Sat, 26 Jul 2008 16:59:14 BST CiteULike: bigbossman distribution http://www.citeulike.org/user/bigbossman/tag/distribution CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/bigbossman/article/2523915 <i>(18 Jul 2005)</i><br /><br />For a large class of statistical systems a geometric mean value of the observables is constrained. These observables are characterized by a power-law statistical distribution. On a geometric mean and power-law statistical distributions A Rostovtsev (18 Jul 2005) 2008-03-13T07:04:41-00:00 2005 distribution mean powerlaw http://www.citeulike.org/user/bigbossman/article/1956827 <i>Scientometrics, Vol. 70, No. 1. (15 January 2007), pp. 153-165.</i><br /><br />Abstract   Context. The use of citation frequency and impact factor as measures of research quality and journal prestige is being criticized. Citation frequency is augmented by self-citation and for most journals the majority of citations originate from a minority of papers. We hypothesized that citation frequency is also associated with the geographical origin of the research publication. Objective. We determined whether citations originate more frequently from institutes that are located in the same country as the authors of the cited publication than would be expected by chance. Design. We screened citations referring to 1200 cardiovascular publications in the 7 years following their publication. For the 1200 citation recipient publications we documented the country where the research originated (9 countries/regions) and the total number of received citations. For a selection of 8864 citation donor papers we registered the country/region where the citing paper originated. Results. Self-citation was common in cardiovascular journals (n = 1534, 17.8%). After exclusion of self-citation, however, the number of citations that originated from the same country as the author of the citation recipient was found to be on average 31.6% higher than would be expected by chance (p<0.01 for all countries/regions). In absolute numbers, nation oriented citation bias was most pronounced in the USA, the country with the largest research output (p<0.001). Conclusion. Citation frequency was significantly augmented by nation oriented citation bias. This nation oriented citation behaviour seems to mainly influence the cumulative citation number for papers originating from the countries with a larger research output. Citation frequency: A biased measure of research impact significantly influenced by the geographical origin of research articles Gerard Pasterkamp Joris Rotmans Dominique de Kleijn Cornelius Borst doi:10.1007/s11192-007-0109-5 Scientometrics, Vol. 70, No. 1. (15 January 2007), pp. 153-165. 2007-11-22T07:02:10-00:00 2007 Scientometrics 70 1 153 165 bibliometrics citation distribution http://www.citeulike.org/user/bigbossman/article/2468540 <i>Scientometrics, Vol. 70, No. 1. (15 January 2007), pp. 167-181.</i><br /><br />Abstract  This article calculates probabilities for the occurrence of different types of papers such as genius papers, basic papers, ordinary papers or insignificant papers. The basis of these calculations are the formulae for the cumulative nth citation distribution, being the cumulative distribution of times at which articles receive their nth(n = 1,2,3,...) citation. These formulae (proved in previous papers) are extended to allow for different aging rates of the papers. These new results are then used to define different importance classes of papers according to the different values of n, in function of time t. Examples are given in case of a classification into four parts: genius papers, basic papers, ordinary papers and (almost) insignificant papers. The fact that, in these examples, the size of each class is inversely related to the importance of the journals in this class is proved in a general mathematical context in which we have an arbitrary number of classes and where the threshold values of n in each class are defined according to the natural law of Weber-Fechner. Probabilities for encountering genius, basic, ordinary or insignificant papers based on the cumulative nth citation distribution Leo Egghe doi:10.1007/s11192-007-0110-z Scientometrics, Vol. 70, No. 1. (15 January 2007), pp. 167-181. 2008-03-05T01:29:34-00:00 2007 Scientometrics 70 1 167 181 bibliometrics citation distribution http://www.citeulike.org/user/bigbossman/article/1387765 <i>(7 Jun 2007)</i><br /><br />Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the empirical detection and characterization of power laws is made difficult by the large fluctuations that occur in the tail of the distribution. In particular, standard methods such as least-squares fitting are known to produce systematically biased estimates of parameters for power-law distributions and should not be used in most circumstances. Here we describe statistical techniques for making accurate parameter estimates for power-law data, based on maximum likelihood methods and the Kolmogorov-Smirnov statistic. We also show how to tell whether the data follow a power-law distribution at all, defining quantitative measures that indicate when the power law is a reasonable fit to the data and when it is not. We demonstrate these methods by applying them to twenty-four real-world data sets from a range of different disciplines. Each of the data sets has been conjectured previously to follow a power-law distribution. In some cases we find these conjectures to be consistent with the data while in others the power law is ruled out. Power-law distributions in empirical data Aaron Clauset Cosma Shalizi MEJ Newman (7 Jun 2007) 2007-06-13T16:25:35-00:00 2007 distribution powerlaw