scholarly journals On a stochastic order induced by an extension of Panjer’s family of discrete distributions

Metrika ◽  
2021 ◽  
Author(s):  
Aleksandr Beknazaryan ◽  
Peter Adamic
Keyword(s):  
Recursion Formula ◽  
Stochastic Order ◽  
Reliability Theory ◽  
Discrete Distributions ◽  
Compound Distributions ◽  
Space Of Distributions ◽  
Probability Mass ◽  
Mass Functions

AbstractWe factorize probability mass functions of discrete distributions belonging to Panjer’s family and to its certain extensions to define a stochastic order on the space of distributions supported on $${\mathbb {N}}_0$$ N 0 . Main properties of this order are presented. Comparison of some well-known distributions with respect to this order allows to generate new families of distributions that satisfy various recurrrence relations. The recursion formula for the probabilities of corresponding compound distributions for one such family is derived. Applications to various domains of reliability theory are provided.

scholarly journals Two Useful Discrete Distributions to Model Overdispersed Count Data

2020 ◽  
Vol 43 (1) ◽  
pp. 21-48
Author(s):  
Josmar Mazucheli ◽  
Wesley Bertoli ◽  
Ricardo Puziol Oliveira
Keyword(s):  
Infinite Series ◽  
Count Data ◽  
Survival Function ◽  
Continuous Distribution ◽  
Discrete Distributions ◽  
Mathematical Expressions ◽  
Probability Mass ◽  
The Difference ◽  
Discrete Analogues ◽  
Mass Functions

The methods to obtain discrete analogues of continuous distributions have been widely considered in recent years. In general, the discretization process provides probability mass functions that can be competitive with the traditional model used in the analysis of count data, the Poisson distribution. The discretization procedure also avoids the use of continuous distribution in the analysis of strictly discrete data. In this paper, we seek to introduce two discrete analogues for the Shanker distribution using the method of the infinite series and the method based on the survival function as alternatives to model overdispersed datasets. Despite the difference between discretization methods, the resulting distributions are interchangeable. However, the distribution generated by the method of infinite series method has simpler mathematical expressions for the shape, the generating functions and the central moments. The maximum likelihood theory is considered for estimation and asymptotic inference concerns. A simulation study is carried out in order to evaluate some frequentist properties of the developed methodology. The usefulness of the proposed models is evaluated using real datasets provided by the literature.

Uniform conditional stochastic order

1980 ◽  
Vol 17 (01) ◽  
pp. 112-123 ◽  
Author(s):  
Ward Whitt
Keyword(s):  
Probability Measure ◽  
Real Line ◽  
Standard Form ◽  
Stochastic Order ◽  
Probability Measures ◽  
Monotone Likelihood Ratio ◽  
Lifetime Distributions ◽  
Probability Mass ◽  
Mass Functions ◽  
Standard Probability

One probability measure is less than or equal to another in the sense of UCSO (uniform conditional stochastic order) if a standard form of stochastic order holds for each pair of conditional probability measures obtained by conditioning on appropriate subsets. UCSO can be applied to the comparison of lifetime distributions or the comparison of decisions under uncertainty when there may be reductions in the set of possible outcomes. When densities or probability mass functions exist on the real line, then the main version of UCSO is shown to be equivalent to the MLR (monotone likelihood ratio) property. UCSO is shown to be preserved by some standard probability operations and not by others.

Uniform conditional stochastic order

1980 ◽  
Vol 17 (1) ◽  
pp. 112-123 ◽  
Author(s):  
Ward Whitt
Keyword(s):  
Probability Measure ◽  
Real Line ◽  
Standard Form ◽  
Stochastic Order ◽  
Probability Measures ◽  
Monotone Likelihood Ratio ◽  
Lifetime Distributions ◽  
Probability Mass ◽  
Mass Functions ◽  
Standard Probability

One probability measure is less than or equal to another in the sense of UCSO (uniform conditional stochastic order) if a standard form of stochastic order holds for each pair of conditional probability measures obtained by conditioning on appropriate subsets. UCSO can be applied to the comparison of lifetime distributions or the comparison of decisions under uncertainty when there may be reductions in the set of possible outcomes. When densities or probability mass functions exist on the real line, then the main version of UCSO is shown to be equivalent to the MLR (monotone likelihood ratio) property. UCSO is shown to be preserved by some standard probability operations and not by others.

scholarly journals Stochastic methods defeat regular RSA exponentiation algorithms with combined blinding methods

2021 ◽  
Vol 15 (1) ◽  
pp. 408-433
Author(s):  
Margaux Dugardin ◽  
Werner Schindler ◽  
Sylvain Guilley
Keyword(s):  
State Of The Art ◽  
Stochastic Methods ◽  
Rsa Algorithm ◽  
Montgomery Ladder ◽  
Channel Information ◽  
Probability Mass ◽  
The Cost ◽  
Noisy Measurements ◽  
Mass Functions ◽  
Larger Sample

Abstract Extra-reductions occurring in Montgomery multiplications disclose side-channel information which can be exploited even in stringent contexts. In this article, we derive stochastic attacks to defeat Rivest-Shamir-Adleman (RSA) with Montgomery ladder regular exponentiation coupled with base blinding. Namely, we leverage on precharacterized multivariate probability mass functions of extra-reductions between pairs of (multiplication, square) in one iteration of the RSA algorithm and that of the next one(s) to build a maximum likelihood distinguisher. The efficiency of our attack (in terms of required traces) is more than double compared to the state-of-the-art. In addition to this result, we also apply our method to the case of regular exponentiation, base blinding, and modulus blinding. Quite surprisingly, modulus blinding does not make our attack impossible, and so even for large sizes of the modulus randomizing element. At the cost of larger sample sizes our attacks tolerate noisy measurements. Fortunately, effective countermeasures exist.

