The Experts below are selected from a list of 282 Experts worldwide ranked by ideXlab platform
Long Feng - One of the best experts on this subject based on the ideXlab platform.
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an inverse norm sign test of Location parameter for high dimensional data
Journal of Business & Economic Statistics, 2020Co-Authors: Long FengAbstract:We consider the one Sample Location testing problem for high-dimensional data, where the data dimension is potentially much larger than the Sample size. We devise a novel inverse norm sign test (IN...
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A high-dimensional spatial rank test for two-Sample Location problems
Computational Statistics & Data Analysis, 2020Co-Authors: Long Feng, Xiaoxu Zhang, Binghui LiuAbstract:Abstract In high-dimensional situations, the traditional multivariate sign- or rank-based procedures for the two-Sample Location testing problems are ineffective, since in the construction of the test statistics, the scatter matrix to be inverted is singular. To solve this problem, many high-dimensional spatial sign or rank tests have been proposed, some of which are very efficient. However, most of these existing tests no longer work in very high dimensional situations, which only allows the dimension of variables to be the square of the Sample sizes at most, hence are restrictive for practical applications. On this ground, a new high-dimensional spatial rank test is proposed in this paper, which is invariant under scalar transformations, maintains the efficiency advantage of spatial-rank-based testing methods, and could even allow the dimension to grow almost exponentially with the Sample sizes. The theoretical results of the proposed test are established, followed by some convincing numerical results and two real data analyses.
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Multivariate-Sign-Based High-Dimensional Tests for the Two-Sample Location Problem
Journal of the American Statistical Association, 2016Co-Authors: Long Feng, Changliang Zou, Zhaojun WangAbstract:ABSTRACTThis article concerns tests for the two-Sample Location problem when data dimension is larger than the Sample size. Existing multivariate-sign-based procedures are not robust against high dimensionality, producing tests with Type I error rates far away from nominal levels. This is mainly due to the biases from estimating Location parameters. We propose a novel test to overcome this issue by using the “leave-one-out” idea. The proposed test statistic is scalar-invariant and thus is particularly useful when different components have different scales in high-dimensional data. Asymptotic properties of the test statistic are studied. Compared with other existing approaches, simulation studies show that the proposed method behaves well in terms of sizes and power. Supplementary materials for this article are available online.
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High Dimensional Spatial Rank Test for Two-Sample Location Problem
arXiv: Methodology, 2015Co-Authors: Long FengAbstract:This article concerns tests for the two-Sample Location problem when the dimension is larger than the Sample size. The traditional multivariate-rank-based procedures cannot be used in high dimensional settings because the Sample scatter matrix is not available. We propose a novel high-dimensional spatial rank test in this article. The asymptotic normality is established. We can allow the dimension being almost the exponential rate of the Sample sizes. Simulations demonstrate that it is very robust and efficient in a wide range of distributions.
Herbert Büning - One of the best experts on this subject based on the ideXlab platform.
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Power of One-Sample Location Tests Under Distributions with Equal Lévy Distance
Communications in Statistics - Simulation and Computation, 2006Co-Authors: Herbert Büning, Salmai QariAbstract:In this article, we study the power of one-Sample Location tests under classical distributions and two supermodels which include the normal distribution as a special case. The distributions of the supermodels are chosen in such a way that they have equal distance to the normal as the logistic, uniform, double exponential, and the Cauchy, respectively. As a measure of distance we use the Levy metric. The tests considered are two parametric tests, the t-test and a trimmed t-test, and two nonparametric tests, the sign test and the Wilcoxon signed-rank tests. It turns out that the power of the tests, first of all, does not depend on the Levy distance but on the special chosen supermodel.
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Maximum Test versus Adaptive Tests for the Two-Sample Location Problem
Journal of Applied Statistics, 2004Co-Authors: Markus Neuhäuser, Herbert Büning, Ludwig A. HothornAbstract:For the non-parametric two-Sample Location problem, adaptive tests based on a selector statistic are compared with a maximum and a sum test, respectively. When the class of all continuous distributions is not restricted, the sum test is not a robust test, i.e. it does not have a relatively high power across the different possible distributions. However, according to our simulation results, the adaptive tests as well as the maximum test are robust. For a small Sample size, the maximum test is preferable, whereas for a large Sample size the comparison between the adaptive tests and the maximum test does not show a clear winner. Consequently, one may argue in favour of the maximum test since it is a useful test for all Sample sizes. Furthermore, it does not need a selector and the specification of which test is to be performed for which values of the selector. When the family of possible distributions is restricted, the maximin efficiency robust test may be a further robust alternative. However, for the fami...
