A Free Cumulant? Jonathan Novak and Piotr Sniady´

Total Page:16

File Type:pdf, Size:1020Kb

A Free Cumulant? Jonathan Novak and Piotr Sniady´ WHAT IS... ? a Free Cumulant? Jonathan Novak and Piotr Sniady´ Dedicated to Roland Speicher on the occasion of his fiftieth birthday. Cumulants are quantities coupled to combinato- Formula (1) is also well known to probabilists. n rial notions of “connectivity” and probabilistic In stochastic applications, mn = mn(X) = EX is notions of “independence”. There are two princi- the moment sequence of a random variable, and pal species of cumulants: classical and free. Our the quantities cn(X) implicitly defined by (1) are discussion begins with the more widely known called the cumulants of X. This term was coined classical cumulants. by R. Fisher and J. Wishart in 1932. Cumulants Suppose one wishes to determine the num- were, however, first investigated by the Danish ber cn of connected graphs on the vertex set astronomer T. Thiele as early as 1889. He called [n] = {1,...,n}. The total number of (possibly them half-invariants because (n) disconnected) graphs on [n] is mn = 2 2 , and X,Y independent ⇒ cn(X + Y ) = cn(X) + cn(Y ). because any graph is the disjoint union of its connected components, we have This linear behavior is what gives cumulants their advantage: in most situations cumulants are the (1) mn = X Y c|B|, “right” quantities to work with. For example, the π∈P(n) B∈π universality of the standard Gaussian distribu- where the sum runs over all partitions π = B1 ⊔ tion is reflected in the simplicity of its cumulant B2 ⊔... of [n] into disjoint nonempty subsets. The sequence 0, 1, 0, 0, 0,... sequence (mn) thus recursively determines (cn) Let us now consider a geometric variation of our via (1). initial graph-counting problem. Given a graph G on More generally, if mn is the number of “struc- [n], we may represent the vertices of G as points tures” that can be placed on [n], and cn is the on a circle and the edges of G as line segments number of “connected structures” on [n] of the joining these points. The resulting picture may be same type, then the sequences (mn) and (cn) are re- connected even if G is not (Figure 1). Let κn denote lated as in (1). This fundamental enumerative link, the number of “geometrically connected” graphs which is often expressed in terms of generating on [n]. As before, we consider a partition π of [n] functions, is ubiquitous in mathematics. Promi- which tells which vertices of G belong to the same nent examples come from enumerative algebraic connected component of its geometric realization. geometry, where connected covers of Riemann surfaces are counted in terms of all covers, and quantum field theory, where sums over connected 3 2 Feynman diagrams are computed in terms of sums over all diagrams. 4 1 Piotr Sniady´ is a professor in the Polish Academy of Sci- ences and the University of Wroclaw. His email address is 5 6 [email protected]. Jonathan Novak is a postdoctoral fellow at MSRI and the Figure 1. Example of a geometrically University of Waterloo. His email address is j2novak@ connected graph. math.uwaterloo.ca. 300 Notices of the AMS Volume 58, Number 2 It turns out that π is always noncrossing: if we Free probability theory was initiated by represent its blocks as convex polygons, they will Voiculescu in the 1980s, who used it to solve be disjoint (Figure 2). It follows that several previously intractable problems in op- erator algebras. Thus it is a very new field of = (2) mn X Y κ|B|, mathematics, albeit an exceptionally successful ∈N C ∈ π (n) B π one. It therefore came as a surprise when P. Biane where now the summation runs over noncrossing and S. Kerov discovered that free cumulants partitions of [n]. play an important role in a very classical the- Remarkably, just like the cn’s of formula (1), ory, namely the representation theory of the the κn’s of formula (2) may be realized as symmetric groups. half-invariants. Recall that a noncommutative Given a finite group G and a