For scientists, for students or for the public? : the shifting roles of natural history museums

This article aims to discuss the main roles of natural history museums and to show how these purposes have evolved and adapted throughout the museums’ history, as a response to the development of natural sciences and societal change, from their creation in the 18th century to the present. It strives to demonstrate how the balance between research, teaching and disseminating knowledge to the public has successively shifted, without ever forsaking any of these functions. It is focused on Portuguese museums, but examining their place within international trends

Climate scientists and the public: interactions and knowledge exchanges

Raising public awareness of climate change is crucial for transforming individual behaviours and amassing support to policy measures, which may threaten prosperity and comfort levels that came to be expected in affluent societies. Scientists are one of several agents involved in public communication of climate chang

New contention resolution schemes for WiMAX

Abstract—The use of Broadband Wireless Access (BWA) technology is increasing due to the use of Internet and multimedia applications with strict requirements of end–to–end delay and jitter, through wireless devices. The IEEE 802.16 standard, which defines the physical (PHY) and the medium access control (MAC) layers, is one of the BWA standards. Its MAC layer is centralized basis, where the Base Station (BS) is responsible for assigning the needed bandwidth for each Subscriber Station (SS), which requests bandwidth competing between all of them. The standard defines a contention resolution process to resolve the potential occurrence of collisions during the requesting process. In this paper, we propose to modify the contention resolution process to improve the network performance, including end–to–end delay and throughput

Random coefficient regressions: parametric goodness of fit tests

Random coefficient regression models have been applied in different fields during recent years and they are a unifying frame for many statistical models. Recently, Beran and Hall (1992) opened the question of the nonparametric study of the distribution of the coefficients. Nonparametric goodness of fit tests were considered in Delicado and Romo (1994.). In this paper we propose statistics for parametric goodness of fit tests and we obtain their asymptotic distributions. Moreover, we construct bootstrap approximations to these distributions, proving their validity. Finally, a simulation study illustrates our results

Goodness of fit tests in random coefficient regression models

Random coefficient regressions have been applied in a wide range of fields, from biology to economics, and constitute a common frame for several important statistical models. A nonparametric approach to inference in random coefficient models was initiated by Beran and Hall. In this paper we introduce and study goodness of fit tests for the coefficient distributions; their asymptotic behaviour under the null hypothesis is obtained. We also propose bootstrap resampling strategies to approach these distributions and prove their asymptotic validity using results by Gine and Zinn on bootstrap empirical processes. A simulation study illustrates the properties of these tests

Another look at principal curves and surfaces

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Principal curves have been defined as smooth curves passing through the “middle” of a multidimensional data set. They are nonlinear generalizations of the first principal component, a characterization of which is the basis of the definition of principal curves. We establish a new characterization of the first principal component and base our new definition of a principal curve on this property. We introduce the notion of principal oriented points and we prove the existence of principal curves passing through these points. We extend the definition of principal curves to multivariate data sets and propose an algorithm to find them. The new notions lead us to generalize the definition of total variance. Successive principal curves are recursively defined from this generalization. The new methods are illustrated on simulated and real data sets.Peer ReviewedPostprint (author's final draft

Bootstraping the general linear hypothesis test

We discuss the use of bootstrap methodology in hypothesis testing, focusing on the classical F-test for linear hypotheses in the linear model. A modification of the F-statistics which allows for resampling under the null hypothesis is proposed. This approach is specifically considered in the one-way analysis of variance model. A simulation study illustrating the behaviour of our proposal is presented

Analysing musical performance through functional data analysis: rhythmic structure in Schumann's Träumerei

Functional data analysis (FDA) is a relatively new branch of statistics devoted to describing and modelling data that are complete functions. Many relevant aspects of musical performance and perception can be understood and quantified as dynamic processes evolving as functions of time. In this paper, we show that FDA is a statistical methodology well suited for research into the field of quantitative musical performance analysis. To demonstrate this suitability, we consider tempo data for 28 performances of Schumann's Träumerei and analyse them by means of functional principal component analysis (one of the most powerful descriptive tools included in FDA). Specifically, we investigate the commonalities and differences between different performances regarding (expressive) timing, and we cluster similar performances together. We conclude that musical data considered as functional data reveal performance structures that might otherwise go unnoticed.Peer ReviewedPostprint (author's final draft

Measuring non-linear dependence for two random variables distributed along a curve

The final publication is available at link.springer.comWe propose new dependence measures for two real random variables not necessarily linearly related. Covariance and linear correlation are expressed in terms of principal components and are generalized for variables distributed along a curve. Properties of these measures are discussed. The new measures are estimated using principal curves and are computed for simulated and real data sets. Finally, we present several statistical applications for the new dependence measures.Peer ReviewedPostprint (author's final draft