Probability density function Pareto Type I probability density functions for various with As the distribution approaches where is the Dirac delta function.  
Cumulative distribution function Pareto Type I cumulative distribution functions for various with  
Parameters 
scale (real) shape (real)  

Support  
CDF  
Mean  
Median  
Mode  
Variance  
Skewness  
Ex. kurtosis  
Entropy  
MGF  
CF  
Fisher information 
Wrong!!!: Right: 
The Pareto distribution, named after the Italian civil engineer, economist, and sociologist Vilfredo Pareto^{[1]}, is a powerlaw probability distribution that is used in description of social, scientific, geophysical, actuarial, and many other types of observable phenomena. Originally applied to describing the distribution of wealth in a society, fitting the trend that a large portion of wealth is held by a small fraction of the population^{[2]}, the Pareto distribution has colloquially become known and referred to as the Pareto principle, or "8020 rule", and is sometimes called the "Matthew principle". This rule states that, for example, 80% of the wealth of a society is held by 20% of its population. However, one should not conflate the Pareto distribution with the Pareto Principle as the former only produces this result for a particular power value, (α = log_{4}5 ≈ 1.16). While is variable, empirical observation has found the 8020 distribution to fit a wide range of cases, including natural phenomena^{[3]} and human activities^{[4]}. Further, it is only an observation, not a law of nature^{[5]}.
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Transcription
Have you ever been interested in becoming more productive or managing your time better? Then you've most likely come across the Pareto Principle before, also known as the 80/20 rule. If you've never heard of it, then you'll learn more about it in this video. The Pareto principle states that in any situation, 20 percent of the inputs or activities, are responsible for 80 percent of the outputs or results. And I'll explain what that means in a second. The principle was named after an Italian economist, Vilfredo Pareto. He first observed this law in his own garden. What he noticed was that 20% of the pea pods generated 80% of the healthy peas. This observation led him to discover that 80% of the land in Italy was owned by just 20% of the population. And we can even see this concept everywhere in our daily lives. For example: You wear 20% of your clothes 80% of the time. In a book, 20% of its pages contain 80% of the most important information. 20% of the company's customers, produce 80% of company's revenue When it comes to YouTube, 20% percent of my videos generate 80% of my views and subscribers. The Pareto principle shows up over and over again, in almost every field. But the inverse is also true. That means that the other 80% is only generating 20% of the results. However you should note that this is not an universal law, and it can differ in many situations. It's not always going to be 80/20, it could be 70/30, 90/10, basically anything. The point is that the majority of results come from minority of causes. And the minority of results come from the majority of causes. Now that we've established that this principle does indeed hold true, let's take a look at how you can use it in your everyday life. Time is our most precious resource. We all have the same amount of time in our day. But most of us don’t use that time efficiently. There's a difference between being busy and being productive. Most people think that working more hours will get them more results. However it's not about the time you put in, it's about how well you spend that time. If you haven't applied the Pareto principle in your life yet, you're most likely just being busy. However when the principle is utilized correctly it enables you to do more by doing less. If we go back to our previous example, we said that 20% of a book will give us 80% of the information. That means that 80% of a book will only give us 20% of its value. Let's say it takes you 10 hours to read 100% of that book. By applying the Pareto principle you know that 80% of the most important information can be found in just 2 hours. Yes, you could go deeper and learn more in depth if you wanted to, but note that you will most likely have to spend 8 hours to get those extra 20% of the information. It's up to you to decide if you think it's worth it. If you're still in school you can also take advantage of the 80/20 rule. The exams never contain 100% of the content. Never. Otherwise it would be a 50 page exam. You can get a good grade by identifying, which 20% of the content you were studying in class, is the most important. Studying the right topic for two hours will get you a much better grade, than studying the wrong topic for a whole week. Again, you could reread every single page of the textbook and get that 100%. But if being a top student is not your priority, then it just might not be worth your time. You can even do a Pareto analysis on your friendships. What you're most likely to find is that 20% of the friends, give you 80% of your fulfillment and joy, that you get from social interactions. The other 80% of your so called "friends" are only giving you 20% fulfillment. Now you don't have to cut away those friendships completely, but you don't have to spend an equal amount of time with all your companions. It's much better to have a few close friends, than to have a bunch of really distant friends. This is why you should spend more time on the 20% that give you the most satisfaction, and commit less time to the other 80%. And that pretty much sums up the Pareto principle aka the 80/20 rule. It can be applied to almost any area of your life, whether it's business or free time. If you can identify the 20% that produces the greatest outcome, you can spend more time doing that, to create an even greater payoff. It also helps you cut back on the 80% of things that waste your time, which create only 20% of the results. It encourages you to think efficiently and focus on what is actually important. So think about what are some of the things that you could double down on, and which ones you should eliminate. Let me know how are you going to use the 80/20 rule in the comments below. For example I have noticed that I use 20% of my shoes 80% of the time. This is why I threw the 80% I wasn't using away, because all they were doing is taking up my space. Thanks for watching and I hope you enjoyed the video. Make sure to leave a like and click that little bell icon next to my channels name. Youtube has been changing up its algorithm and for some reason it's much harder to find my videos now. However clicking on the bell will notify you whenever I post some new content. This way you won't miss a chance to become better than yesterday.