conjugacy class C probability space is a complex algebra A to- of G (viewed as an element of the group algebra gether with a linear functional ϕ : A → C sending C[G]), one knows that C acts as a scalar operator 1A to 1. One regards the elements of A as random in any irreducible representation of C[G]. The variables, with ϕ playing the role of expectation. value of this scalar defines a function on conju- The decision to view noncommutative algebras as gacy classes, called the central character of the probability spaces is justified a posteriori by the irreducible representation. Understanding an irre- fact that this framework supports a new notion of ducible representation amounts to computing its independence, called free independence, modeled central character which, typically, is very difficult. on the free product of algebras. Free independence In the case of the symmetric group G = SN , free is the key to a rich noncommutative probability cumulants shed substantial light on this question. theory—D. Voiculescu’s free probability theory— One begins by considering the Jucys-Murphy ele- which is now widely studied and well known to ment XN = (1 N + 1) +···+ (N N + 1) ∈ C[SN+1], appear in a wide variety of contexts, for example, which is the sum of all transpositions interchang- in the large dimension limit of random matrix ing N + 1 with a smaller number. The spectrum theory. of XN can be completely understood in any ir- The realization that formula (2) holds the key reducible representation; thus XN and simple to free half-invariants is due to R. Speicher. functions of XN may be considered “known”. Given a random variable X in a noncommu- One then defines the nth moment mn = mn(XN ) n tative probability space, we associate with its of XN to be that part of the expansion of XN which n belongs to C[SN ]. These moments are not scalars moment sequence mn(X) = ϕ(X ) the free cumu- but central elements in C[SN ], and they give rise lant sequence κn(X) via (2). This is completely analogous to the construction of classical cumu- to free cumulants κn = κn(XN ) via (2). Remark- lants, the only difference being that the lattice of ably, the conjugacy class Ck of k-cycles can be all partitions has been replaced by the lattice of expressed as a polynomial in the free cumulants noncrossing partitions. Fundamentally, this means of Xn, and furthermore one has the first-order = + that set-theoretic connectivity has been replaced approximation Ck κk+1 lower order terms. with geometric connectivity. Speicher showed that This is not the only surprising appearanceof the free cumulant concept. For example, R. Stanley has X,Y freely indep. ⇒ κn(X + Y ) = κn(X) + κn(Y ). shown that the free cumulants of M. Haiman’s park- Indeed, the relationship between free indepen- ing function symmetric functions are precisely dence and free cumulants mirrors the relationship the complete symmetric functions. M. Lassalle has between classical independence and classical cu- formulated conjectures linking free cumulants mulants in every conceivable way. For example,the to Jack polynomials. Where will free cumulants analogue of the Gaussian distribution in free prob- appear next? ability theory is the Wigner semicircle distribution, and its universality is reflected in the simplicity of References Philippe Biane its free cumulant sequence 0, 1, 0, 0, 0,... [1] , Free probability and combinatorics. In Proceedings of the International Congress of Math- ematicians, Vol. II (Beijing, 2002), pages 765–774, Beijing, 2002, Higher Ed. Press. [2] , Free probability for probabilists. In Quantum probability communications, Vol. XI (Grenoble, 1998), QP-PQ, XI, pages 55–71, World Sci. Publ., River Edge, NJ, 2003. [3] Alexandru Nica and Roland Speicher, Lectures on the combinatorics of free probability, volume 335 of London Mathematical Society Lecture Note Series, Figure 2. Noncrossing (left) versus a crossing Cambridge University Press, Cambridge, 2006. (right) partition. February 2011 Notices of the AMS 301.
