scholarly journals Approximating the Distribution of a Dynamic Risk Portfolio

1984 ◽  
Vol 14 (2) ◽  
pp. 135-148 ◽  
Author(s):  
William S. Jewell
Keyword(s):  
Recursive Algorithm ◽  
Random Variables ◽  
Approximation Procedure ◽  
Random Sum ◽  
Dynamic Risk ◽  
Sum Of Random Variables

AbstractIn a previous paper, Jewell and Sundt showed how to approximate a distribution of total losses from a large, fixed, heterogeneous portfolio, using a recursive algorithm developed by Panjer for the distribution of a random sum of random variables (a single casualty contract). This paper extends the approximation procedure to large, dynamic heterogeneous portfolios, in order to model either a portfolio of correlated casualty contracts, or a future portfolio, whose composition is not known with certainty.

The tail behaviour of a random sum of subexponential random variables and vectors

Extremes ◽  
2007 ◽  
Vol 10 (1-2) ◽  
pp. 21-39 ◽  
Author(s):  
D. J. Daley ◽  
Edward Omey ◽  
Rein Vesilo
Keyword(s):  
Random Variables ◽  
Random Sum ◽  
Tail Behaviour

scholarly journals Tracking—A

2021 ◽  
pp. 163-192
Author(s):  
Jean Walrand
Keyword(s):  
Kalman Filter ◽  
Linear Regression ◽  
Least Squares ◽  
Recursive Algorithm ◽  
Nonlinear Function ◽  
Random Variables ◽  
Random Variable ◽  
Linear Least Squares ◽  
Least Squares Estimate ◽  
Related Notion

AbstractThis chapter explains how to estimate an unobserved random variable or vector from available observations. This problem arises in many examples, as illustrated in Sect. 9.1. The basic problem is defined in Sect. 9.2. One commonly used approach is the linear least squares estimate explained in Sect. 9.3. A related notion is the linear regression covered in Sect. 9.4. Section 9.5 comments on the problem of overfitting. Sections 9.6 and 9.7 explain the minimum means squares estimate that may be a nonlinear function of the observations and the remarkable fact that it is linear for jointly Gaussian random variables. Section 9.8 is devoted to the Kalman filter, which is a recursive algorithm for calculating the linear least squares estimate of the state of a system given previous observations.

A central limit theorem for exchangeable variates with geometric applications

1973 ◽  
Vol 10 (4) ◽  
pp. 837-846 ◽  
Author(s):  
P. A. P. Moran
Keyword(s):  
Central Limit Theorem ◽  
Limit Theorem ◽  
Asymptotic Normality ◽  
Central Limit ◽  
Random Variables ◽  
Multinomial Distribution ◽  
Sum Of Random Variables ◽  
Th Cell ◽  
Occupancy Problems

A central limit theorem is proved for the sum of random variables Xr all having the same form of distribution and each of which depends on a parameter which is the number occurring in the rth cell of a multinomial distribution with equal probabilities in N cells and a total n, where nN–1 tends to a non-zero constant. This result is used to prove the asymptotic normality of the distribution of the fractional volume of a large cube which is not covered by N interpenetrating spheres whose centres are at random, and for which NV–1 tends to a non-zero constant. The same theorem can be used to prove asymptotic normality for a large number of occupancy problems.

Large deviations of heavy-tailed random sums with applications in insurance and finance

1997 ◽  
Vol 34 (2) ◽  
pp. 293-308 ◽  
Author(s):  
C. Klüppelberg ◽  
T. Mikosch
Keyword(s):  
Distribution Function ◽  
Large Deviation ◽  
Random Variables ◽  
Compound Poisson Process ◽  
Random Sum ◽  
Random Sums ◽  
Common Distribution ◽  
Common Distribution Function ◽  
Heavy Tailed ◽  
The Right

We prove large deviation results for the random sum , , where are non-negative integer-valued random variables and are i.i.d. non-negative random variables with common distribution function F, independent of . Special attention is paid to the compound Poisson process and its ramifications. The right tail of the distribution function F is supposed to be of Pareto type (regularly or extended regularly varying). The large deviation results are applied to certain problems in insurance and finance which are related to large claims.

On the distribution of the (un)bounded sum of random variables

2011 ◽  
Vol 48 (1) ◽  
pp. 56-63 ◽  
Author(s):  
Umberto Cherubini ◽  
Sabrina Mulinacci ◽  
Silvia Romagnoli
Keyword(s):  
Random Variables ◽  
Sum Of Random Variables

scholarly journals Rigorous upper bounds on data complexities of block cipher cryptanalysis

2017 ◽  
Vol 11 (3) ◽  
Author(s):  
Subhabrata Samajder ◽  
Palash Sarkar
Keyword(s):  
Statistical Analysis ◽  
Normal Distribution ◽  
Block Cipher ◽  
Random Variables ◽  
Upper Bounds ◽  
Statistical Analyses ◽  
Data Complexity ◽  
Sum Of Random Variables ◽  
Symmetric Key

AbstractStatistical analysis of symmetric key attacks aims to obtain an expression for the data complexity which is the number of plaintext-ciphertext pairs needed to achieve the parameters of the attack. Existing statistical analyses invariably use some kind of approximation, the most common being the approximation of the distribution of a sum of random variables by a normal distribution. Such an approach leads to expressions for data complexities which are

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