Contents
 1 Definitions
 2 Properties
 3 Related distributions
 4 Statistical Inference
 5 Occurrence and applications
 6 Computational methods
 7 See also
 8 References
 9 Notes
 10 External links
Definitions
If X is a random variable with a Pareto (Type I) distribution,^{[6]} then the probability that X is greater than some number x, i.e. the survival function (also called tail function), is given by
where x_{m} is the (necessarily positive) minimum possible value of X, and α is a positive parameter. The Pareto Type I distribution is characterized by a scale parameter x_{m} and a shape parameter α, which is known as the tail index. When this distribution is used to model the distribution of wealth, then the parameter α is called the Pareto index.
Cumulative distribution function
From the definition, the cumulative distribution function of a Pareto random variable with parameters α and x_{m} is
Probability density function
It follows (by differentiation) that the probability density function is
When plotted on linear axes, the distribution assumes the familiar Jshaped curve which approaches each of the orthogonal axes asymptotically. All segments of the curve are selfsimilar (subject to appropriate scaling factors). When plotted in a loglog plot, the distribution is represented by a straight line.
Properties
Moments and characteristic function
 The expected value of a random variable following a Pareto distribution is
 The variance of a random variable following a Pareto distribution is
 (If α ≤ 1, the variance does not exist.)
 The raw moments are
 The moment generating function is only defined for nonpositive values t ≤ 0 as
 The characteristic function is given by
 where Γ(a, x) is the incomplete gamma function.
Conditional distributions
The conditional probability distribution of a Paretodistributed random variable, given the event that it is greater than or equal to a particular number exceeding , is a Pareto distribution with the same Pareto index but with minimum instead of .
A characterization theorem
Suppose are independent identically distributed random variables whose probability distribution is supported on the interval for some . Suppose that for all , the two random variables and are independent. Then the common distribution is a Pareto distribution.^{[citation needed]}
Geometric mean
The geometric mean (G) is^{[7]}
Harmonic mean
The harmonic mean (H) is^{[7]}
Graphical representation
The characteristic curved 'long tail' distribution when plotted on a linear scale, masks the underlying simplicity of the function when plotted on a loglog graph, which then takes the form of a straight line with negative gradient: It follows from the formula for the probability density function that for x ≥ x_{m},
Since α is positive, the gradient −(α + 1) is negative.
Related distributions
Generalized Pareto distributions
There is a hierarchy ^{[6]}^{[8]} of Pareto distributions known as Pareto Type I, II, III, IV, and Feller–Pareto distributions.^{[6]}^{[8]}^{[9]} Pareto Type IV contains Pareto Type I–III as special cases. The Feller–Pareto^{[8]}^{[10]} distribution generalizes Pareto Type IV.
Pareto types I–IV
The Pareto distribution hierarchy is summarized in the next table comparing the survival functions (complementary CDF).
When μ = 0, the Pareto distribution Type II is also known as the Lomax distribution.^{[11]}
In this section, the symbol x_{m}, used before to indicate the minimum value of x, is replaced by σ.
Support  Parameters  

Type I  
Type II  
Lomax  
Type III  
Type IV 
The shape parameter α is the tail index, μ is location, σ is scale, γ is an inequality parameter. Some special cases of Pareto Type (IV) are
The finiteness of the mean, and the existence and the finiteness of the variance depend on the tail index α (inequality index γ). In particular, fractional δmoments are finite for some δ > 0, as shown in the table below, where δ is not necessarily an integer.