Recommended publications
  • Simulation of Moment, Cumulant, Kurtosis and the Characteristics Function of Dagum Distribution
    264 IJCSNS International Journal of Computer Science and Network Security, VOL.17 No.8, August 2017 Simulation of Moment, Cumulant, Kurtosis and the Characteristics Function of Dagum Distribution Dian Kurniasari1*,Yucky Anggun Anggrainy1, Warsono1 , Warsito2 and Mustofa Usman1 1Department of Mathematics, Faculty of Mathematics and Sciences, Universitas Lampung, Indonesia 2Department of Physics, Faculty of Mathematics and Sciences, Universitas Lampung, Indonesia Abstract distribution is a special case of Generalized Beta II The Dagum distribution is a special case of Generalized Beta II distribution. Dagum (1977, 1980) distribution has been distribution with the parameter q=1, has 3 parameters, namely a used in studies of income and wage distribution as well as and p as shape parameters and b as a scale parameter. In this wealth distribution. In this context its features have been study some properties of the distribution: moment, cumulant, extensively analyzed by many authors, for excellent Kurtosis and the characteristics function of the Dagum distribution will be discussed. The moment and cumulant will be survey on the genesis and on empirical applications see [4]. discussed through Moment Generating Function. The His proposals enable the development of statistical characteristics function will be discussed through the expectation of the definition of characteristics function. Simulation studies distribution used to empirical income and wealth data that will be presented. Some behaviors of the distribution due to the could accommodate both heavy tails in empirical income variation values of the parameters are discussed. and wealth distributions, and also permit interior mode [5]. Key words: A random variable X is said to have a Dagum distribution Dagum distribution, moment, cumulant, kurtosis, characteristics function.
    [Show full text]
  • Lecture 2: Moments, Cumulants, and Scaling
    Lecture 2: Moments, Cumulants, and Scaling Scribe: Ernst A. van Nierop (and Martin Z. Bazant) February 4, 2005 Handouts: • Historical excerpt from Hughes, Chapter 2: Random Walks and Random Flights. • Problem Set #1 1 The Position of a Random Walk 1.1 General Formulation Starting at the originX� 0= 0, if one takesN steps of size�xn, Δ then one ends up at the new position X� N . This new position is simply the sum of independent random�xn), variable steps (Δ N � X� N= Δ�xn. n=1 The set{ X� n} contains all the positions the random walker passes during its walk. If the elements of the{ setΔ�xn} are indeed independent random variables,{ X� n then} is referred to as a “Markov chain”. In a MarkovX� chain,N+1is independentX� ofnforn < N, but does depend on the previous positionX� N . LetPn(R�) denote the probability density function (PDF) for theR� of values the random variable X� n. Note that this PDF can be calculated for both continuous and discrete systems, where the discrete system is treated as a continuous system with Dirac delta functions at the allowed (discrete) values. Letpn(�r|R�) denote the PDF�x forn. Δ Note that in this more general notation we have included the possibility that�r (which is the value�xn) of depends Δ R� on. In Markov­chain jargon,pn(�r|R�) is referred to as the “transition probability”X� N fromto state stateX� N+1. Using the PDFs we just defined, let’s return to Bachelier’s equation from the first lecture.
    [Show full text]
  • STAT 830 Generating Functions
    STAT 830 Generating Functions Richard Lockhart Simon Fraser University STAT 830 — Fall 2011 Richard Lockhart (Simon Fraser University) STAT 830 Generating Functions STAT 830 — Fall 2011 1 / 21 What I think you already have seen Definition of Moment Generating Function Basics of complex numbers Richard Lockhart (Simon Fraser University) STAT 830 Generating Functions STAT 830 — Fall 2011 2 / 21 What I want you to learn Definition of cumulants and cumulant generating function. Definition of Characteristic Function Elementary features of complex numbers How they “characterize” a distribution Relation to sums of independent rvs Richard Lockhart (Simon Fraser University) STAT 830 Generating Functions STAT 830 — Fall 2011 3 / 21 Moment Generating Functions pp 56-58 Def’n: The moment generating function of a real valued X is tX MX (t)= E(e ) defined for those real t for which the expected value is finite. Def’n: The moment generating function of X Rp is ∈ ut X MX (u)= E[e ] defined for those vectors u for which the expected value is finite. Formal connection to moments: ∞ k MX (t)= E[(tX ) ]/k! Xk=0 ∞ ′ k = µk t /k! . Xk=0 Sometimes can find power series expansion of MX and read off the moments of X from the coefficients of tk /k!. Richard Lockhart (Simon Fraser University) STAT 830 Generating Functions STAT 830 — Fall 2011 4 / 21 Moments and MGFs Theorem If M is finite for all t [ ǫ,ǫ] for some ǫ> 0 then ∈ − 1 Every moment of X is finite. ∞ 2 M isC (in fact M is analytic). ′ k 3 d µk = dtk MX (0).
