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• W1969614853 abstract "Loss tomography has received considerable interest in recent years. Although a number of estimators have been proposed for the tree topology, most of them do not consider data missing. To correct this, we in this paper classify data into five classes and propose four estimators, one for a type of data with missing. The estimators are proved to be the maximum likelihood ones. The work is further extended into the general topology that has hardly been explored previously, where a structure between data and models is established. Index Terms—Network tomography, Data missing, Maximum likelihood Estimate (MLE), Observation and Model. I. INTRODUCTION Previous works on loss tomography were mostly focused on proposing estimators and proving the solution obtained by an estimator is the MLE. There has been little concern on the observations obtained from a large multicast networks that can be incomplete. There is no exception for the most influential estimator proposed in this area, such as the one proposed in (1). If we use the estimators developed for a perfect condition on incomplete data, it is inevitable for most of us to take the incorrect estimate as correct one because of the unawareness of the necessity of using a different estimator. To improve this situation, we provide a definition in this paper to clarify the connection between data and model and use the definition to divide data obtained from experiments into classes. We then propose a number of estimators, one for a data class. As stated, this paper aims at identifying some issues missed in loss tomography that contribute to loss tomography in the following areas: 1) we show that the observations received from an experi- ment can be divided into a number of classes, depending on the data missed from a perfect situation. We further classify observations into 5 classes. 2) four maximum likelihood estimators are proposed for the 4 classes that have been overlooked previously. 3) apart from the tree topology, the results obtained above are extended to the general topology that has been hardly investigated. The rest of the paper is organized as follows. In Section II we present the essential background of loss tomography, including the notations and statistics used in this paper. In Section III, we point out the incapability of an well known estimator for some of the observations obtained from an exper- iment. In Section IV we classify observations into five classes and present four estimators for the four data classes overlooked previously. Section V extends the solutions obtained from the tree topology to the general topology. The last section is devoted to concluding remark." @default.
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• W1969614853 date "2014-08-01" @default.
• W1969614853 modified "2023-09-25" @default.
• W1969614853 title "Loss rate estimation with incomplete data set" @default.
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• W1969614853 doi "https://doi.org/10.1109/fskd.2014.6980973" @default.
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