Open Access
Relations between the continuous and the discrete Lotka power function
Author(s) -
Egghe L
Publication year - 2005
Publication title -
journal of the american society for information science and technology
Language(s) - English
Resource type - Journals
eISSN - 1532-2890
pISSN - 1532-2882
DOI - 10.1002/asi.20157
Subject(s) - informetrics , function (biology) , continuous function (set theory) , power function , mathematics , variable (mathematics) , probability density function , continuous variable , statistical physics , discrete mathematics , computer science , statistics , mathematical analysis , data mining , physics , evolutionary biology , bibliometrics , biology
Abstract The discrete Lotka power function describes the number of sources (e.g., authors) with n = 1, 2, 3, … items (e.g., publications). As in econometrics, informetrics theory requires functions of a continuous variable j , replacing the discrete variable n . Now j represents item densities instead of number of items. The continuous Lotka power function describes the density of sources with item density j . The discrete Lotka function one obtains from data, obtained empirically; the continuous Lotka function is the one needed when one wants to apply Lotkaian informetrics, i.e., to determine properties that can be derived from the (continuous) model. It is, hence, important to know the relations between the two models. We show that the exponents of the discrete Lotka function (if not too high, i.e., within limits encountered in practice) and of the continuous Lotka function are approximately the same. This is important to know in applying theoretical results (from the continuous model), derived from practical data.