    [Show full text]
  • Moments & Cumulants
    ECON-UB 233 Dave Backus @ NYU Lab Report #1: Moments & Cumulants Revised: September 17, 2015 Due at the start of class. You may speak to others, but whatever you hand in should be your own work. Use Matlab where possible and attach your code to your answer. Solution: Brief answers follow, but see also the attached Matlab program. Down- load this document as a pdf, open it, and click on the pushpin: 1. Moments of normal random variables. This should be review, but will get you started with moments and generating functions. Suppose x is a normal random variable with mean µ = 0 and variance σ2. (a) What is x's standard deviation? (b) What is x's probability density function? (c) What is x's moment generating function (mgf)? (Don't derive it, just write it down.) (d) What is E(ex)? (e) Let y = a + bx. What is E(esy)? How does it tell you that y is normal? Solution: (a) The standard deviation is the (positive) square root of the variance, namely σ if σ > 0 (or jσj if you want to be extra precise). (b) The pdf is p(x) = (2πσ2)−1=2 exp(−x2=2σ2): (c) The mgf is h(s) = exp(s2σ2=2). (d) E(ex) is just the mgf evaluated at s = 1: h(1) = eσ2=2. (e) The mfg of y is sy s(a+bx) sa sbx sa hy(s) = E(e ) = E(e ) = e E(e ) = e hx(bs) 2 2 2 2 = esa+(bs) σ =2 = esa+s (bσ) =2: This has the form of a normal random variable with mean a (the coefficient of s) and variance (bσ)2 (the coefficient of s2=2).
    [Show full text]
  • Asymptotic Theory for Statistics Based on Cumulant Vectors with Applications
    UC Santa Barbara UC Santa Barbara Previously Published Works Title Asymptotic theory for statistics based on cumulant vectors with applications Permalink https://escholarship.org/uc/item/4c6093d0 Journal SCANDINAVIAN JOURNAL OF STATISTICS, 48(2) ISSN 0303-6898 Authors Rao Jammalamadaka, Sreenivasa Taufer, Emanuele Terdik, Gyorgy H Publication Date 2021-06-01 DOI 10.1111/sjos.12521 Peer reviewed eScholarship.org Powered by the California Digital Library University of California Received: 21 November 2019 Revised: 8 February 2021 Accepted: 20 February 2021 DOI: 10.1111/sjos.12521 ORIGINAL ARTICLE Asymptotic theory for statistics based on cumulant vectors with applications Sreenivasa Rao Jammalamadaka1 Emanuele Taufer2 György H. Terdik3 1Department of Statistics and Applied Probability, University of California, Abstract Santa Barbara, California For any given multivariate distribution, explicit formu- 2Department of Economics and lae for the asymptotic covariances of cumulant vectors Management, University of Trento, of the third and the fourth order are provided here. Gen- Trento, Italy 3Department of Information Technology, eral expressions for cumulants of elliptically symmetric University of Debrecen, Debrecen, multivariate distributions are also provided. Utilizing Hungary these formulae one can extend several results currently Correspondence available in the literature, as well as obtain practically Emanuele Taufer, Department of useful expressions in terms of population cumulants, Economics and Management, University and computational formulae in terms of commutator of Trento, Via Inama 5, 38122 Trento, Italy. Email: [email protected] matrices. Results are provided for both symmetric and asymmetric distributions, when the required moments Funding information exist. New measures of skewness and kurtosis based European Union and European Social Fund, Grant/Award Number: on distinct elements are discussed, and other applica- EFOP3.6.2-16-2017-00015 tions to independent component analysis and testing are considered.
