Rosa Arboretti

Union---intersection permutation solution for two-sample equivalence testing

One of the well-known problems with testing for sharp null hypotheses against two-sided alternatives is that, when sample sizes diverge, every consistent test rejects the null with a probability converging to one, even when it is true. This kind of problem emerges in practically all applications of traditional two-sided tests. The main purpose of the present paper is to overcome this very intriguing impasse by considering a general solution to the problem of testing for an equivalence null interval against a two one-sided alternative. Our goal is to go beyond the limitations of likelihood-based methods by working in a nonparametric permutation framework. This solution requires the nonparameteric Combination of dependent permutation tests, which is the methodological tool that achieves Roy’s Union–intersection principle. To obtain practical solutions, the related algorithm is presented. To appreciate its effectiveness for practical purposes, a simple example and some simulation results are also presented. In addition, for every pair of consistent partial test statistics it is proved that, if sample sizes diverge, when the effect lies in the open equivalence interval, the Rejection probability (RP) converges to zero. Analogously, if the effect lies outside that interval, the RP converges to one.

Statistical tests in medical research: traditional methods vs. multivariate npc permutation tests

Within medical research, a useful statistical tool is based on hypotheses testing in terms of the so-called null, that is the treatment has no effect, and alternative hypotheses, that is the treatment has some effects. By controlling the risks of wrong decisions, empirical data are used in order to possibly reject the null hypotheses in favour of the alternative, so that demonstrating the efficacy of a treatment of interest. The multivariate permutation tests, based on the nonparametric combination – NPC method, provide an innovative, robust and effective hypotheses testing solution to many real problems that are commonly encountered in medical research when multiple end-points are observed. This paper discusses the various approaches to hypothesis testing and the main advantages of NPC tests, which consist in the fact that they require much less stringent assumptions than traditional statistical tests. Moreover, the related results may be extended to the reference population even in case of selection-bias, that is non-random sampling. In this work, we review and discuss some basic testing procedures along with the theoretical and practical relevance of NPC tests showing their effectiveness in medical research. Within the non-parametric methods, NPC tests represent the current “frontier” of statistical research, but already widely available in the practice of analysis of clinical data.

Ranking of Multivariate Populations : A Permutation Approach with Applications

By reading, you can know the knowledge and things more, not only about what you get from people to people. Book will be more trusted. As this ranking of multivariate populations a permutation approach with applications, it will really give you the good idea to be successful. It is not only for you to be success in certain life you can be successful in everything. The success can be started by knowing the basic knowledge and do actions.

Cheese perception in the North American market: Empirical evidence for domestic vs. imported Parmesan

Purpose – The purpose of this paper is to detect market segments where consumers have a different knowledge of domestic and imported Parmesan cheese in USA and Canada. The results may be helpful in understanding to what extend North America consumers appreciate Parmesan cheese and brands, Parmesan consumption and price while recognizing market segments according to consumer awareness, involvement and covariate effects. Design/methodology/approach – A class of mixture models, known as combination uniform binomial (CUB), is applied to survey data collected in USA and Canada. A questionnaire, filled out by 540 restaurant customers, collects opinions about consumption, purchase features and price. The CUB model estimates the two latent variables, known as feeling and uncertainty, explaining the respondent’s behavior as awareness and involvement variability while the CUB clustering procedure detects market segments. Findings – CUB results show that the Parmesan is a well-known cheese but also that a small shar...

Nonparametric combination tests for comparing two survival curves with informative and non-informative censoring

This paper looks at permutation methods used to deal with hypothesis testing within the survival analysis framework. In the literature, several attempts have been made to deal with the comparison of survival curves and, depending on the survival and hazard functions of two groups, they can be more or less efficient in detecting differences. Furthermore, in some situations, censoring can be informative in that it depends on treatment effect. Our proposal is based on the nonparametric combination approach and has proven to be very effective under different configurations of survival and hazard functions. It allows the practitioner to test jointly on primary and censoring events and, by using multiple testing methods, to assess the significance of the treatment effect separately on the survival and the censoring process.

Testing for Equivalence: An Intersection-Union Permutation Solution

ABSTRACT 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 the present article is to go beyond most of the limitations of likelihood-based methods, that is, 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.

Ranking Multivariate Populations

The need to define an appropriate ranking of several populations of interest, i.e. processes, products, and so on is very common within many areas of applied research such as Food Science, Chemistry, Engineering, Biomedicine, etc.

Customer Satisfaction Heterogeneity

The measurement of the customer satisfaction concerns the gap between the customer expectations about the product or service and the perceptions of the customer after the consumption or use. In other words, the customer satisfaction is closely related to the concept of “perceived quality”. According to the definition of Montgomery [24], it depends on how much the products or services meet the requirements of the consumers/users and it is directly connected to the homogeneity of the performance of the production process or service provision process.

The CUB Models

The CUB model [12], where CUB stands for Combination of a discrete Uniform and a shifted Binomial distributions assumes the involvement of two latent variables during an evaluation process, that have been called feeling and uncertainty. In order to justify the names for latent variables, consider the way you choose an evaluation grade from a set of 9. The final choice reflects your feeling about the evaluated item, your past experience, your knowledge about it, and so on. On the other hand, there are some aspects concern with a basic uncertainty about the evaluated item, for example you are asked to deal with it for the first time and you don’t know what grade to choose, maybe the task is too difficult or the task is annoying you. These two main components are supposed to move your final choice and they are supposed to follow respectively a shifted Binomial distribution and a Uniform distribution [12, 18].

Composite Indicators and Satisfaction Profiles

Evaluating the satisfaction about public services, organizations or products is very important in order to have a measure of their efficiency and effectiveness.