Condition  Condition  

Type I  
Type II  
Type III  
Type IV 
Feller–Pareto distribution
Feller^{[8]}^{[10]} defines a Pareto variable by transformation U = Y^{−1} − 1 of a beta random variable Y, whose probability density function is
where B( ) is the beta function. If
then W has a Feller–Pareto distribution FP(μ, σ, γ, γ_{1}, γ_{2}).^{[6]}
If and are independent Gamma variables, another construction of a Feller–Pareto (FP) variable is^{[12]}
and we write W ~ FP(μ, σ, γ, δ_{1}, δ_{2}). Special cases of the Feller–Pareto distribution are
Relation to the exponential distribution
The Pareto distribution is related to the exponential distribution as follows. If X is Paretodistributed with minimum x_{m} and index α, then
is exponentially distributed with rate parameter α. Equivalently, if Y is exponentially distributed with rate α, then
is Paretodistributed with minimum x_{m} and index α.
This can be shown using the standard changeofvariable techniques:
The last expression is the cumulative distribution function of an exponential distribution with rate α.
Relation to the lognormal distribution
The Pareto distribution and lognormal distribution are alternative distributions for describing the same types of quantities. One of the connections between the two is that they are both the distributions of the exponential of random variables distributed according to other common distributions, respectively the exponential distribution and normal distribution.^{[citation needed]}
Relation to the generalized Pareto distribution
The Pareto distribution is a special case of the generalized Pareto distribution, which is a family of distributions of similar form, but containing an extra parameter in such a way that the support of the distribution is either bounded below (at a variable point), or bounded both above and below (where both are variable), with the Lomax distribution as a special case. This family also contains both the unshifted and shifted exponential distributions.
The Pareto distribution with scale and shape is equivalent to the generalized Pareto distribution with location , scale and shape . Vice versa one can get the Pareto distribution from the GPD by and .
Bounded Pareto distribution
The bounded (or truncated) Pareto distribution has three parameters: α, L and H. As in the standard Pareto distribution α determines the shape. L denotes the minimal value, and H denotes the maximal value.
The probability density function is
 ,
where L ≤ x ≤ H, and α > 0.
Generating bounded Pareto random variables
If U is uniformly distributed on (0, 1), then applying inversetransform method ^{[13]}
is a bounded Paretodistributed.^{[citation needed]}
Symmetric Pareto distribution
The purpose of Symmetric Pareto distribution and Zero Symmetric Pareto distribution is to capture some special statistical distribution with a sharp probability peak and symmetric long probability tails. These two distributions are derived from Pareto distribution. Long probability tail normally means that probability decays slowly. Pareto distribution performs fitting job in many cases. But if the distribution has symmetric structure with two slow decaying tails, Pareto could not do it. Then Symmetric Pareto or Zero Symmetric Pareto distribution is applied instead.^{[14]}
The Cumulative distribution function (CDF) of Symmetric Pareto distribution is defined as following:^{[14]}
The corresponding probability density function (PDF) is:^{[14]}
This distribution has two parameters: a and b. It is symmetric by b. Then the mathematic expectation is b. When, it has variance as following:
The CDF of Zero Symmetric Pareto (ZSP) distribution is defined as following:
The corresponding PDF is:
This distribution is symmetric by zero. Parameter is related to the decay rate of probability and represents peak magnitude of probability.^{[14]}
Multivariate Pareto distribution
The univariate Pareto distribution has been extended to a multivariate Pareto distribution.^{[15]}
Statistical Inference
Estimation of parameters
The likelihood function for the Pareto distribution parameters α and x_{m}, given an independent sample x = (x_{1}, x_{2}, ..., x_{n}), is
Therefore, the logarithmic likelihood function is
It can be seen that is monotonically increasing with x_{m}, that is, the greater the value of x_{m}, the greater the value of the likelihood function. Hence, since x ≥ x_{m}, we conclude that
To find the estimator for α, we compute the corresponding partial derivative and determine where it is zero:
Thus the maximum likelihood estimator for α is:
The expected statistical error is:^{[16]}
Malik (1970)^{[17]} gives the exact joint distribution of . In particular, and are independent and is Pareto with scale parameter x_{m} and shape parameter nα, whereas has an inversegamma distribution with shape and scale parameters n − 1 and nα, respectively.
