scholarly journals A new trivariate model for stochastic episodes

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Francesco Zuniga ◽  
Tomasz J. Kozubowski ◽  
Anna K. Panorska
Keyword(s):  
Mixed Type ◽  
Geometric Distribution ◽  
Integral Transforms ◽  
Conditional Distributions ◽  
Actuarial Science ◽  
Multivariate Distributions ◽  
Weather And Climate ◽  
Exponential Random Variables ◽  
Stochastic Events ◽  
Stochastic Representations

AbstractWe study the joint distribution of stochastic events described by (X,Y,N), where N has a 1-inflated (or deflated) geometric distribution and X, Y are the sum and the maximum of N exponential random variables. Models with similar structure have been used in several areas of applications, including actuarial science, finance, and weather and climate, where such events naturally arise. We provide basic properties of this class of multivariate distributions of mixed type, and discuss their applications. Our results include marginal and conditional distributions, joint integral transforms, moments and related parameters, stochastic representations, estimation and testing. An example from finance illustrates the modeling potential of this new model.

scholarly journals A general stochastic model for bivariate episodes driven by a gamma sequence

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Charles K. Amponsah ◽  
Tomasz J. Kozubowski ◽  
Anna K. Panorska
Keyword(s):  
Stochastic Model ◽  
Joint Distribution ◽  
Pareto Distribution ◽  
Integral Transforms ◽  
Bivariate Distribution ◽  
Conditional Distributions ◽  
General Stochastic Model ◽  
Heavy Tailed ◽  
Discrete Pareto Distribution ◽  
Special Case

AbstractWe propose a new stochastic model describing the joint distribution of (X,N), where N is a counting variable while X is the sum of N independent gamma random variables. We present the main properties of this general model, which include marginal and conditional distributions, integral transforms, moments and parameter estimation. We also discuss in more detail a special case where N has a heavy tailed discrete Pareto distribution. An example from finance illustrates the modeling potential of this new mixed bivariate distribution.

scholarly journals BAYESIAN INFERENCE FOR THE TANGENT PORTFOLIO

2018 ◽  
Vol 21 (08) ◽  
pp. 1850054 ◽  
Author(s):  
DAVID BAUDER ◽  
TARAS BODNAR ◽  
STEPAN MAZUR ◽  
YAREMA OKHRIN
Keyword(s):  
Bayesian Inference ◽  
Point Of View ◽  
Posterior Distributions ◽  
Conditional Distributions ◽  
Linear Combinations ◽  
Conjugate Priors ◽  
Mean And Variance ◽  
The Mean ◽  
Stochastic Representations ◽  
Mean Vector

In this paper, we consider the estimation of the weights of tangent portfolios from the Bayesian point of view assuming normal conditional distributions of the logarithmic returns. For diffuse and conjugate priors for the mean vector and the covariance matrix, we derive stochastic representations for the posterior distributions of the weights of tangent portfolio and their linear combinations. Separately, we provide the mean and variance of the posterior distributions, which are of key importance for portfolio selection. The analytic results are evaluated within a simulation study, where the precision of coverage intervals is assessed.

Seasonal Modeling of Multivariate Distributions of Metocean Parameters With Application to Marine Operations

2004 ◽  
Vol 126 (3) ◽  
pp. 202-212 ◽  
Author(s):  
Se´bastien Fouques ◽  
Dag Myrhaug ◽  
Finn Gunnar Nielsen
Keyword(s):  
Hermite Polynomials ◽  
Statistical Information ◽  
Conditional Distributions ◽  
Multivariate Distributions ◽  
Sea State ◽  
Joint Distributions ◽  
The Mean ◽  
Wave Conditions ◽  
Offshore Activities ◽  
Joint Occurrence

Statistical information about the joint occurrence of metocean parameters is of importance for many offshore activities. For instance, in marine operations, environmental limitations may be brought about by both wind and wave conditions. Thus, knowledge of their joint occurrence is important as the persistence duration (i.e., the duration of the sea state persistence above or beneath a given level) and the seasonal dependence of wind and waves appear to be of large interest. However, such a modeling becomes difficult as the number of considered variables increases, especially when utilizing a common parameterization of some conditional distributions. This paper proposes a general methodology that aims at modeling seasonal joint distributions of n such parameters from their correlation structure and the n marginal distributions fitted by generalized gamma ones. Two methods are proposed in order to derive an approximate joint distribution from the modeled margins. The first one matches the correlation matrix only, whereas the second one, which is based on a multivariate Hermite polynomials expansion of the multinormal distribution, is able to match joint moments of order higher than two. However, more restrictive conditions are shown by the latter. An application to the simple example of the joint occurrence of significant wave height and the mean wind velocity at the 10m elevation is used to illustrate the methods. Eventually, examples of applications like simultaneous persistence of wind and wave conditions as well as seastate forecasting from statistics are given.

EQUIVALENT CHARACTERIZATIONS ON ORDERINGS OF ORDER STATISTICS AND SAMPLE RANGES

2010 ◽  
Vol 24 (2) ◽  
pp. 245-262 ◽  
Author(s):  
Tiantian Mao ◽  
Taizhong Hu
Keyword(s):  
Order Statistics ◽  
Exponential Distribution ◽  
Geometric Distribution ◽  
Random Variables ◽  
The Other ◽  
Stochastic Orders ◽  
Exponential Random Variables ◽  
Discrete Counterpart ◽  
Similarities And Differences ◽  
Geometric Variables

The purpose of this article is to present several equivalent characterizations of comparing the largest-order statistics and sample ranges of two sets of n independent exponential random variables with respect to different stochastic orders, where the random variables in one set are heterogeneous and the random variables in the other set are identically distributed. The main results complement and extend several known results in the literature. The geometric distribution can be regarded as the discrete counterpart of the exponential distribution. We also study the orderings of the largest-order statistics from geometric random variables and point out similarities and differences between orderings of the largest-order statistics from geometric variables and from exponential variables.

Seasonal Modelling of Multivariate Distributions of Metocean Parameters

2002 ◽  
Author(s):  
Se´bastien Fouques ◽  
Dag Myrhaug ◽  
Finn Gunnar Nielsen
Keyword(s):  
Correlation Matrix ◽  
Hermite Polynomials ◽  
Statistical Information ◽  
Conditional Distributions ◽  
Multivariate Distributions ◽  
Joint Distributions ◽  
The Mean ◽  
Wave Conditions ◽  
Offshore Activities ◽  
Joint Occurrence

Statistical information about the joint occurrence of metocean parameters is of importance for many offshore activities. For instance, in marine operations, environmental limitations may be brought about by both wind and wave conditions. Thus, knowledge of their joint occurrence is important as the persistence duration and the seasonal dependence of wind and waves appear to be of large interest. However, such a modelling becomes difficult as the number of considered variables increases, especially when utilizing a common parameterization of some conditional distributions. This paper proposes a general methodology that aims at modelling seasonal joint distributions of n such parameters from their correlation structure and the n marginal distributions fitted by generalized gamma ones. Two methods are proposed in order to derive an approximate joint distribution from the modelled margins. The first one matches the correlation matrix only, whereas the second one, which is based on a multivariate Hermite polynomials expansion of the multinormal distribution, is able to match joint moments of order higher than two. However, more restrictive conditions are shown by the latter. An application to the simple example of the joint occurrence of significant wave height and the mean wind velocity at the 10m elevation is used to illustrate the methods. Eventually, examples of applications like simultaneous persistence of wind and wave conditions as well as seastate forecasting from statistics are given.

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