Search results for "Distance"
showing 10 items of 1009 documents
Weighted distance-based trees for ranking data
2017
Within the framework of preference rankings, the interest can lie in finding which predictors and which interactions are able to explain the observed preference structures, because preference decisions will usually depend on the characteristics of both the judges and the objects being judged. This work proposes the use of a univariate decision tree for ranking data based on the weighted distances for complete and incomplete rankings, and considers the area under the ROC curve both for pruning and model assessment. Two real and well-known datasets, the SUSHI preference data and the University ranking data, are used to display the performance of the methodology.
Cotas inferiores para el QAP-Arbol
1985
The Tree-QAP is a special case of the Quadratic Assignment Problem where the flows not equal zero form a tree. No condition is required for the distance matrix. In this paper we present an integer programming formulation for the Tree-QAP. We use this formulation to construct four Lagrangean relaxations that produce several lower bounds for this problem. To solve one of the relaxed problems we present a Dynamic Programming algorithm which is a generalization of the algorithm of this type that gives a lower bound for the Travelling Salesman Problem. A comparison is given between the lower bounds obtained by each ralaxation for examples with size from 12 to 25.
Efficient spatial designs using Hausdorff distances and Bayesian optimization
2021
An iterative Bayesian optimisation technique is presented to find spatial designs of data that carry much information. We use the decision theoretic notion of value of information as the design criterion. Gaussian process surrogate models enable fast calculations of expected improvement for a large number of designs, while the full-scale value of information evaluations are only done for the most promising designs. The Hausdorff distance is used to model the similarity between designs in the surrogate Gaussian process covariance representation, and this allows the suggested algorithm to learn across different designs. We study properties of the Bayesian optimisation design algorithm in a sy…
Accounting for previous events to model and predict traffic accidents at the road segment level: A study in Valencia (Spain)
2022
Abstract Predicting the occurrence of traffic accidents is essential for establishing preventive measures and reducing the impact of traffic accidents. In particular, it is fundamental to make predictions using fine spatio-temporal units. In this paper, the daily risk of traffic accident occurrence across the road network of Valencia (Spain) is modeled through logistic regression models. The spatio-temporal dependence between the observations is accounted for through the inclusion of lagged binary covariates representing the previous occurrence of a traffic accident within a spatio-temporal window centered at each combination of day and segment of the network. A temporal distance of 28 days…
Tests of multinormality based on location vectors and scatter matrices
2007
Classical univariate measures of asymmetry such as Pearson’s (mean-median)/σ or (mean-mode)/σ often measure the standardized distance between two separate location parameters and have been widely used in assessing univariate normality. Similarly, measures of univariate kurtosis are often just ratios of two scale measures. The classical standardized fourth moment and the ratio of the mean deviation to the standard deviation serve as examples. In this paper we consider tests of multinormality which are based on the Mahalanobis distance between two multivariate location vector estimates or on the (matrix) distance between two scatter matrix estimates, respectively. Asymptotic theory is develop…
Recursive estimation of the conditional geometric median in Hilbert spaces
2012
International audience; A recursive estimator of the conditional geometric median in Hilbert spaces is studied. It is based on a stochastic gradient algorithm whose aim is to minimize a weighted L1 criterion and is consequently well adapted for robust online estimation. The weights are controlled by a kernel function and an associated bandwidth. Almost sure convergence and L2 rates of convergence are proved under general conditions on the conditional distribution as well as the sequence of descent steps of the algorithm and the sequence of bandwidths. Asymptotic normality is also proved for the averaged version of the algorithm with an optimal rate of convergence. A simulation study confirm…
A more efficient second order blind identification method for separation of uncorrelated stationary time series
2016
The classical second order source separation methods use approximate joint diagonalization of autocovariance matrices with several lags to estimate the unmixing matrix. Based on recent asymptotic results, we propose a novel unmixing matrix estimator which selects the best lag set from a finite set of candidate sets specified by the user. The theory is illustrated by a simulation study.
Classification trees for multivariate ordinal response: an application to Student Evaluation Teaching
2016
Data from multiple items on an ordinal scale are commonly collected when qualitative variables, such as feelings, attitudes and many other behavioral and health-related variables are observed. In this paper we introduce a method to derive a distance-based tree for multivariate ordinal response that allows, when subject-specific characteristics are available, to derive common profiles for respondents giving the same/similar multivariate ratings. Special attention will be paid to the performance comparison in terms of AUC, for three different distances used as splitting criteria. Simulated data an a dataset from a Student Evaluation of Teaching survey will be used as illustrative examples. Th…
Discord of response
2014
The presence of quantum correlations in a quantum state is related to the state response to local unitary perturbations. Such response is quantified by the distance between the unperturbed and perturbed states, minimized with respect to suitably identified sets of local unitary operations. In order to be a bona fide measure of quantum correlations, the distance function must be chosen among those that are contractive under completely positive and trace preserving maps. The most relevant instances of such physically well behaved metrics include the trace, the Bures, and the Hellinger distance. To each of these metrics one can associate the corresponding discord of response, namely the trace,…
Unifying approach to the quantification of bipartite correlations by Bures distance
2014
The notion of distance defined on the set of states of a composite quantum system can be used to quantify total, quantum and classical correlations in a unifying way. We provide new closed formulae for classical and total correlations of two-qubit Bell-diagonal states by considering the Bures distance. Complementing the known corresponding expressions for entanglement and more general quantum correlations, we thus complete the quantitative hierarchy of Bures correlations for Bell-diagonal states. We then explicitly calculate Bures correlations for two relevant families of states: Werner states and rank-2 Bell-diagonal states, highlighting the subadditivity which holds for total correlations…