1,721,117 research outputs found
A Permutation Test for the Two Sample Design in Case of Clustered Data
No full textIn this work we propose a new two-sample test for clustered data. In order to test the null hypothesis of equality in distribution of the two underlying populations against an alternative of stochastic dominance, we extend the nonparametric combination-based permutation testing to such kind of data.
The solution is valid both for continuous and for ordered categorical responses and the proposed method is investigated and validated by a suitable simulation study. A real case study in the field of toxicology is finally presented
Multivariate approach for comparative evaluations of customer satisfaction with application to transport services
No full textIn problems related to evaluations of products or services (e.g. in customer satisfaction analysis) the main difficulties concern the synthesis of the information, which is necessary for the presence of several evaluators and many response variables (aspects under evaluation). In this paper the problem of determining and comparing the satisfaction of different groups of customers, in the presence of multivariate response variables and using the results of pairwise comparisons is addressed. Within the framework of group ranking methods and multicriteria decision making theory, a new approach, based on nonparametric techniques, for evaluating group satisfaction in a multivariate framework is proposed and the concept of Multivariate Relative Satisfaction is defined. An application to the evaluation of public transport services, like the railways service and the urban bus service, by students of the University of Ferrara (Italy) is also discussed
Testing for Heterogeneity with Categorical Data: Permutation Solution vs. Bootstrap Method
No full textIn this article the problem of comparing distributional heterogeneities for categorical variables is addressed. Specifically, the one-sided testing problem for heterogeneity comparisons is considered. For such a problem a bootstrap method is proposed and compared with a permutation method already present in literature. The power behavior of the two methods is compared through a Monte Carlo simulation study. The results of two real applications are shown
Habits and behaviors of students at lunch and factors affecting student’s satisfaction for the canteen service: the case of University of Ferrara
No full textIn 2012 a survey on the quality of life of students of University of Ferrara inspired to Eurostudent was performed by the Center for Modeling, Computation and Simulation
(CMCS) of this University. The questionnaire was designed according to a division into several sections related to different aspects considered crucial for an exhaustive knowledge of students’ behaviors and habits.
The present paper focus on habits and behaviors of students at lunch, their level of use and satisfaction for some aspects of the canteen service of the University. Some descriptive statistics and an inferential analysis aimed at identifying the main factors
affecting the students’ satisfaction for the canteen service through the application of the ANOVA method are considered
Indagine quantitativa su opinioni, interessi e aspettative sulla città dei turisti e visitatori di Ferrara
No full textLo studio analizza i flussi turistici nella città di Ferrara, analizza la composizione delle presenze turistiche, il gradimento per i principali eventi turistici e la domanda di servizi legati ad iniziative turistiche, cercando inoltre di individuare punti di forza e debolezza di Ferrara. I dati si basano su una indagine condotta nel 2013
Analisi statistica delle presenze turistiche a Ferrara e relazione con i principali eventi nel triennio 2010-2012
No full textStudio delle presenze turistiche a Ferrara nel triennio 2010-2012 e analisi degli effetti dei principali eventi sulle presenze giornaliere
Nonparametric inference via permutation tests for Cub models
No full textAbstract In statistical surveys, respondents are often asked to express evaluations on several topics. The rating problem can be often faced in many fields. A new approach is represented by a class of mixture models with covariates (CUB models).
Together with parametric inference, a permutation solution to test for covariates effects, when an univariate response is considered, has been discussed in [1], where the preference for a permutation test as compared to asymptotic ones when the sample size is moderate or even small has been justified through a simulation study. We propose an extension of this nonparametric inference to deal with the multivariate case. The method is applied to a real data set
Nonparametric Test for Logistic Regression with Application to Italian Enterprises’ Propensity for Innovation
No full textIn this work, a nonparametric method is proposed to jointly test the significance of the regression coefficient estimates in a logistic regression model and identify which explanatory variables are effective in predicting the binary response. The motivating example is related to the factors affecting the propensity of Italian Small Medium Enterprises (SMEs) to innovate. The explanatory variables of the model represent firms’ characteristics, such as size and age, and the possible effect of the sector of economic activity is taken into account by including a set of binary variables as control factors. The dependent variable indicates whether a company, in the period under study, introduced at least one product or process innovation. Therefore, it is also dichotomous, and the logistic regression model is appropriate for representing the relationship between explanatory variables and dependent variable. Specifically, the logit transformation of the firm’s propensity to innovate, i.e., the probability that a company randomly chosen from the population of Italian SMEs has introduced an innovation or, equivalently, the proportion of innovative companies among the Italian SMEs, is expressed as a linear function of the predictors (explanatory and control variables). The proposed test is based on the permutation approach and satisfies important statistical properties, proved in a simulation study. The test is more flexible and robust than the classic parametric approach, and is preferable to typical stepwise regression procedures for the selection of a parsimonious and effective model
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