Occurrence and applications
General
Vilfredo Pareto originally used this distribution to describe the allocation of wealth among individuals since it seemed to show rather well the way that a larger portion of the wealth of any society is owned by a smaller percentage of the people in that society. He also used it to describe distribution of income.^{[18]} This idea is sometimes expressed more simply as the Pareto principle or the "8020 rule" which says that 20% of the population controls 80% of the wealth.^{[19]} However, the 8020 rule corresponds to a particular value of α, and in fact, Pareto's data on British income taxes in his Cours d'économie politique indicates that about 30% of the population had about 70% of the income.^{[citation needed]} The probability density function (PDF) graph at the beginning of this article shows that the "probability" or fraction of the population that owns a small amount of wealth per person is rather high, and then decreases steadily as wealth increases. (The Pareto distribution is not realistic for wealth for the lower end, however. In fact, net worth may even be negative.) This distribution is not limited to describing wealth or income, but to many situations in which an equilibrium is found in the distribution of the "small" to the "large". The following examples are sometimes seen as approximately Paretodistributed:
 The sizes of human settlements (few cities, many hamlets/villages)^{[20]}
 File size distribution of Internet traffic which uses the TCP protocol (many smaller files, few larger ones)^{[20]}
 Hard disk drive error rates^{[21]}
 Clusters of Bose–Einstein condensate near absolute zero^{[22]}
 The values of oil reserves in oil fields (a few large fields, many small fields)^{[20]}
 The length distribution in jobs assigned to supercomputers (a few large ones, many small ones)^{[23]}
 The standardized price returns on individual stocks ^{[20]}
 Sizes of sand particles ^{[20]}
 The size of meteorites
 Male dating success on Tinder [80% of females compete for the 20% most attractive males] ^{[24]}
 Severity of large casualty losses for certain lines of business such as general liability, commercial auto, and workers compensation.^{[25]}^{[26]}
 Amount of time a user on Steam will spend playing different games. (Some games get played a lot, but most get played almost never.) [3]
 In hydrology the Pareto distribution is applied to extreme events such as annually maximum oneday rainfalls and river discharges.^{[27]} The blue picture illustrates an example of fitting the Pareto distribution to ranked annually maximum oneday rainfalls showing also the 90% confidence belt based on the binomial distribution. The rainfall data are represented by plotting positions as part of the cumulative frequency analysis.
Relation to Zipf's law
The Pareto distribution is a continuous probability distribution. Zipf's law, also sometimes called the zeta distribution, is a discrete distribution, separating the values into a simple ranking. Both are a simple power law with a negative exponent, scaled so that their cumulative distributions equal 1. Zipf's can be derived from the Pareto distribution if the values (incomes) are binned into ranks so that the number of people in each bin follows a 1/rank pattern. The distribution is normalized by defining so that where is the generalized harmonic number. This makes Zipf's probability density function derivable from Pareto's.
where and is an integer representing rank from 1 to N where N is the highest income bracket. So a randomly selected person (or word, website link, or city) from a population (or language, internet, or country) has probability of ranking .
Relation to the "Pareto principle"
The "8020 law", according to which 20% of all people receive 80% of all income, and 20% of the most affluent 20% receive 80% of that 80%, and so on, holds precisely when the Pareto index is α = log_{4}(5) = log(5)/log(4), approximately 1.161. This result can be derived from the Lorenz curve formula given below. Moreover, the following have been shown^{[28]} to be mathematically equivalent:
 Income is distributed according to a Pareto distribution with index α > 1.
 There is some number 0 ≤ p ≤ 1/2 such that 100p % of all people receive 100(1 − p)% of all income, and similarly for every real (not necessarily integer) n > 0, 100p^{n} % of all people receive 100(1 − p)^{n} percentage of all income. α and p are related by
This does not apply only to income, but also to wealth, or to anything else that can be modeled by this distribution.
This excludes Pareto distributions in which 0 < α ≤ 1, which, as noted above, have infinite expected value, and so cannot reasonably model income distribution.
Relation to Price's law
Price's square root law is sometimes offered as a property of or as similar to the Pareto distribution. However, the law only holds in the case that . Note that in this case, the total and expected amount of wealth are not defined, and the rule only applies asymptotically to random samples. The extended Pareto Principle mentioned above is a far more general rule.
Lorenz curve and Gini coefficient
The Lorenz curve is often used to characterize income and wealth distributions. For any distribution, the Lorenz curve L(F) is written in terms of the PDF f or the CDF F as
where x(F) is the inverse of the CDF. For the Pareto distribution,
and the Lorenz curve is calculated to be
For the denominator is infinite, yielding L=0. Examples of the Lorenz curve for a number of Pareto distributions are shown in the graph on the right.
