Benvenuti nell'Anagrafe della Ricerca d'Ateneo
La valutazione delle attività di ricerca che si svolgono nelle università ha assunto un'importanza rilevante sia per l'intero sistema universitario sia all'interno di ogni singolo ateneo. Roma Tre si è quindi dotata di idonei strumenti informativi in grado di supportare al meglio le azioni di monitoraggio e di valutazione delle attività di ricerca svolte nei propri dipartimentied ha realizzato una Anagrafe della Ricerca di Ateneo.
EnglishThe classical chi-square test of goodness of fit compares the hypothesis that data arise from some parametric family of distributions, against the nonparametric alternative that they arise from some other distribution. However, the chi-square test requires continuous data to be grouped into arbitrary categories, and, being based upon an approximation, it can only be used if there is sufficient data. In many practical situations these requirements are wasteful of information and overly restrictive respectively. We explore the use of the fractional Bayes factor to obtain a Bayesian alternative to the chi-square test when no specific prior information is available, including consideration of the extent to which it can handle small data sets and continuous data without arbitrary grouping. -
CATERINA CONIGLIANI, . CASTRO, & A. OHAGAN (2000). Bayesian assessment of goodness of fit against nonparametric alternatives. CANADIAN JOURNAL OF STATISTICS, 28, 327-342.
| Titolo: | Bayesian assessment of goodness of fit against nonparametric alternatives | |
| Autori: | ||
| Data di pubblicazione: | 2000 | |
| Rivista: | ||
| Citazione: | CATERINA CONIGLIANI, . CASTRO, & A. OHAGAN (2000). Bayesian assessment of goodness of fit against nonparametric alternatives. CANADIAN JOURNAL OF STATISTICS, 28, 327-342. | |
| Abstract: | The classical chi-square test of goodness of fit compares the hypothesis that data arise from some parametric family of distributions, against the nonparametric alternative that they arise from some other distribution. However, the chi-square test requires continuous data to be grouped into arbitrary categories, and, being based upon an approximation, it can only be used if there is sufficient data. In many practical situations these requirements are wasteful of information and overly restrictive respectively. We explore the use of the fractional Bayes factor to obtain a Bayesian alternative to the chi-square test when no specific prior information is available, including consideration of the extent to which it can handle small data sets and continuous data without arbitrary grouping. - | |
| Handle: | http://hdl.handle.net/11590/114407 | |
| Appare nelle tipologie: | 1.1 Articolo in rivista |
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