Khaled Alotaibi  Vic Rayward‐Smith  Beatriz de la Iglesia 《Statistical Analysis and Data Mining》2014,7(3):175-193

Data perturbation aims to disguise original data values so the confidential information is kept safe and the disclosure risk is minimized. In this article, we exploit the characteristics of nonmetric multidimensional scaling (NMDS) to transform data and generate perturbed data. We hypothesize that NMDS is a good tool for privacy‐preserving data clustering. For this, our model should preserve the distance between data objects (data utility in the context of clustering) whilst introducing enough uncertainty about the original data. We discuss privacy attacks in the context of disclosing the original data values and show that our method introduces uncertainty which could hinder the attacker from determining the exact location of points in the original space. We examine our model on a number of benchmark datasets and compare the results obtained from the perturbed data with those obtained from the original data using a number of clustering algorithms. We compare with other data perturbation techniques such as principal component analysis (PCA), random projection, single value decomposition and discrete cosine transform. We show that both NMDS and PCA produce the best utility preservation, obtaining clusterings of the perturbed data which are very similar to those on the original data. We also show that the transformation using NMDS introduces more uncertainty than other transformations in the point placement location and as the transformation is independent of the original data values it should produce enhanced privacy preservation.  相似文献   

    

Hoang Thanh Lam  Fabian Mrchen  Dmitriy Fradkin  Toon Calders 《Statistical Analysis and Data Mining》2014,7(1):34-52

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Clare Robertson  Craig Ramsay  Tara Gurung  Graham Mowatt  Robert Pickard  Pawana Sharma   《Research Synthesis Methods》2014,5(3):200-211

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Richard A. Levine  Juanjuan Fan  Xiaogang Su  Martha E. Nunn 《Statistical Analysis and Data Mining》2014,7(2):111-124

Tooth loss from periodontal disease or dental caries (decay) afflicts most adults over the course of their lives. Survival tree methods for correlated observations have shown potential for developing objective tooth prognosis systems; however, the current technology suffers either from prohibitive computational expense or unrealistic simplifying assumptions to overcome computational demands. In this article Bayesian tree methods are developed for correlated survival data, relying on a computationally feasible, yet flexible, frailty model with piecewise constant hazard function. Bayesian stochastic search methods, using a Laplace approximated marginal likelihood, are detailed for tree construction, and posterior ensemble averaged variable importance ranking and amalgamation procedures are developed. The proposed methods are used to assign each tooth from the Veteran Administration (VA) Dental Longitudinal Study to one of five prognosis categories and evaluate the effects of clinical factors and genetic polymorphisms in predicting tooth loss. The prognostic rules established may be used in clinical practice to optimize tooth retention and devise periodontal treatment plans.  相似文献   

    

Anna Maria Fiori  Davide Beltrami 《Stat》2014,3(1):95-108

Although the standard fourth moment coefficient is routinely computed as “the kurtosis” of a distribution, the measure is not easily interpreted and has been a subject of considerable debate in statistical literature. The financial community has recently joined in the debate, calling for more robust estimators of kurtosis in distributions of stock market returns. For these reasons, we here consider alternative measures of right and left kurtosis, which arise from a recent characterization of kurtosis as inequality at either side of the median. Based on Gini's coefficient of concentration, the new measures apply to both symmetric and asymmetric distributions, their interpretation is clear and they are consistent with common risk perceptions of investors and risk managers. In this contribution, we show that the theory of L‐statistics provides a natural framework for the construction of empirical estimators of the proposed measures and the derivation of their asymptotic properties under mild moment requirements. A real data example illustrates the potential of these estimators in financial contexts, in which the existence of higher moments is still an open question. Copyright © 2014 John Wiley & Sons, Ltd  相似文献   

    

Adelchi Azzalini  Giuliana Regoli 《Stat》2014,3(1):56-67

Substantial work has been dedicated in recent years to the construction of families of continuous distributions obtained by applying a modulation factor to a base symmetric density so as to obtain non‐symmetric variant forms, often denoted skew‐symmetric distributions. All this development has dealt with the case of continuous variables, while here we extend the formulation to the discrete case; moreover, some of the statements are of general validity. The results are illustrated with an application to the distribution of the score difference in sport matches. Copyright © 2014 John Wiley & Sons, Ltd  相似文献   

    

Huixia Judy Wang  Lan Wang 《Stat》2014,3(1):31-47

Length‐biased time‐to‐event data commonly arise in epidemiological cohort studies and cross‐sectional surveys. Ignoring length‐biased sampling often leads to severe bias in estimating the survival time in the general population. We propose a flexible quantile regression framework for analysing the covariate effects on the population survival time under both length‐biased sampling and random censoring. This framework allows for easy interpretation of the statistical model. Furthermore, it allows the covariates to have different impacts at different tails of the survival distribution and thus is able to capture important population heterogeneity. Using an unbiased estimating equation approach, we develop a new estimator that allows the censoring variable to depend on covariates in a non‐parametric way. We establish the consistency and asymptotic normality for the proposed estimator. A lack‐of‐fit test is proposed for diagnosing the adequacy of the population quantile regression model. The finite sample performance of the proposed methods is assessed through a simulation study. We demonstrate that the proposed method is effective in discovering interesting covariate effects by analysing the Canadian Study of Health and Aging dementia data. Copyright © 2014 John Wiley & Sons Ltd  相似文献   

    

Yuan Geng  Wenbin Lu  Hao Helen Zhang 《Stat》2014,3(1):337-350

Risk classification and survival probability prediction are two major goals in survival data analysis because they play an important role in patients' risk stratification, long‐term diagnosis, and treatment selection. In this article, we propose a new model‐free machine learning framework for risk classification and survival probability prediction based on weighted support vector machines. The new procedure does not require any specific parametric or semiparametric model assumption on data and is therefore capable of capturing non‐linear covariate effects. We use numerous simulation examples to demonstrate finite sample performance of the proposed method under various settings. Applications to a glioma tumour data and a breast cancer gene‐expression survival data are shown to illustrate the new methodology in real data analysis. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

    

Avideh Sabeti  Mian Wei  Radu V. Craiu 《Stat》2014,3(1):300-312

Conditional copulas are flexible statistical tools that couple joint conditional and marginal conditional distributions. In a linear regression setting with more than one covariate and two dependent outcomes, we consider additive models for studying the dependence between covariates and the copula parameter. We examine the computation and model selection tools needed for Bayesian inference. The method is illustrated using simulations and a real example. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

    

Guy Feldman  Anindya Bhadra  Sergey Kirshner 《Stat》2014,3(1):258-272

We consider the problem of feature selection in a high‐dimensional multiple predictors, multiple responses regression setting. Assuming that regression errors are i.i.d. when they are in fact dependent leads to inconsistent and inefficient feature estimates. We relax the i.i.d. assumption by allowing the errors to exhibit a tree‐structured dependence. This allows a Bayesian problem formulation with the error dependence structure treated as an auxiliary variable that can be integrated out analytically with the help of the matrix‐tree theorem. Mixing over trees results in a flexible technique for modelling the graphical structure for the regression errors. Furthermore, the analytic integration results in a collapsed Gibbs sampler for feature selection that is computationally efficient. Our approach offers significant performance gains over the competing methods in simulations, especially when the features themselves are correlated. In addition to comprehensive simulation studies, we apply our method to a high‐dimensional breast cancer data set to identify markers significantly associated with the disease. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献