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Adaptive bootstrap tests and their competitors in the c -Sample Location problem
Journal of Statistical Computation and Simulation, 2003Co-Authors: Herbert Büning, Michael RietzAbstract:This paper deals with a power comparison of different types of tests, parametric, nonparametric, robustified and adaptive ones for the two-sided c -Sample Location problem. A robustness study on level f in the case of heteroscedasticity and non-normal distributions is included in our study, too. First of all, we consider an adaptive test based on Hogg's concept and two adaptive Bootstrap tests using Hogg's principle. It turns out that the adaptive Hogg-test is the best one in the case of homoscedasticity but for heteroscedasticity, an adaptive Bootstrap test using Hogg's principle is preferable.
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Robustness and power of parametric, nonparametric, robustified and adaptive tests—The multi-Sample Location problem
Statistical Papers, 2000Co-Authors: Herbert BüningAbstract:This paper deals with a survey of different types of tests, parametric, nonparametric, robustified and adaptive ones, and with an application to the two-sided c-Sample Location problem. Some concepts of robustness are discussed, such as breakdown point, influence function, gross-error sensitivity and especially α- and β-robustness. A robustness study on level α in the case of heteroscedasticity and nonnormal distributions is carried out via Monte Carlo methods and also a power comparison of all the tests considered. It turns out that robustified versions of the F-test and Welch-test where the original observations are replaced by its ranks behave well over a broad class of distributions, symmetric ones with different tail weight and asymmetric ones, but, on the whole, an adaptive test is to prefer.
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An adaptive two-Sample Location-scale test of lepage type for symmetric distributions
Journal of Statistical Computation and Simulation, 2000Co-Authors: Herbert Büning, Thorsten ThadewaldAbstract:For the two-Sample Location and scale problem we propose an adaptive test which is based on so called Lepage type tests. The well known test of Lepage (1971) is a combination of the Wilcoxon test for Location alternatives and the Ansari-Bradley test for scale alternatives and it behaves well for symmetric and medium-tailed distributions. For the cae of short-, medium- and long-tailed distributions we replace the Wilcoxon test and the .Ansari-Bradley test by suitable other two-Sample tests for Location and scale, respectively, in oder to get higher power than the classical Lepage test for such distribotions. These tests here are called Lepage type tests. in practice, however, we generally have no clear idea about the distribution having generated our data. Thus, an adaptive test should be applied which takes the the given data set inio consideration. The proposed adaptive test is based on the concept of Hogg (1974), i.e., first, to classify the unknown symmetric distribution function with respect to a meas...
Wolfgang Kössler - One of the best experts on this subject based on the ideXlab platform.
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max type rank tests u tests and adaptive tests for the two Sample Location problem an asymptotic power study
Computational Statistics & Data Analysis, 2010Co-Authors: Wolfgang KösslerAbstract:For the two-Sample Location problem, two types of tests are considered, linear rank tests with various scores, but also some tests based on U-statistics. For both types adaptive tests as well as max-type tests are constructed and their asymptotic and finite power properties are investigated. It turns out that both the adaptive tests have a larger asymptotic power than the max-type tests. For small Sample sizes, however, some of the max-type tests are preferable. U-statistics are convenient if extreme densities may occur.
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Power of Some Tests for Umbrella Alternatives in the Multi‐Sample Location Problem
Biometrical Journal, 1997Co-Authors: Herbert Büning, Wolfgang KösslerAbstract:For the c-Sample Location problem with umbrella alternatives we present a modification of a test proposed by BARLOW et al. (1972) for trend alternatives. Furthermore, we study a rank version of this modified test following a proposal of CONOVER and IMAN (1981). These tests are compared with the test of MACK and WOLFE (1978) and with some so called Mack-Wolfe-type tests in which the Mann-Whitney statistics are replaced by other two-Sample linear rank statistics. The comparison is referred to the power of the test assuming a broad range of distributions. It will be shown firstly, that for nonnormal data there are tests with higher power than the modification of the test of Barlow et al. and secondly, that for a special distribution there is a Mack-Wolfe-type test which is more efficient than its competitors. We also consider the case of unknown peak.
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Robustness and efficiency of some tests for ordered alternatives in the C-Sample Location problem
Journal of Statistical Computation and Simulation, 1996Co-Authors: Herbert Büning, Wolfgang KösslerAbstract:For the c-Sample Location problem with ordered alternatives we compare a test proposed by Barlow et al. (1972) and its Welch modification for the case of unequal variances with some nonparametric counterparts, the Jonckheere test and modifications of the Jonckheere test. In these modifications the Mann-Whitney statistic is replaced by other two-Sample linear rank statistics. The comparison is referred to the actual level and the power of the tests and it is carried out via Monte Carlo simulation assuming short-, medium- and long-tailed as well as asymmetric distributions. It turns out that in the case of unequal variances and symmetric distributions the Welch modification of the test of Barlow et al is the most α-robust test among the tests considered, but for equal variances special Jonckheere-type tests are to be preferred.