According to Oxfam (2016) the richest 62 people have as much wealth as the poorest half of the world's population.^{[29]} We can estimate the Pareto index that would apply to this situation. Letting ε equal we have:
or
The solution is that α equals about 1.15, and about 9% of the wealth is owned by each of the two groups. But actually the poorest 69% of the world adult population owns only about 3% of the wealth.^{[30]}
The Gini coefficient is a measure of the deviation of the Lorenz curve from the equidistribution line which is a line connecting [0, 0] and [1, 1], which is shown in black (α = ∞) in the Lorenz plot on the right. Specifically, the Gini coefficient is twice the area between the Lorenz curve and the equidistribution line. The Gini coefficient for the Pareto distribution is then calculated (for ) to be
(see Aaberge 2005).
Computational methods
Random sample generation
Random samples can be generated using inverse transform sampling. Given a random variate U drawn from the uniform distribution on the unit interval (0, 1], the variate T given by
is Paretodistributed.^{[31]} If U is uniformly distributed on [0, 1), it can be exchanged with (1 − U).
See also
 Bradford's law
 Gutenberg–Richter law
 Matthew effect
 Pareto analysis
 Pareto efficiency
 Pareto interpolation
 Power law probability distributions
 Sturgeon's law
 Traffic generation model
 Zipf's law
 Heavytailed distribution
References
 ^ Amoroso, Luigi (1938). "VILFREDO PARETO". Econometrica (Pre1986); Jan 1938; 6, 1; ProQuest. 6.
 ^ Pareto, Vilfredo (1898). "Cours d'economie politique". Journal of Political Economy. 6.
 ^ VAN MONTFORT, M.A.J. (1986). "The Generalized Pareto distribution applied to rainfall depths". Hydrological Sciences Journal. 31 (2): 151–162. doi:10.1080/02626668609491037.
 ^ Oancea, Bogdan (2017). "Income inequality in Romania: The exponentialPareto distribution". Physica A: Statistical Mechanics and Its Applications. 469: 486–498. Bibcode:2017PhyA..469..486O. doi:10.1016/j.physa.2016.11.094.
 ^ "Understanding the Pareto Principle (The 80/20 Rule)".
 ^ ^{a} ^{b} ^{c} ^{d} Barry C. Arnold (1983). Pareto Distributions. International Cooperative Publishing House. ISBN 9780899740126.
 ^ ^{a} ^{b} Johnson NL, Kotz S, Balakrishnan N (1994) Continuous univariate distributions Vol 1. Wiley Series in Probability and Statistics.
 ^ ^{a} ^{b} ^{c} ^{d} Johnson, Kotz, and Balakrishnan (1994), (20.4).
 ^ Christian Kleiber & Samuel Kotz (2003). Statistical Size Distributions in Economics and Actuarial Sciences. Wiley. ISBN 9780471150640.
 ^ ^{a} ^{b} Feller, W. (1971). An Introduction to Probability Theory and its Applications. II (2nd ed.). New York: Wiley. p. 50. "The densities (4.3) are sometimes called after the economist Pareto. It was thought (rather naïvely from a modern statistical standpoint) that income distributions should have a tail with a density ~ Ax^{−α} as x → ∞."
 ^ Lomax, K. S. (1954). "Business failures. Another example of the analysis of failure data". Journal of the American Statistical Association. 49 (268): 847–52. doi:10.1080/01621459.1954.10501239.
 ^ Chotikapanich, Duangkamon. "Chapter 7: Pareto and Generalized Pareto Distributions". Modeling Income Distributions and Lorenz Curves. pp. 121–22.
 ^ http://www.cs.bgu.ac.il/~mps042/invtransnote.htm
 ^ ^{a} ^{b} ^{c} ^{d} Huang, Xiaodong (2004). "A Multiscale Model for MPEG4 Varied Bit Rate Video Traffic". IEEE Transactions on Broadcasting. 50 (3): 323–334. doi:10.1109/TBC.2004.834013.
 ^ Rootzén, Holger; Tajvidi, Nader (2006). "Multivariate generalized Pareto distributions". Bernoulli. 12 (5): 917–30. CiteSeerX 10.1.1.145.2991. doi:10.3150/bj/1161614952.
 ^ M. E. J. Newman (2005). "Power laws, Pareto distributions and Zipf's law". Contemporary Physics. 46 (5): 323–51. arXiv:condmat/0412004. Bibcode:2005ConPh..46..323N. doi:10.1080/00107510500052444.
