The Experts below are selected from a list of 118503 Experts worldwide ranked by ideXlab platform
Thibaut Jombart - One of the best experts on this subject based on the ideXlab platform.
-
an eigenvalue Test for spatial principal component analysis
BMC Bioinformatics, 2017Co-Authors: Valeria Montano, Thibaut JombartAbstract:Background The spatial Principal Component Analysis (sPCA, Jombart (Heredity 101:92-103, 2008) is designed to investigate non-random spatial distributions of genetic variation. Unfortunately, the associated Tests used for assessing the existence of spatial patterns (global and local Test; (Heredity 101:92-103, 2008) lack statistical power and may fail to reveal existing spatial patterns. Here, we present a non-Parametric Test for the significance of specific patterns recovered by sPCA.
-
an eigenvalue Test for spatial principal component analysis
bioRxiv, 2017Co-Authors: Valeria Montano, Thibaut JombartAbstract:The spatial Principal Component Analysis (sPCA, Jombart 2008) is designed to investigate non-random spatial distributions of genetic variation. Unfortunately, the associated Tests used for assessing the existence of spatial patterns (global and local Test; Jombart et al. 2008) lack statistical power and may fail to reveal existing spatial patterns. Here, we present a non-Parametric Test for the significance of specific patterns recovered by sPCA. We compared the performance of this new Test to the original global and local Tests using datasets simulated under classical population genetic models. Results show that our Test outperforms the original global and local Tests, exhibiting improved statistical power while retaining similar, and reliable type I errors. Moreover, by allowing to Test various sets of axes, it can be used to guide the selection of retained sPCA components. As such, it represents a valuable complement to the original analysis, and should prove useful for the investigation of spatial genetic patterns.
Valeria Montano - One of the best experts on this subject based on the ideXlab platform.
-
an eigenvalue Test for spatial principal component analysis
BMC Bioinformatics, 2017Co-Authors: Valeria Montano, Thibaut JombartAbstract:Background The spatial Principal Component Analysis (sPCA, Jombart (Heredity 101:92-103, 2008) is designed to investigate non-random spatial distributions of genetic variation. Unfortunately, the associated Tests used for assessing the existence of spatial patterns (global and local Test; (Heredity 101:92-103, 2008) lack statistical power and may fail to reveal existing spatial patterns. Here, we present a non-Parametric Test for the significance of specific patterns recovered by sPCA.
-
an eigenvalue Test for spatial principal component analysis
bioRxiv, 2017Co-Authors: Valeria Montano, Thibaut JombartAbstract:The spatial Principal Component Analysis (sPCA, Jombart 2008) is designed to investigate non-random spatial distributions of genetic variation. Unfortunately, the associated Tests used for assessing the existence of spatial patterns (global and local Test; Jombart et al. 2008) lack statistical power and may fail to reveal existing spatial patterns. Here, we present a non-Parametric Test for the significance of specific patterns recovered by sPCA. We compared the performance of this new Test to the original global and local Tests using datasets simulated under classical population genetic models. Results show that our Test outperforms the original global and local Tests, exhibiting improved statistical power while retaining similar, and reliable type I errors. Moreover, by allowing to Test various sets of axes, it can be used to guide the selection of retained sPCA components. As such, it represents a valuable complement to the original analysis, and should prove useful for the investigation of spatial genetic patterns.
Patricia Reynaud-bouret - One of the best experts on this subject based on the ideXlab platform.
-
Bootstrap and permutation Tests of independence for point processes
Annals of Statistics, 2015Co-Authors: Mélisande Albert, Yann Bouret, Magalie Fromont, Patricia Reynaud-bouretAbstract:Motivated by a neuroscience question about synchrony detection in spike train analysis, we deal with the independence Testing problem for point processes. We introduce non-Parametric Test statistics, which are rescaled general $U$-statistics, whose corresponding critical values are constructed from bootstrap and randomization/permutation approaches, making as few assumptions as possible on the underlying distribution of the point processes. We derive general consistency results for the bootstrap and for the permutation w.r.t. to Wasserstein's metric, which induce weak convergence as well as convergence of second order moments. The obtained bootstrap or permutation independence Tests are thus proved to be asymptotically of the prescribed size, and to be consistent against any reasonable alternative. A simulation study is performed to illustrate the derived theoretical results, and to compare the performance of our new Tests with existing ones in the neuroscientific literature.
Eric Strobl - One of the best experts on this subject based on the ideXlab platform.
-
exports international investment and plant performance evidence from a non Parametric Test
2004Co-Authors: Sourafel Girma, Holger Gorg, Eric StroblAbstract:This paper compares the performance of purely domestic plants, domestic exporters and domestic multinationals. For our empirical analysis we utilise a non-Parametric approach based on the principle of first order stochastic dominance. Comparing the cumulative distributions of the measures of plant performance across the three types of plants we find that the distributions for multinationals dominate that of domestic exporters and non-exporters, while we do not find clear differences in plant performance between domestic exporters and non-exporters, although the latter finding may be due to the lack of many very small plants in our data set.
-
exports international investment and plant performance evidence from a non Parametric Test
2003Co-Authors: Sourafel Girma, Holger Gorg, Eric StroblAbstract:This paper compares the performance of purely domestic plants, domestic exporters and domestic multinationals. For our empirical analysis we utilise a non-Parametric approach based on the principle of first order stochastic dominance. We find that the distributions for multinationals dominate that of domestic exporters and non-exporters, while we do not find clear differences in plant performance between domestic exporters and non-exporters.
P N Suganthan - One of the best experts on this subject based on the ideXlab platform.
-
a novel fuzzy and multiobjective evolutionary algorithm based gene assignment for clustering short time series expression data
Congress on Evolutionary Computation, 2007Co-Authors: Ashish Anand, P N SuganthanAbstract:Conventional clustering algorithms based on Euclidean distance or Pearson correlation coefficient are not able to include order information in the distance metric and also unable to distinguish between random and real biological patterns. We present template based clustering algorithm for time series gene expression data. Template profiles are defined based on up-down regulation of genes between consecutive time points. Assignment of genes to templates is based on fuzzy membership function. Multi-objective evolutionary algorithm is used to determine compact clusters with varying number of templates. Statistical significance of each template is determined using permutation based non-Parametric Test. Statistically significant profiles are further Tested for their biological relevance using gene ontology analysis. The algorithm was able to distinguish between real and noisy pattern when Tested on artificial and real biological data. The proposed algorithm has shown better or similar performance compared to STEM and better than k-means on a real biological data.