General Counting Process Research Articles

Students’ Combinatorial Thinking Error in Solving Combinatorial Problem

Combinatorial thinking errors describe students’ difficulties and obstacles in solving combinatorial problems. This study aims to describe the errors experienced by students in solving combinatorial problems in terms of combinatorial thinking processes. This research involved two subjects who were 12th grade high school students at a school in Gresik, Indonesia. The students have already taken a combinatorics course. Data collection was conducted using the think-aloud observation method and task-based interviews. Both methods of data collection were conducted to validate the data using the triangulation method. The two subjects experienced similar errors. The research shows that the filling slots method is a simple and easy way for students to understand, but problems arise when students cannot understand the meaning of the questions and input the correct numbers for the problem. The combinatorial thinking error includes the general counting process and vertical upward formulas or expressions. The general counting process error is generating a number that represents the given aspects of the problem and the vertical upward formula/expression is identifying the concept that fits the problem. This research suggests enhancing students understanding of number representation when teaching the filling slot method. The teacher should illustrate some of the multiplication rule and addition rule examples to help students distinguish between these two fundamental rules. Further research is needed to provide solutions to the constraints experienced by students in solving combinatorial problems.

Measuring tail operational risk in univariate and multivariate models with extreme losses

This paper considers some univariate and multivariate operational risk models, in which the loss severities are modeled by some weakly tail dependent and heavy-tailed positive random variables, and the loss frequency processes are some general counting processes. We derive some limit behaviors for the value-at-risk and conditional tail expectation of aggregate operational risks in such models. The methodology is based on capital approximation within the Basel II/III framework (the so-called loss distribution approach). We also conduct some simulation studies to check the accuracy of our approximations and the (in)sensitivity due to different dependence structures or to the heavy-tailedness of the severities.

The finite-time ruin probability of a risk model with a general counting process and stochastic return

<p style='text-indent:20px;'>This paper considers a general risk model with stochastic return and a Brownian perturbation, where the claim arrival process is a general counting process and the price process of the investment portfolio is expressed as a geometric Lévy process. When the claim sizes are pairwise strong quasi-asymptotically independent random variables with heavy-tailed distributions, the asymptotics of the finite-time ruin probability of this risk model have been obtained.</p>

Exponential inequalities for the supremum of some counting processes and their square martingales

We establish exponential inequalities for the supremum of martingales and square martingales obtained from counting processes, as well as for the oscillation modulus of these processes. Our inequalities, that play a decisive role in the control of errors in statistical procedures, apply to general non-explosive counting processes including Poisson, Hawkes and Cox models. Some applications for $U$-statistics are discussed.

Periodic Solutions for Stochastic Differential Equations Driven by General Counting Processes: Application to Malaria

In this paper, a class of periodic stochastic differential equations driven by general counting processes (SDEsGp) is studied. First, an existence-uniqueness result for the solution of general SDEsGp based on Poisson processes with т-periodic stochastic intensity of time t has been given, for some  т> 0. Then, using the properties of periodic Markov processes, sufficient conditions for the existence and uniqueness of a periodic solution of the considered equations are obtained. We will then apply the obtained results to the propagation of malaria in a periodic environment.

Uniform asymptotics for ruin probabilities of a non standard bidimensional perturbed risk model with subexponential claims

In this paper, we consider three types of finite-time ruin probabilities for a non standard bidimensional risk model perturbed by diffusion. In this model, it is assumed that the two claim-arrival processes are general counting processes and arbitrarily dependent. Moreover, the two classes of claim sizes are dependent according to a certain structure proposed in Ko and Tang (Journal of Applied Probability 45:5–95, 2008). When the claim sizes are assumed to be subexponential, we derive three uniformly asymptotic formulas for finite-time ruin probabilities over a finite interval of time horizon. The obtained results extend some existing ones in the literature.

Asymptotics for VaR and CTE of total aggregate losses in a bivariate operational risk cell model

<p style='text-indent:20px;'>This paper considers a bivariate operational risk cell model, in which the loss severities are modelled by some heavy-tailed and weakly (or strongly) dependent nonnegative random variables, and the frequency processes are described by two arbitrarily dependent general counting processes. In such a model, we establish some asymptotic formulas for the Value-at-Risk and Conditional Tail Expectation of the total aggregate loss. Some simulation studies are also conducted to check the accuracy of the obtained theoretical results via the Monte Carlo method.</p>

Measuring Tail Operational Risk in Univariate and Multivariate Models under Extreme Losses

This paper considers some univariate and multivariate operational risk models, in which the loss severities are modelled by some weakly tail dependent and heavy-tailed positive random variables, and the loss frequency processes are some general counting processes. In such models, we derive some limit behaviors for the Value-at-Risk and Conditional Tail Expectation of aggregate operational risks. The methodology is based on capital approximation within the framework of the Basel II/III regulatory capital accords, which is the so-called Loss Distribution Approach. We also conduct some simulation studies to check the accuracy of our obtained approximations and the (in)sensitivity due to different dependence structures or the heavy-tailedness of the severities.

