I-Scal: Multidimensional scaling of interval dissimilarities

Groenen, Patrick J. F.; Winsberg, Suzanne; Rodríguez, Omar Gerardo Lizano; Diday, Edwin

doi:10.1016/j.csda.2006.04.003

Cited by 37 publications

(33 citation statements)

References 19 publications

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“…This solution was proved to be efficient and advantageous in our localization scenario when compared to different choices of objects' shapes [11].…”

Section: A Circular Interval-based Smacofmentioning

confidence: 98%

Sensor localization with algebraic confidence

Saloranta

Severi

Macagnano

et al. 2012

2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)

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In this paper we introduce the concept of algebraic confidence, defined as the measure of belief provided by an algebraic algorithm without a priori information of ranging statistics. This is obtained by employing the Circular-based Interval SMACOF (CIS) algorithm which outputs an algebraic confidence level on targets' estimates, without relying on the propagation of location distributions. We illustrate the validity of our confidence by comparing it to the Fisher error ellipses derived from the Cramér-Rao lower bound (CRLB), corroborating the concept. To exploit the proposed confidence measures during the optimization process, we modify the cost function used in the Circular-based Interval SMACOF (CIS) algorithm and describe how to solve the corresponding optimization problem by means of majorization techniques. The resulting CIS+ algorithm is shown to outperform the CIS algorithm both in terms of accuracy as well as in terms of computational complexity.

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“…This solution was proved to be efficient and advantageous in our localization scenario when compared to different choices of objects' shapes [11].…”

Section: A Circular Interval-based Smacofmentioning

confidence: 98%

Sensor localization with algebraic confidence

Saloranta

Severi

Macagnano

et al. 2012

2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)

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“…Maia et al [32] have introduced autoregressive integrated moving average (ARIMA) as well as a hybrid methodology that combines both ARIMA and artificial neural network models. Other contributions were proposed by Groenen et al [22], Ichino et al [24] and Arroyo et al [1]. A large number of cluster methods were proposed to interval-valued data, differing in terms of the cluster structure and/or clustering criteria [10,[12][13][14]21,23].…”

Section: Introductionmentioning

confidence: 98%

Regression model for interval-valued variables based on copulas

Neto

Anjos

2015

Journal of Applied Statistics

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In real problems, it is usual to have the available data presented as intervals. Therefore, different approaches have been proposed to obtain a regression model for this new type of data. In this paper, we represent the interval-valued response variable Z = [Y L , Y U ] as a bivariate random vector and we consider the copula's theory to propose a general bivariate distribution for Z, creating a more flexible random component to the model. Inference techniques and a residual definition based on deviance are considered, as well as applications to synthetic and real data sets that demonstrate the usefulness of the proposed approach. The new method is also compared with other methods reported in the literature.

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“…The size of these regions is simultaneously affected by the uncertainties related to the data and to the scaling model. In this paper [13], one of the models also considered in the preceding paper by Hébert et al is studied in more detail.…”

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confidence: 97%