    [Show full text]
  • Arxiv:2102.01459V2 [Math.PR] 4 Mar 2021 H EHDO UUAT O H OMLAPPROXIMATION NORMAL the for CUMULANTS of METHOD the .Bud Ihfiieymn Oet.Pofo Hoe
    THE METHOD OF CUMULANTS FOR THE NORMAL APPROXIMATION HANNA DORING,¨ SABINE JANSEN, AND KRISTINA SCHUBERT Abstract. The survey is dedicated to a celebrated series of quantitave results, developed by the Lithuanian school of probability, on the normal approximation for a real-valued random 1+γ j−2 variable. The key ingredient is a bound on cumulants of the type |κj (X)|≤ j! /∆ , which is weaker than Cram´er’s condition of finite exponential moments. We give a self-contained proof of some of the “main lemmas” in a book by Saulis and Statuleviˇcius (1989), and an accessible introduction to the Cram´er-Petrov series. In addition, we explain relations with heavy-tailed Weibull variables, moderate deviations, and mod-phi convergence. We discuss some methods for bounding cumulants such as summability of mixed cumulants and dependency graphs, and briefly review a few recent applications of the method of cumulants for the normal approxima- tion. Mathematics Subject Classification 2020: 60F05; 60F10; 60G70; 60K35. Keywords: cumulants; central limit theorems and Berry-Esseen theorems; large and moderate deviations; heavy-tailed variables. Contents 1. Introduction...................................... ................................... 2 1.1. Aims and scope of the article........................... ......................... 2 1.2. Cumulants........................................ ............................... 3 1.3. Short description of the “main lemmas” . ....................... 4 1.4. How to bound cumulants .............................. .........................
    [Show full text]
  • Moments and Generating Functions
    Moments and Generating Functions September 24 and 29, 2009 Some choices of g yield a specific name for the value of Eg(X). 1 Moments, Factorial Moments, and Central Moments • For g(x) = x, we call EX the mean of X and often write µX or simply µ if only the random variable X is under consideration. { S, the number of successes in n Bernoulli trials with success parameter p, has mean np. { The mean of a geometric random variable with parameter p is 1=p. { The mean of a exponential random variable with parameter β is β. { A standard normal random variable has mean 0. • For g(x) = xm, EXm is called the m-th moment of X. { If X is a Bernoulli random variable, then X = Xm. Thus, EXm = EX = p. R 1 m { For a uniform random variable on [0; 1], the m-th moment is 0 x dx = 1=(m + 1). { The third moment for Z, a standard normal random, is 0. The fourth moment, 1 Z 1 z2 1 z2 1 Z 1 z2 4 4 3 2 EZ = p z exp − dz = −p z exp − + 3z exp − dz 2π −∞ 2 2π 2 −∞ −∞ 2 = 3EZ2 = 3 3 z2 u(z) = z v(t) = − exp − 2 0 2 0 z2 u (z) = 3z v (t) = z exp − 2 { For T , an exponential random variable, we integrate by parts Z 1 1 1 Z 1 m m m m−1 ET = t exp −(t/β) dt = t exp −(t/β) + mt exp −(t/β) dt 0 β 0 0 Z 1 1 = βm tm−1 exp −(t/β) dt = mβET m−1 0 β u(t) = tm v(t) = exp −(t/β) 0 m−1 0 1 u (t) = mt v (t) = β exp −(t/β) Thus, by induction, we have that ET m = βmm!: 1 • If g(x) = (x)k, where (x)k = x(x−1) ··· (x−k +1), then E(X)k is called the k-th factorial moment.
