10,610 research outputs found
Multivariate small sample tests for two-way designs with applications to industrial statistics
In this paper, we present a novel nonparametric approach for multivariate analysis of two-way crossed factorial design based on NonParametric Combination applied to Synchronized Permutation tests. This nonparametric hypothesis testing procedure not only allows to overcome the shortcomings of MANOVA test like violation of assumptions such as multivariate normality or covariance homogeneity, but, in an extensive simulation study, reveals to be a powerful instrument both in case of small sample size and many response variables. We contextualize its application in the field of industrial experiments and we assume a linear additive model for the data set analysis. Indeed, the linear additive model interpretation well adapts to the industrial production environment because of the way control of production machineries is implemented. The case of small sample size reflects the frequent needs of practitioners in the industrial environment where there are constraints or limited resources for the experimental design. Furthermore, an increase in rejection rate can be observed under alternative hypothesis when the number of response variables increases with fixed number of observed units. This could lead to a strategical benefit considering that in many real problems it could be easier to collect more information on a single experimental unit than adding a new unit to the experimental design. An application to industrial thermoforming processes is useful to illustrate and highlight the benefits of the adoption of the herein presented nonparametric approach
Testing for equivalence: an intersection-union permutation solution
The notion of testing for equivalence of two treatments is widely used in
clinical trials, pharmaceutical experiments,bioequivalence and quality control.
It is essentially approached within the intersection-union (IU) principle.
According to this principle the null hypothesis is stated as the set of effects
lying outside a suitably established interval and the alternative as the set of
effects lying inside that interval. The solutions provided in the literature
are mostly based on likelihood techniques, which in turn are rather difficult
to handle, except for cases lying within the regular exponential family and the
invariance principle. The main goal of present paper is to go beyond most of
the limitations of likelihood based methods, i.e. to work in a nonparametric
setting within the permutation frame. To obtain practical solutions, a new IU
permutation test is presented and discussed. A simple simulation study for
evaluating its main properties, and three application examples are also
presented.Comment: 21 pages, 2 figure
A Distance-Based Test of Association Between Paired Heterogeneous Genomic Data
Due to rapid technological advances, a wide range of different measurements
can be obtained from a given biological sample including single nucleotide
polymorphisms, copy number variation, gene expression levels, DNA methylation
and proteomic profiles. Each of these distinct measurements provides the means
to characterize a certain aspect of biological diversity, and a fundamental
problem of broad interest concerns the discovery of shared patterns of
variation across different data types. Such data types are heterogeneous in the
sense that they represent measurements taken at very different scales or
described by very different data structures. We propose a distance-based
statistical test, the generalized RV (GRV) test, to assess whether there is a
common and non-random pattern of variability between paired biological
measurements obtained from the same random sample. The measurements enter the
test through distance measures which can be chosen to capture particular
aspects of the data. An approximate null distribution is proposed to compute
p-values in closed-form and without the need to perform costly Monte Carlo
permutation procedures. Compared to the classical Mantel test for association
between distance matrices, the GRV test has been found to be more powerful in a
number of simulation settings. We also report on an application of the GRV test
to detect biological pathways in which genetic variability is associated to
variation in gene expression levels in ovarian cancer samples, and present
results obtained from two independent cohorts
Adaptive Mantel Test for AssociationTesting in Imaging Genetics Data
Mantel's test (MT) for association is conducted by testing the linear
relationship of similarity of all pairs of subjects between two observational
domains. Motivated by applications to neuroimaging and genetics data, and
following the succes of shrinkage and kernel methods for prediction with
high-dimensional data, we here introduce the adaptive Mantel test as an
extension of the MT. By utilizing kernels and penalized similarity measures,
the adaptive Mantel test is able to achieve higher statistical power relative
to the classical MT in many settings. Furthermore, the adaptive Mantel test is
designed to simultaneously test over multiple similarity measures such that the
correct type I error rate under the null hypothesis is maintained without the
need to directly adjust the significance threshold for multiple testing. The
performance of the adaptive Mantel test is evaluated on simulated data, and is
used to investigate associations between genetics markers related to
Alzheimer's Disease and heatlhy brain physiology with data from a working
memory study of 350 college students from Beijing Normal University
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