共查询到7条相似文献,搜索用时 0 毫秒
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Dynamic clustering for interval data based on L
2 distance 总被引:2,自引:0,他引:2
Francisco de A. T. de Carvalho Paula Brito Hans-Hermann Bock 《Computational Statistics》2006,21(2):231-250
Summary This paper introduces a partitioning clustering method for objects described by interval data. It follows the dynamic clustering
approach and uses and L
2 distance. Particular emphasis is put on the standardization problem where we propose and investigate three standardization
techniques for interval-type variables. Moreover, various tools for cluster interpretation are presented and illustrated by
simulated and real-case data. 相似文献
3.
Reda Boukezzoula Sylvie GalichetAmory Bisserier 《International Journal of Approximate Reasoning》2011,52(9):1257-1271
In this paper, a revisited interval approach for linear regression is proposed. In this context, according to the Midpoint-Radius (MR) representation, the uncertainty attached to the set-valued model can be decoupled from its trend. The estimated interval model is built from interval input-output data with the objective of covering all available data. The constrained optimization problem is addressed using a linear programming approach in which a new criterion is proposed for representing the global uncertainty of the interval model. The potential of the proposed method is illustrated by simulation examples. 相似文献
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A class of estimators of the mean survival time from interval censored data with application to linear regression 总被引:3,自引:0,他引:3
Zu-kang Zheng 《高校应用数学学报(英文版)》2008,23(4):377-390
A class of estimators of the mean survival time with interval censored data are studied by unbiased transformation method. The estimators are constructed based on the observations to ensure unbiasedness in the sense that the estimators in a certain class have the same expectation as the mean survival time. The estimators have good properties such as strong consistency (with the rate of O(n^-1/1 (log log n)^1/2)) and asymptotic normality. The application to linear regression is considered and the simulation reports are given. 相似文献
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Several papers have already stressed the interest of latent root regression and its similarities to partial least squares
regression. A new formulation of this method which makes it even simpler than the original method to set up a prediction model
is discussed. Furthermore, it is shown how this method can be extended not only to the case where it is desired to predict
several response variables from a set of predictors but also to the multiblock setting where the aim is to predict one or
several data sets from several other data sets. The interest of the method is illustrated on the basis of a data set pertaining
to epidemiology. 相似文献
6.
To improve the reduction of metrological data, that are typically grouped in series and cannot be considered as replicated data, a modelling procedure has been obtained by adding to the model representing the physical behaviour, common to all data, a specific term for each series. Such a procedure combines both the advantages of preserving the individuality of each series and of improving the variance estimate which arises from fitting the overall data. A non-parametric bootstrap method for the error analysis has been developed, which does not imply the assumption of the Normal distribution in the least squares estimation. Two examples of application of the method to thermodynamic data series are reported. 相似文献
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Isotonic nonparametric least squares (INLS) is a regression method for estimating a monotonic function by fitting a step function to data. In the literature of frontier estimation, the free disposal hull (FDH) method is similarly based on the minimal assumption of monotonicity. In this paper, we link these two separately developed nonparametric methods by showing that FDH is a sign-constrained variant of INLS. We also discuss the connections to related methods such as data envelopment analysis (DEA) and convex nonparametric least squares (CNLS). Further, we examine alternative ways of applying isotonic regression to frontier estimation, analogous to corrected and modified ordinary least squares (COLS/MOLS) methods known in the parametric stream of frontier literature. We find that INLS is a useful extension to the toolbox of frontier estimation both in the deterministic and stochastic settings. In the absence of noise, the corrected INLS (CINLS) has a higher discriminating power than FDH. In the case of noisy data, we propose to apply the method of non-convex stochastic envelopment of data (non-convex StoNED), which disentangles inefficiency from noise based on the skewness of the INLS residuals. The proposed methods are illustrated by means of simulated examples. 相似文献