    [Show full text]
  • "Advanced Statistics
    the untedn ons educational, scientifi* c abdus s salam and cultural organization international centre for theoretical physics international atomic energy agency SMR: 1343/2 EU ADVANCED COURSE IN COMPUTATIONAL NEUROSCIENCE An IBRO Neuroscience School ( 30 July - 24 August 2001) "Advanced Statistics presented by: Martin STETTER Siemens AG, Corporate Technology CT IC 4, Otto-Hahn-Ring 6 81739 Miinchen GERMANY These are preliminary lecture notes, intended only for distribution to participants. strada costiera, I I - 34014 trieste italy - tel.+39 04022401 I I fax +39 040224163 - [email protected] - www.ictp.trieste.it Statistics Tutorial - Martin Stetter Statistics - Short Intro Martin Stetter Siemens AG, Corporate Technology CT IC 4, Otto-Hahn-Ring 6 81739 Munchen, Germany [email protected] Statistics Tutorial - Martin Stetter Why Statistics in CNS? • Analysis of biological neural data. • Statistical characterization of sensory data. • Modeling the brain as a statistical inference machine... - Statistical characterization of optimal codes. - Description of brain dynamics at a population level. - Statistical biophysical models (reaction kinetics). • You name it... Statistics Tutorial - Martin Stetter 3 A Topics to be Addressed • Introduction and Definitions. • Moments and Cumulants. • Some Basic Concepts of Information Theory. • Point Processes. • Statistical Modeling - Basic Concepts. • Statistical Modeling - Examples. Statistics Tutorial- Martin Stetter Introduction and Definitions One Discrete Random Variable Consider a discrete random variable X. If sampled ("trial"), it randomly assumes one of the values Probability for event X = x(i): Prob(X = x(i)) =: p*. Properties: • 0 < pi < 1. pi = 0 : x(i) does'nt ever occur; pt = 1: o;(i) occurs in every trial (=*• no other x(j) ever observed).
    [Show full text]
  • B.Sc. Course : STATISTICS
    UNIVERSITY OF MUMBAI Syllabus for the S. Y. B.Sc. Program: B.Sc. Course : STATISTICS (Credit Based Semester and Grading System with effect from the academic year 2017–2018) 1 S.Y.B.Sc. STATISTICS Syllabus For Credit Based and Grading System To be implemented from the Academic year 2017-2018 SEMESTER III Course Code UNIT TOPICS Credits L / Week Univariate Random Variables. I 1 (Discrete and Continuous) Standard Discrete Probability USST301 II 2 1 Distributions. Bivariate Probability III 1 Distributions. Concepts of Sampling and I 1 Simple Random Sampling. USST302 II Stratified Sampling. 2 1 III Ratio and Regression Estimation. 1 USSTP3 6 USSTP3(A) Practicals based on USST301 1 3 USSTP3(B) Practicals based on USST302 1 3 SEMESTER IV Course Code UNIT TOPICS Credits L / Week Standard Continuous I Probability Distributions. 1 USST401 II Normal Distribution. 2 1 III Exact Sampling Distributions. 1 I Analysis of Variance. 1 Design Of Experiments, II Completely Randomized design 1 USST402 & Randomized Block Design. 2 Latin Square Design & Factorial III 1 Experiments. USSTP4 6 USSTP4(A) Practicals based on USST401 1 3 USSTP4(B) Practicals based on USST402 1 3 2 Course Title Credits Code USST301 PROBABILITY DISTRIBUTIONS 2 Credits (45 lectures ) Unit I Univariate Random Variables (Discrete and Continuous): 15 Lectures Moment Generating Function(M.G.F.): Definition Properties: - Effect of change of origin and scale, - M.G.F of sum of two independent random variables X and Y , - Extension of this property for n independent random variables and for n i.i.d. random variables. All above properties with proof, - Uniqueness Property without proof.
    [Show full text]
  • Semi-Empirical Probability Distributions and Their
    SEMI-EMPIRICAL PROBABILITY DISTRIBUTIONS AND THEIR APPLICATION IN WAVE-STRUCTURE INTERACTION PROBLEMS A Dissertation by AMIR HOSSEIN IZADPARAST Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY December 2010 Major Subject: Ocean Engineering SEMI-EMPIRICAL PROBABILITY DISTRIBUTIONS AND THEIR APPLICATION IN WAVE-STRUCTURE INTERACTION PROBLEMS A Dissertation by AMIR HOSSEIN IZADPARAST Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Approved by: Chair of Committee, John M. Niedzwecki Committee Members, Kenneth F. Reinschmidt James M. Kaihatu Steven F. DiMarco Head of Department, John M. Niedzwecki December 2010 Major Subject: Ocean Engineering iii I.ABSTRACT Semi-empirical Probability Distributions and Their Application in Wave-Structure Interaction Problems. (December 2010) Amir Hossein Izadparast, B.S., Shiraz University; M.S., University of Tehran Chair of Advisory Committee: Dr. John Michael Niedzwecki In this study, the semi-empirical approach is introduced to accurately estimate the probability distribution of complex non-linear random variables in the field of wave- structure interaction. The structural form of the semi-empirical distribution is developed based on a mathematical representation of the process and the model parameters are estimated directly from utilization of the sample data. Here, three probability distributions are developed based on the quadratic transformation of the linear random variable. Assuming that the linear process follows a standard Gaussian distribution, the three-parameter Gaussian-Stokes model is derived for the second-order variables. Similarly, the three-parameter Rayleigh-Stokes model and the four-parameter Weibull- Stokes model are derived for the crests, troughs, and heights of non-linear process assuming that the linear variable has a Rayleigh distribution or a Weibull distribution.