 ^ H. J. Malik (1970). "Estimation of the Parameters of the Pareto Distribution". Metrika. 15: 126–132. doi:10.1007/BF02613565.
 ^ Pareto, Vilfredo, Cours d'Économie Politique: Nouvelle édition par G.H. Bousquet et G. Busino, Librairie Droz, Geneva, 1964, pp. 299–345.
 ^ For a twoquantile population, where approximately 18% of the population owns 82% of the wealth, the Theil index takes the value 1.
 ^ ^{a} ^{b} ^{c} ^{d} ^{e} Reed, William J.; et al. (2004). "The Double ParetoLognormal Distribution – A New Parametric Model for Size Distributions". Communications in Statistics – Theory and Methods. 33 (8): 1733–53. CiteSeerX 10.1.1.70.4555. doi:10.1081/sta120037438.
 ^ Schroeder, Bianca; Damouras, Sotirios; Gill, Phillipa (20100224). "Understanding latent sector error and how to protect against them" (PDF). 8th Usenix Conference on File and Storage Technologies (FAST 2010). Retrieved 20100910.
We experimented with 5 different distributions (Geometric,Weibull, Rayleigh, Pareto, and Lognormal), that are commonly used in the context of system reliability, and evaluated their fit through the total squared differences between the actual and hypothesized frequencies (χ^{2} statistic). We found consistently across all models that the geometric distribution is a poor fit, while the Pareto distribution provides the best fit.
 ^ Yuji Ijiri; Simon, Herbert A. (May 1975). "Some Distributions Associated with Bose–Einstein Statistics". Proc. Natl. Acad. Sci. USA. 72 (5): 1654–57. Bibcode:1975PNAS...72.1654I. doi:10.1073/pnas.72.5.1654. PMC 432601. PMID 16578724.
 ^ HarcholBalter, Mor; Downey, Allen (August 1997). "Exploiting Process Lifetime Distributions for Dynamic Load Balancing" (PDF). ACM Transactions on Computer Systems. 15 (3): 253–258. doi:10.1145/263326.263344.
 ^ [1]
 ^ Kleiber and Kotz (2003): p. 94.
 ^ Seal, H. (1980). "Survival probabilities based on Pareto claim distributions". ASTIN Bulletin. 11: 61–71. doi:10.1017/S0515036100006620.
 ^ CumFreq, software for cumulative frequency analysis and probability distribution fitting [2]
 ^ Hardy, Michael (2010). "Pareto's Law". Mathematical Intelligencer. 32 (3): 38–43. doi:10.1007/s0028301091592.
 ^ "62 people own the same as half the world, reveals Oxfam Davos report". Oxfam. Jan 2016.
 ^ "Global Wealth Report 2013". Credit Suisse. Oct 2013. p. 22.
 ^ Tanizaki, Hisashi (2004). Computational Methods in Statistics and Econometrics. CRC Press. p. 133. ISBN 9780824750886.
Notes
 M. O. Lorenz (1905). "Methods of measuring the concentration of wealth". Publications of the American Statistical Association. 9 (70): 209–19. Bibcode:1905PAmSA...9..209L. doi:10.2307/2276207. JSTOR 2276207.
 Pareto, Vilfredo (1965). Librairie Droz (ed.). Ecrits sur la courbe de la répartition de la richesse. Œuvres complètes : T. III. p. 48. ISBN 9782600040211.
 Pareto, Vilfredo (1895). "La legge della domanda". Giornale Degli Economisti. 10: 59–68.
 Pareto, Vilfredo (1896). "Cours d'économie politique". doi:10.1177/000271629700900314. Cite journal requires
journal=
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External links
 Hazewinkel, Michiel, ed. (2001) [1994], "Pareto distribution", Encyclopedia of Mathematics, Springer Science+Business Media B.V. / Kluwer Academic Publishers, ISBN 9781556080104
 Weisstein, Eric W. "Pareto distribution". MathWorld.
 Aabergé, Rolf (May 2005), Gini's Nuclear Family (PDF)
 Crovella, Mark E.; Bestavros, Azer (December 1997). SelfSimilarity in World Wide Web Traffic: Evidence and Possible Causes (PDF). IEEE/ACM Transactions on Networking. 5. pp. 835–846.
 syntraf1.c is a C program to generate synthetic packet traffic with bounded Pareto burst size and exponential interburst time.