Editing Power law (section)

==== Bundle plots ====
Another graphical method for the identification of power-law probability distributions using random samples has been proposed.<ref name="Diaz" /> This methodology consists of plotting a ''bundle for the log-transformed sample''. Originally proposed as a tool to explore the existence of moments and the moment generation function using random samples, the bundle methodology is based on residual [[quantile function]]s (RQFs), also called residual percentile functions,<ref>{{cite journal | last1 = Arnold | first1 = B. C. | last2 = Brockett | first2 = P. L. | year = 1983 | title = When does the βth percentile residual life function determine the distribution? | journal = Operations Research | volume = 31 | issue = 2| pages = 391–396 | doi=10.1287/opre.31.2.391| doi-access =  }}</ref><ref>{{cite journal | last1 = Joe | first1 = H. | last2 = Proschan | first2 = F. | year = 1984 | title = Percentile residual life functions | journal = Operations Research | volume = 32 | issue = 3| pages = 668–678 | doi=10.1287/opre.32.3.668}}</ref><ref>Joe, H. (1985), "Characterizations of life distributions from percentile residual lifetimes", ''Ann. Inst. Statist. Math.'' 37, Part A, 165–172.</ref><ref>{{cite journal | last1 = Csorgo | first1 = S. | last2 = Viharos | first2 = L. | year = 1992 | title = Confidence bands for percentile residual lifetimes | url =https://deepblue.lib.umich.edu/bitstream/2027.42/30190/1/0000575.pdf | journal = Journal of Statistical Planning and Inference | volume = 30 | issue = 3| pages = 327–337 | doi=10.1016/0378-3758(92)90159-p| hdl = 2027.42/30190 | hdl-access = free }}</ref><ref>{{cite journal | last1 = Schmittlein | first1 = D. C. | last2 = Morrison | first2 = D. G. | year = 1981 | title = The median residual lifetime: A characterization theorem and an application | journal = Operations Research | volume = 29 | issue = 2| pages = 392–399 | doi=10.1287/opre.29.2.392}}</ref><ref>{{cite journal | last1 = Morrison | first1 = D. G. | last2 = Schmittlein | first2 = D. C. | year = 1980 | title = Jobs, strikes, and wars: Probability models for duration | journal = Organizational Behavior and Human Performance | volume = 25 | issue = 2| pages = 224–251 | doi=10.1016/0030-5073(80)90065-3}}</ref><ref>{{cite journal | last1 = Gerchak | first1 = Y | year = 1984 | title = Decreasing failure rates and related issues in the social sciences | journal = Operations Research | volume = 32 | issue = 3| pages = 537–546 | doi=10.1287/opre.32.3.537}}</ref> which provide a full characterization of the tail behavior of many well-known probability distributions, including power-law distributions, distributions with other types of heavy tails, and even non-heavy-tailed distributions. Bundle plots do not have the disadvantages of Pareto Q–Q plots, mean residual life plots and log–log plots mentioned above (they are robust to outliers,  allow visually identifying power laws with small values of <math>\alpha</math>, and do not demand the collection of much data).{{citation needed|date=May 2012}} In addition, other types of tail behavior can be identified using bundle plots.