    [Show full text]
  • Lecture Notes
    Risk theory Harri Nyrhinen, University of Helsinki Fall 2017 Sisältö 1 Introduction 1 2 Background from Probability Theory 3 3 The number of claims 9 3.1 Poisson distribution and process . 10 3.2 Mixed Poisson variable . 16 3.3 The number of claims of a single policy-holder . 18 3.4 Mixed Poisson process . 19 4 Total claim amount 22 5 Viewpoints on claim size distributions 26 5.1 Tabulation method . 26 5.2 Analytical methods . 26 5.3 On the estimation of the tails of the distribution . 29 6 Calculation and estimation of the total claim amount 31 6.1 Panjer method . 31 6.2 Approximation of the compound distributions . 33 6.2.1 Limiting behaviour of compound distributions . 33 6.2.2 Refinements of the normal approximation . 36 6.2.3 Applications of approximation methods . 39 6.3 Simulation of compound distributions . 42 6.3.1 Producing observations . 43 6.3.2 Estimation . 44 6.3.3 Increasing efficiency of simulation of small probabilities . 46 6.4 An upper bound for the tail probability . 53 6.5 Modelling dependence . 54 6.5.1 Mixing models . 55 6.5.2 Copulas . 55 7 Reinsurance 61 7.1 Excess of loss (XL) . 61 7.2 Quota share (QS) . 67 7.3 Surplus . 68 7.4 Stop loss (SL) . 69 8 Outstanding claims 71 8.1 Development triangles . 71 8.2 Chain-Ladder method . 72 8.3 Predicting of the unknown claims . 76 8.4 Credibility estimates for outstanding claims . 82 9 Solvency in the long run 86 9.1 Classical ruin problem .
    [Show full text]
  • Finitely Generated Cumulants
    Statistica Sinica 9(1999), 1029-1052 FINITELY GENERATED CUMULANTS Giovanni Pistone and Henry P. Wynn Politecnico di Torino and University of Warwick Abstract: Computations with cumulants are becoming easier through the use of computer algebra but there remains a difficulty with the finiteness of the com- putations because all distributions except the normal have an infinite number of non-zero cumulants. One is led therefore to replacing finiteness of computations by “finitely generated” in the sense of recurrence relationships. In fact it turns out that there is a natural definition in terms of the exponential model which is that the first and second derivative of the cumulant generating function, K, lie on a polynomial variety. This generalises recent polynomial conditions on variance functions. This is satisfied by many examples and has applications to, for example, exact expressions for variance functions and saddle-point approximations. Key words and phrases: Computer algebra, cumulants, exponential models. 1. Introduction It is perhaps best to introduce this paper by describing briefly the route by which the authors came to the definition of finite generation of cumulants. The starting point was the recognition that in the understanding of the propagation of randomness through systems, cumulants may be useful. The cumulants of the output Y of a system may be computed, for some systems, directly from the cumulants of the input X. The theory of McCullagh (1987) allows this to be done if the function y = f(x) relating the input to the output is polynomial, and where both X and Y are multivariate. We mention also the survey paper of Guti´errez- Pe˜na and Smith (1997), where the relation to conjugate prior distributions is discussed.
    [Show full text]