Search (4 results, page 1 of 1)

  • × author_ss:"Burrell, Q.L."
  • × theme_ss:"Informetrie"
  1. Burrell, Q.L.: Predicting future citation behavior (2003) 0.02 
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    Abstract
    In this article we further develop the theory for a stochastic model for the citation process in the presence of obsolescence to predict the future citation pattern of individual papers in a collection. More precisely, we investigate the conditional distribution-and its mean- of the number of citations to a paper after time t, given the number of citations it has received up to time t. In an important parametric case it is shown that the expected number of future citations is a linear function of the current number, this being interpretable as an example of a success-breeds-success phenomenon.
    Date
    29. 3.2003 19:22:48
  2. Mingers, J.; Burrell, Q.L.: Modeling citation behavior in Management Science journals (2006) 0.01 
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    Abstract
    Citation rates are becoming increasingly important in judging the research quality of journals, institutions and departments, and individual faculty. This paper looks at the pattern of citations across different management science journals and over time. A stochastic model is proposed which views the generating mechanism of citations as a gamma mixture of Poisson processes generating overall a negative binomial distribution. This is tested empirically with a large sample of papers published in 1990 from six management science journals and found to fit well. The model is extended to include obsolescence, i.e., that the citation rate for a paper varies over its cited lifetime. This leads to the additional citations distribution which shows that future citations are a linear function of past citations with a time-dependent and decreasing slope. This is also verified empirically in a way that allows different obsolescence functions to be fitted to the data. Conclusions concerning the predictability of future citations, and future research in this area are discussed.
    Date
    26.12.2007 19:22:05
  3. Burrell, Q.L.: Egghe's construction of Lorenz curves resolved (2007) 0.01 
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    Abstract
    In a recent article (Burrell, 2006), the author pointed out that the version of Lorenz concentration theory presented by Egghe (2005a, 2005b) does not conform to the classical statistical/econometric approach. Rousseau (2007) asserts confusion on our part and a failure to grasp Egghe's construction, even though we simply reported what Egghe stated. Here the author shows that Egghe's construction rather than including the standard case, as claimed by Rousseau, actually leads to the Leimkuhler curve of the dual function, in the sense of Egghe. (Note that here we distinguish between the Lorenz curve, a convex form arising from ranking from smallest to largest, and the Leimkuhler curve, a concave form arising from ranking from largest to smallest. The two presentations are equivalent. See Burrell, 1991, 2005; Rousseau, 2007.)
  4. Burrell, Q.L.: Measuring similarity of concentration between different informetric distributions : two new approaches (2005) 0.01 
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    Abstract
    From its earliest days, much investigative work in informetrics has been concerned with inequality aspects. Beginning with the weIl-known Gin! coefficient as a measure of the concentration/inequality of productivity within a single data set, in this study we look at the problem of measuring relative inequallty of productivity between two data sets. A measure originally proposed by Dagum (1987), analogous to the Gin! coefficient, is discussed and developed with both theoretical and empir!cal illustrations. From this we derive a standardized measure-the relative concentration coefficient-based an the notion of "relative economic affluence" also introduced by Dagum (1987). Finally, a new standardized measure-the co-concentration coefficient, in some ways analogous to the correlation coefficient-is defined. The merits and drawbacks of these two measures are discussed and illustrated. Their value will be most readily appreclated in comparative empirical studies.