scholarly journals Modified Recursions for a Class of Compound Distributions

1996 ◽  
Vol 26 (2) ◽  
pp. 213-224 ◽  
Author(s):  
Karl-Heinz Waldmann
Keyword(s):  
Distribution Function ◽  
Cumulative Distribution Function ◽  
Mass Function ◽  
Cumulative Distribution ◽  
Probability Mass Function ◽  
Initial Value ◽  
Compound Distributions ◽  
Claim Frequency ◽  
Probability Mass ◽  
Stop Loss

AbstractRecursions are derived for a class of compound distributions having a claim frequency distribution of the well known (a,b)-type. The probability mass function on which the recursions are usually based is replaced by the distribution function in order to obtain increasing iterates. A monotone transformation is suggested to avoid an underflow in the initial stages of the iteration. The faster increase of the transformed iterates is diminished by use of a scaling function. Further, an adaptive weighting depending on the initial value and the increase of the iterates is derived. It enables us to manage an arbitrary large portfolio. Some numerical results are displayed demonstrating the efficiency of the different methods. The computation of the stop-loss premiums using these methods are indicated. Finally, related iteration schemes based on the cumulative distribution function are outlined.

scholarly journals Scholarly Book Review on Bayesian Statistics for Beginners: A Step-by-Step Approach

2020 ◽  
pp. 1-4
Author(s):  
Eahsan Shahriary ◽  
Amir Hajibabaee
Keyword(s):  
Bayesian Statistics ◽  
Open Source Software ◽  
Probability Density Functions ◽  
Bayesian Probability ◽  
Density Functions ◽  
Scholarly Book ◽  
Real World Applications ◽  
Probability Mass ◽  
Mass Functions

This book offers the students and researchers a unique introduction to Bayesian statistics. Authors provide a wonderful journey in the realm of Bayesian Probability and aspire readers to become Bayesian statisticians. The book starts with Introduction to Probability and covers Bayes’ Theorem, Probability Mass Functions, Probability Density Functions, The Beta-Binomial Conjugate, Markov chain Monte Carlo (MCMC), and Metropolis-Hastings Algorithm. The book is very well written, and topics are very to the point with real-world applications but does not provide examples for computing using common open-source software.

A Review of Panjer's Recursion Formula and its Applications

1995 ◽  
Vol 1 (1) ◽  
pp. 107-124 ◽  
Author(s):  
D.C.M. Dickson
Keyword(s):  
Recursion Formula ◽  
Compound Distributions ◽  
General Insurance

ABSTRACTThis paper reviews Panjer's recursion formula for evaluation of compound distributions and illustrates how it can be applied to life and general insurance problems.

Stochastic order for redundancy allocations in series and parallel systems

1992 ◽  
Vol 24 (1) ◽  
pp. 161-171 ◽  
Author(s):  
Philip J. Boland ◽  
Emad El-Neweihi ◽  
Frank Proschan
Keyword(s):  
Stochastic Order ◽  
Parallel Systems ◽  
Reliability Theory ◽  
Mathematical Statistics ◽  
In Series ◽  
Redundant Component

The problem of where to allocate a redundant component in a system in order to optimize the lifetime of a system is an important problem in reliability theory which also poses many interesting questions in mathematical statistics. We consider both active redundancy and standby redundancy, and investigate the problem of where to allocate a spare in a system in order to stochastically optimize the lifetime of the resulting system. Extensive results are obtained in particular for series and parallel systems.

Probability Mass Functions for Years to Final Separation from the Labor Force Induced by the Markov Model

2003 ◽  
Vol 16 (1) ◽  
pp. 51-86 ◽  
Author(s):  
Gary R. Skoog ◽  
James E. Ciecka
Keyword(s):  
Markov Model ◽  
Labor Force ◽  
Probability Mass ◽  
Mass Functions

Abstract No abstract available.

Joint distributions of successes, failures and patterns in enumeration problems

2000 ◽  
Vol 32 (3) ◽  
pp. 866-884 ◽  
Author(s):  
S Chadjiconstantinidis ◽  
D. L. Antzoulakos ◽  
M. V. Koutras
Keyword(s):  
Joint Distribution ◽  
Joint Probability ◽  
Fixed Number ◽  
Composite Pattern ◽  
Joint Distributions ◽  
Special Cases ◽  
Enumeration Problems ◽  
Binomial Type ◽  
Probability Mass ◽  
Mass Functions

Let ε be a (single or composite) pattern defined over a sequence of Bernoulli trials. This article presents a unified approach for the study of the joint distribution of the number Sn of successes (and Fn of failures) and the number Xn of occurrences of ε in a fixed number of trials as well as the joint distribution of the waiting time Tr till the rth occurrence of the pattern and the number STr of successes (and FTr of failures) observed at that time. General formulae are developed for the joint probability mass functions and generating functions of (Xn,Sn), (Tr,STr) (and (Xn,Sn,Fn),(Tr,STr,FTr)) when Xn belongs to the family of Markov chain imbeddable variables of binomial type. Specializing to certain success runs, scans and pattern problems several well-known results are delivered as special cases of the general theory along with some new results that have not appeared in the statistical literature before.

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