STOCHASTIC VOLATILITY MODEL WITH CORRELATED JUMP SIZES AND INDEPENDENT ARRIVALS

In light of recent empirical research on jump activity, this article study the calibration of a new class of stochastic volatility models that include both jumps in return and volatility. Specifically, we consider correlated jump sizes and both contemporaneous and independent arrival of jumps in return and volatility. Based on the specifications of this model, we derive a closed-form relationship between the VIX index and latent volatility. Also, we propose a closed-form logarithmic likelihood formula by using the link to the VIX index. By estimating alternative models, we find that the general counting processes setting lead to better capturing of return jump behaviors. That is, the part where the return and volatility jump simultaneously and the part that jump independently can both be captured. In addition, the size of the jumps in volatility is, on average, positive for both contemporaneous and independent arrivals. However, contemporaneous jumps in the return are negative, but independent return jumps are positive. The sub-period analysis further supports above insight, and we find that the jumps in return and volatility increased significantly during the two recent economic crises.

A martingale view of Blackwell’s renewal theorem and its extensions to a general counting process

AbstractMartingales constitute a basic tool in stochastic analysis; this paper considers their application to counting processes. We use this tool to revisit a renewal theorem and give extensions for various counting processes. We first consider a renewal process as a pilot example, deriving a new semimartingale representation that differs from the standard decomposition via the stochastic intensity function. We then revisit Blackwell’s renewal theorem, its refinements and extensions. Based on these observations, we extend the semimartingale representation to a general counting process, and give conditions under which asymptotic behaviour similar to Blackwell’s renewal theorem holds.

Asymptotics for the finite-time ruin probability of a risk model with a general counting process

This paper considers a general risk model. In this risk model there does not need independent or dependent assumption on the inter-arrival times. When the claim sizes have the WUOD structure, the asymptotics of the finite-time ruin probability of this risk model have been given. The obtained results extend and improve some existed results.

Reliability analysis of processes with moving cracked material

The reliability of processes with moving elastic and isotropic material containing initial cracks is considered in terms of fracture. The material is modelled as a moving plate which is simply supported from two of its sides and subjected to homogeneous tension acting in the travelling direction. For tension, two models are studied: (i) tension is constant with respect to time, and (ii) tension varies temporally according to an Ornstein–Uhlenbeck process. Cracks of random length are assumed to occur in the material according to a stochastic counting process. For a general counting process, a representation of the nonfracture probability of the system is obtained that exploits conditional Monte Carlo simulation. Explicit formulae are derived for special cases. To study the reliability of the system with temporally varying tension, a known explicit result for the first passage time of an Ornstein–Uhlenbeck process to a constant boundary is utilised. Numerical examples are provided for printing presses and paper material.

The DFR Property for Counting Processes Stopped at an Independent Random Time

In this paper we consider general counting processes stopped at a random time T, independent of the process. Provided that T has the decreasing failure rate (DFR) property, we present sufficient conditions on the arrival times so that the number of events occurring before T preserves the DFR property of T. In particular, when the interarrival times are independent, we consider applications concerning the DFR property of the stationary number of customers waiting in queue for specific queueing models.

The DFR Property for Counting Processes Stopped at an Independent Random Time

In this paper we consider general counting processes stopped at a random timeT, independent of the process. Provided thatThas the decreasing failure rate (DFR) property, we present sufficient conditions on the arrival times so that the number of events occurring beforeTpreserves the DFR property ofT. In particular, when the interarrival times are independent, we consider applications concerning the DFR property of the stationary number of customers waiting in queue for specific queueing models.

Stochastic comparison of generalized combined risk processes

Usually, two different types of shock models (extreme and cumulative shock models) are employed to model the dynamic risk processes. In extreme shock models, only the impact of the current fatal shock is usually taken into account, whereas, in cumulative shock models, the impact of the preceding shocks is accumulated as well. However, in practice, the effect of the corresponding shock can be realized in those two ways in one model (i.e., it can be fatal or, otherwise it is accumulated). This observation justifies the consideration of a ‘combined shock model’ in the risk modeling and analysis. In this paper, we generalize the study of the dynamic risk processes that were previously considered in the literature. The main theme of this paper is to find the optimal allocation policies for the generalized combined risk processes via the stochastic comparisons of survival functions. It will be seen that the obtained results hold for ‘general counting processes’ of shocks. In addition, we consider the problem of maximizing a gain function under certain risks and obtain reasonable decisions based on a variability measure. Furthermore, the meaningful explanations for the results on the policy ordering will be provided.

Hazard Rate Properties of a General Counting Process Stopped at an Independent Random Time

In this work we provide sufficient conditions under which a general counting process stopped at a random time independent from the process belongs to the reliability decreasing reversed hazard rate (DRHR) or increasing failure rate (IFR) class. We also give some applications of these results in generalized renewal and trend renewal processes stopped at a random time.

Hazard Rate Properties of a General Counting Process Stopped at an Independent Random Time

In this work we provide sufficient conditions under which a general counting process stopped at a random time independent from the process belongs to the reliability decreasing reversed hazard rate (DRHR) or increasing failure rate (IFR) class. We also give some applications of these results in generalized renewal and trend renewal processes stopped at a random time.

On the time spent below a random threshold by a system driven by a general counting process

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On the time spent below a random threshold by a system driven by a general counting process

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

On increasing-failure-rate random variables

We provide sufficient conditions for the following types of random variable to have the increasing-failure-rate (IFR) property: sums of a random number of random variables; the time at which a Markov chain crosses a random threshold; the time until a random number of events have occurred in an inhomogeneous Poisson process; and the number of events of a renewal process, and of a general counting process, that have occurred by a randomly distributed time.