26,025 research outputs found
Caveats for using statistical significance tests in research assessments
This paper raises concerns about the advantages of using statistical
significance tests in research assessments as has recently been suggested in
the debate about proper normalization procedures for citation indicators.
Statistical significance tests are highly controversial and numerous criticisms
have been leveled against their use. Based on examples from articles by
proponents of the use statistical significance tests in research assessments,
we address some of the numerous problems with such tests. The issues
specifically discussed are the ritual practice of such tests, their dichotomous
application in decision making, the difference between statistical and
substantive significance, the implausibility of most null hypotheses, the
crucial assumption of randomness, as well as the utility of standard errors and
confidence intervals for inferential purposes. We argue that applying
statistical significance tests and mechanically adhering to their results is
highly problematic and detrimental to critical thinking. We claim that the use
of such tests do not provide any advantages in relation to citation indicators,
interpretations of them, or the decision making processes based upon them. On
the contrary their use may be harmful. Like many other critics, we generally
believe that statistical significance tests are over- and misused in the social
sciences including scientometrics and we encourage a reform on these matters.Comment: Accepted version for Journal of Informetric
Modeling interest rate dynamics: an infinite-dimensional approach
We present a family of models for the term structure of interest rates which
describe the interest rate curve as a stochastic process in a Hilbert space. We
start by decomposing the deformations of the term structure into the variations
of the short rate, the long rate and the fluctuations of the curve around its
average shape. This fluctuation is then described as a solution of a stochastic
evolution equation in an infinite dimensional space. In the case where
deformations are local in maturity, this equation reduces to a stochastic PDE,
of which we give the simplest example. We discuss the properties of the
solutions and show that they capture in a parsimonious manner the essential
features of yield curve dynamics: imperfect correlation between maturities,
mean reversion of interest rates and the structure of principal components of
term structure deformations. Finally, we discuss calibration issues and show
that the model parameters have a natural interpretation in terms of empirically
observed quantities.Comment: Keywords: interest rates, stochastic PDE, term structure models,
stochastic processes in Hilbert space. Other related works may be retrieved
on http://www.eleves.ens.fr:8080/home/cont/papers.htm
On the statistical description of the inbound air traffic over Heathrow airport
We present a model to describe the inbound air traffic over a congested hub.
We show that this model gives a very accurate description of the traffic by the
comparison of our theoretical distribution of the queue with the actual
distribution observed over Heathrow airport. We discuss also the robustness of
our model
Weighting Ripley’s K-function to account for the firm dimension in the analysis of spatial concentration
The spatial concentration of firms has long been a central issue in economics both under the theoretical and the applied point of view due mainly to the important policy implications. A popular approach to its measurement, which does not suffer from the problem of the arbitrariness of the regional boundaries, makes use of micro data and looks at the firms as if they were dimensionless points distributed in the economic space. However in practical circumstances the points (firms) observed in the economic space are far from being dimensionless and are conversely characterized by different dimension in terms of the number of employees, the product, the capital and so on. In the literature, the works that originally introduce such an approach (e.g. Arbia and Espa, 1996; Marcon and Puech, 2003) disregard the aspect of the different firm dimension and ignore the fact that a high degree of spatial concentration may result from both the case of many small points clustering in definite portions of space and from only few large points clustering together (e.g. few large firms). We refer to this phenomena as to clustering of firms and clustering of economic activities. The present paper aims at tackling this problem by adapting the popular Kfunction (Ripley, 1977) to account for the point dimension using the framework of marked point process theory (Penttinen, 2006)Agglomeration, Marked point processes, Spatial clusters, Spatial econometrics
- …