Hannu Oja - One of the best experts on this subject based on the ideXlab platform.
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One-Sample problem: Spatial signed-rank test and Hodges-Lehmann estimate
Multivariate Nonparametric Methods with R, 2010Co-Authors: Hannu OjaAbstract:The spatial signed-rank score function Q(y) is used for the one-Sample Location problem. The test is then the spatial signed-rank test, and the estimate is the spatial Hodges-Lehmann estimate. The tests and estimates based on outer standardization as well as those based on inner standardization are again discussed.
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Analysis of cluster-correlated data
Multivariate Nonparametric Methods with R, 2010Co-Authors: Hannu OjaAbstract:In this chapter it is shown how the spatial sign and rank methods can be extended to cluster-correlated data. Tests and estimates for the one-Sample Location problemwith a general score function are given in detail. Then two-Sampleweighted spatial rank tests are considered.
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one Sample problem spatial sign test and spatial median
2010Co-Authors: Hannu OjaAbstract:The spatial sign score function U(y) is used for the one-Sample Location problem. The test is then the spatial sign test, and the estimate is the spatial median. The tests and estimates using outer standardization as well as those using inner standardization are discussed.
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one Sample Location tests for multilevel data
Les Cahiers du GERAD, 2006Co-Authors: Denis Larocque, Jaakko Nevalainen, Hannu OjaAbstract:Abstract In this paper, we consider testing the Location parameter with multilevel (or hierarchical) data. A general family of weighted test statistics is introduced. This family includes extensions to the case of multilevel data of familiar procedures like the t, the sign and the Wilcoxon signed-rank tests. Under mild assumptions, the test statistics have a null limiting normal distribution which facilitates their use. An investigation of the relative merits of selected members of the family of tests is achieved theoretically by deriving their asymptotic relative efficiency (ARE) and empirically via a simulation study. It is shown that the performance of a test depends on the clusters configurations and on the intracluster correlations. Explicit formulas for optimal weights and a discussion of the impact of omitting a level are provided for 2 and 3-level data. It is shown that using appropriate weights can greatly improve the performance of the tests. Finally, the use of the new tests is illustrated with a real data example.
Tieyun Qian - One of the best experts on this subject based on the ideXlab platform.
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Sample Location selection for efficient distance aware influence maximization in geo social networks
Database Systems for Advanced Applications, 2018Co-Authors: Ming Zhong, Qian Zeng, Yuanyuan Zhu, Tieyun QianAbstract:In geo-social networks, the distances of users to a Location play an important role in populating the business or campaign at the Location. Thereby, the problem of Distance-Aware Influence Maximization (DAIM) has been investigated recently. The efficiency of DAIM computation heavily relies on the Sample Location selection, because the online seeding performance is sensitive to the distance between Sample Location and promoted Location, and the offline precomputation performance is sensitive to the number of Samples. However, there is no work to fully study the problem of Sample Location selection w.r.t. DAIM in geo-social networks. To do this, we first formalize the problem under a reasonable assumption that a promoted Location always adheres to the distribution of users. Then, we propose an efficient Location sampling approach based on the heuristic anchor point selection and facility alLocation techniques. Our experimental results on two real datasets demonstrate that our approach can improve the online and offline efficiency of DAIM approach like [9] by orders of magnitude.
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DASFAA (1) - Sample Location selection for efficient distance-aware influence maximization in geo-social networks
Database Systems for Advanced Applications, 2018Co-Authors: Ming Zhong, Qian Zeng, Yuanyuan Zhu, Tieyun QianAbstract:In geo-social networks, the distances of users to a Location play an important role in populating the business or campaign at the Location. Thereby, the problem of Distance-Aware Influence Maximization (DAIM) has been investigated recently. The efficiency of DAIM computation heavily relies on the Sample Location selection, because the online seeding performance is sensitive to the distance between Sample Location and promoted Location, and the offline precomputation performance is sensitive to the number of Samples. However, there is no work to fully study the problem of Sample Location selection w.r.t. DAIM in geo-social networks. To do this, we first formalize the problem under a reasonable assumption that a promoted Location always adheres to the distribution of users. Then, we propose an efficient Location sampling approach based on the heuristic anchor point selection and facility alLocation techniques. Our experimental results on two real datasets demonstrate that our approach can improve the online and offline efficiency of DAIM approach like [9] by orders of magnitude.
