nonparametric Regression for Problems Involving Lognormal Distributions
"... First of all, I wish to devote my sincere thanks and deep appreciation to my dissertation advisor, Professor Lawrence D. Brown, for his tremendous amount of encouragement, guidance and financial support during the development of my research. It is he who has brought me into the wonderful world of St ..."
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First of all, I wish to devote my sincere thanks and deep appreciation to my dissertation advisor, Professor Lawrence D. Brown, for his tremendous amount of encouragement, guidance and financial support during the development of my research. It is he who has brought me into the wonderful world of Statistics. It is him that I will keep learning from and living up to through the rest of my life. Special thanks are due to all those people who had advised and helped me at important steps of my life, among whom are Noah Gans, Jianhua Huang, Paul Shaman
Corresponding author:
, 2004
"... and sponsored research. 1 A call center is a service network in which agents provide telephonebased services. Customers that seek these services are delayed in telequeues. This paper summarizes an analysis of a unique record of call center operations. The data comprise a complete operational histo ..."
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and sponsored research. 1 A call center is a service network in which agents provide telephonebased services. Customers that seek these services are delayed in telequeues. This paper summarizes an analysis of a unique record of call center operations. The data comprise a complete operational history of a small banking call center, call by call, over a full year. Taking the perspective of queueing theory, we decompose the service process into three fundamental components: arrivals, customer patience, and service durations. Each component involves different basic mathematical structures and requires a different style of statistical analysis. Some of the key empirical results are sketched, along with descriptions of the varied techniques required.
Performance evaluation of clustering techniques for image segmentation
"... In this paper, we tackle the performance evaluation of two clustering algorithms: EFC and AICbased. Both algorithms face the cluster validation problem, in which they need to estimate the number of components. While EFC algorithm is a direct method, the AICbased is a verificative one. For a fair q ..."
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In this paper, we tackle the performance evaluation of two clustering algorithms: EFC and AICbased. Both algorithms face the cluster validation problem, in which they need to estimate the number of components. While EFC algorithm is a direct method, the AICbased is a verificative one. For a fair quantitative evaluation, comparisons are conducted on numerical data and image histograms data are used. We also propose to use artificial data satisfying the overlapping rate between adjacent components. The artificial data is modeled as a mixture of univariate normal densities as they are able to approximate a wide class of continuous densities.
An Algorithm for Determination of the Number of Modes for
"... An algorithm for determination of the number of modes in a graylevel image histogram is presented in this paper. The hypothesis is that the image histogram's pdf is approached by a mixture of Gaussians. Then, the algorithm tries to estimate the number of components in the mixture, which is an ..."
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An algorithm for determination of the number of modes in a graylevel image histogram is presented in this paper. The hypothesis is that the image histogram's pdf is approached by a mixture of Gaussians. Then, the algorithm tries to estimate the number of components in the mixture, which is an important parameter when using the maximum likelihood technique to estimate the remaining of parameters of the mixture. The algorithm is divided into two parts. First, initial clustering using the kmeans algorithm is performed. This allows to estimate the centers of each cluster. Second, a novel algorithm, denoted "Elimination of False Clusters" (EFC) based on the Gaussian characteristics tries to suppress clusters which have no corresponding modes in the histogram. The algorithm has been validated on both artificial and real histograms. Keywords: pdf estimation, mixture models, unsupervised learning, graylevel image histogram 1 INTRODUCTION Estimation of a histogram's probability density fu...
An Algorithm for Determination of the Number of Modes for pdf Estimation of multimodal Histograms
"... An algorithm for determination of the number of modes in a graylevel image histogram is presented in this paper. The hypothesis is that the image histogram’s pdf is approached by a mixture of Gaussians. Then, the algorithm tries to estimate the number of components in the mixture, which is an impor ..."
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An algorithm for determination of the number of modes in a graylevel image histogram is presented in this paper. The hypothesis is that the image histogram’s pdf is approached by a mixture of Gaussians. Then, the algorithm tries to estimate the number of components in the mixture, which is an important parameter when using the maximum likelihood technique to estimate the remaining of parameters of the mixture. The algorithm is divided into two parts. First, initial clustering using the kmeans algorithm is performed. This allows to estimate the centers of each cluster. Second, a novel algorithm, denoted “Elimination of False Clusters” (EFC) based on the Gaussian characteristics tries to suppress clusters which have no corresponding modes in the histogram. The algorithm has been validated on both artificial and real histograms.
COMPSTAT’2004 Symposium ○c PhysicaVerlag/Springer 2004 OUTLIER DETECTION AND CLUSTERING BY PARTIAL MIXTURE MODELING
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Practical Robust Learning with L2 & L1 Estimation: Beyond Maximum Likelihood Anonymous Author(s) Affiliation
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DUAL φDIVERGENCES ESTIMATION IN NORMAL MODELS
"... Abstract. A class of robust estimators which are obtained from dual representation of φdivergences, are studied empirically for the normal location model. Members of this class of estimators are compared, and it is found that they are efficient at the true model and offer an attractive alternative ..."
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Abstract. A class of robust estimators which are obtained from dual representation of φdivergences, are studied empirically for the normal location model. Members of this class of estimators are compared, and it is found that they are efficient at the true model and offer an attractive alternative to the maximum likelihood, in term of robustness. Key words and phrases: Minimum divergence estimators; Efficiency; Robustness; Mestimators; Influence function. 1.