1,720,989 research outputs found
A unified approach to permutation testing for equivalence
Full text linkThe notion of testing for equivalence of two treatments is widely used in clinical trials, pharmaceutical experiments, bioequivalence and quality control. It is traditionally operated within the intersection–union principle (IU). According to this principle the null hypothesis is stated as the set of effects the differences δ of which lie outside a suitable equivalence interval and the alternative as the set of δ that lie inside it. In the literature related solutions are essentially based on likelihood techniques, which in turn are rather difficult to deal with. A recently published paper goes beyond most of likelihood limitations by using the IU approach within the permutation theory. One more paper, based on Roy’s union–intersection principle (UI) within the permutation theory, goes beyond some limitations of traditional two-sided tests. Such UI approach, effectively a mirror image of IU, assumes a null hypothesis where δ lies inside the equivalence interval and an alternative where it lies outside. Since testing for equivalence can rationally be analyzed by both principles but, as the two differ in terms of the mirror-like roles assigned to the hypotheses under study, they are not strictly comparable. The present paper’s main goal is to look into these problems and provide a sort of comparative analysis of both by highlighting the related requirements, properties, limitations, difficulties, and pitfalls so as to get practitioners properly acquainted with their correct use in practical contexts
Permutation testing for goodness-of-fit and stochastic ordering with multivariate mixed variables
No full textPermutation tests are highly versatile non-parametric procedures that can be used to address a wide set of statistical problems, without strict assumptions on data distribution. The Non-Parametric Combination (NPC) procedure has been proposed in the multivariate context to combine the results of several univariate permutation tests. This work demonstrates the flexibility and power of the procedure with a focus on Goodness-of-Fit and the comparison of C>2 different distributions, and includes the particular case of stochastic ordering problems. For each problem, we propose a different extension of the NPC procedure and suitable solutions for contexts in which the data are not solely continuous or ordinal, but also mixed. Additionally, the paper shows how these procedures can work with small total sample size n, even when n is lower than the number of variables V, and how a higher value of V has a positive effect on the power of the tests
Stratified two-sample design: A review on nonparametric methods
No full textIn this article, a comparison between the most promising nonparametric tests in a two-sample stratified design for practical uses is performed. We compared methods that exhibit good small-sample properties in order to be used with the most common stratum sizes. From the literature we identified as promising the following solutions: the aligned rank test, a small-sample approximation for the ANOVA-type statistic based on an unweighted average of all the distributions, and an asymptotic permutation distribution for the Wald-type statistic. We also developed a permutation version of the aligned rank test and another permutation testing procedure based on the Mann-Whitney statistic using the nonparametric combination procedure. All selected methods were compared by means of a simulation study. The results show that the aligned rank test and its permutation version perform better in most of the considered situations. Data from a genuine industrial problem were used for illustration purposes and to confirm the simulation results
Regression analysis with compositional data using orthogonal log-ratio coordinates
No full textCompositional data frequently arise when data refer to components which are proportions or fractions of a whole. Within the log-ratio approach, the analysis of compositional data can be conducted in terms of log-ratio transformations of components. These transformations make it possible to overcome the problem of the constant-sum constraint, making standard statistical methods applicable. In the present work, the log-ratio approach based on orthogonal log-ratio coordinates is adopted to show how it can lead to considerable improvements in the interpretation of the results of regression modeling with compositional data, both as explanatory or response variables. In order to demonstrate its practical usefulness, the methodology presented in this paper is applied to the analysis of air pollution produced by vehicles traveling through road intersections, with a specific focus on the effect of the type of traffic control (traffic signal vs. roundabout) on CO2 emissions
Association between magnetic resonance signs of temporomandibular joint effusion and disk displacement
No full textInterval selection: A case-study-based approach
No full textVariable selection plays a fundamental role in the analysis of data containing several variables which are redundant or irrelevant to the problem of interest. The ability to identify and discard these variables would make it possible to improve predictive performances and data interpretation, thus reducing costs and computational time. Although many methods have been proposed for feature selection, in some fields there is more interest in selecting groups of variables because of the continuous nature and covariance of adjacent data. This is the case for near-infrared spectroscopy, where several methods, mainly based on partial least squares regression, have been proposed to deal with interval selection. In this article, we consider some of these methods and propose an additional solution based on a variable clustering procedure (Cov/VSURF), Lasso regression and permutation tests. We compare their performances on four different public datasets and discuss the impact of interval selection on the predictive performances of the considered models
Temporomandibular joint osteoarthritis: an open label trial of 76 patients treated with arthrocentesis plus hyaluronic acid injections
No full textThis study is an open-label trial oil a sample of 76 consecutive patients with temporomandibular joint (TMJ) osteoarthritis treated with a cycle of five weekly arthrocenteses Plus hyaluronic acid injections. Patients had a diagnosis of osteoarthritis according to the Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD Axis I Group IIIb). They underwent a cycle of five arthrocenteses with injections (1 per week) of 1 ml hyaluronic acid and four follow-up assessments after the end of the treatment (at I week, I month, 3 months, 6 months). At cacti appointment, several subjective and objective Outcome variables were assessed to test the efficacy of the treatment protocol. Marked improvements were reported for all variables during the treatment phase. The improvements were maintained over the 6-month follow-up period. The p-value of the multivariate permutation test for the efficacy of the treatment over time (with Tippett's combination) was 0.001, and significant changes at the end of the follow-up period were detected for almost all the outcome variables. Data from this Study lend further Support to the usefulness of serial hyaluronic acid injections performed after arthrocentesis for the treatment of TMJ osteoarthritis and for the maintenance of improvements over a 6-month follow-up period
Design choice and machine learning model performances
Full text linkAn increasing number of publications present the joint application of design of experiments (DOE) and machine learning (ML) as a methodology to collect and analyze data on a specific industrial phenomenon. However, the literature shows that the choice of the design for data collection and model for data analysis is often not driven by statistical or algorithmic advantages, thus there is a lack of studies which provide guidelines on what designs and ML models to jointly use for data collection and analysis. This article discusses the choice of design in relation to the ML model performances. A study is conducted that considers 12 experimental designs, seven families of predictive models, seven test functions that emulate physical processes, and eight noise settings, both homoscedastic and heteroscedastic. The results of the research can have an immediate impact on the work of practitioners, providing guidelines for practical applications of DOE and ML
- …
