On Infinitely Divisible Exponential Dispersion Model Related to Poisson-Exponential Distribution

We construct a univariate exponential dispersion model comprised of discrete infinitely divisible distributions. This model emerges in the theory of branching processes. We obtain a representation for the Lévy measure of relevant distributions and characterize their laws as Poisson mixtures and/or compound Poisson distributions. The regularity of the unit variance function of this model is employed for the derivation of approximations by the Poisson-exponential model. We emphasize the role of the latter class. We construct local approximations relating them to properties of special functions and branching diffusions.     

Estimation in second order branching processes with application to swine flu data

Abstract

This paper discusses inferential issues related to estimation of offspring mean and variance in a second order branching process, when both the offspring distributions are assumed to have identical mean and variance. Estimating equation approach is used to find the estimator of the offspring mean and the fact that a second order branching process model can be modeled as an autoregressive process is utilized to obtain the estimator of the offspring variance. Both the estimators are shown to be consistent and asymptotically normal. The second order branching process model is applied to H1N1 data for Pune, India, and Mexico and is found to be a suitable model. The estimates obtained from this model are used to compute the proportion of vaccination required for elimination of the disease.     

A nonparametric estimation procedure for the Hawkes process: comparison with maximum likelihood estimation

In earlier work, Kirchner [An estimation procedure for the Hawkes process. Quant Financ. 2017;17(4):571–595], we introduced a nonparametric estimation method for the Hawkes point process. In this paper, we present a simulation study that compares this specific nonparametric method to maximum-likelihood estimation. We find that the standard deviations of both estimation methods decrease as power-laws in the sample size. Moreover, the standard deviations are proportional. For example, for a specific Hawkes model, the standard deviation of the branching coefficient estimate is roughly 20% larger than for MLE – over all sample sizes considered. This factor becomes smaller when the true underlying branching coefficient becomes larger. In terms of runtime, our method clearly outperforms MLE. The present bias of our method can be well explained and controlled. As an incidental finding, we see that also MLE estimates seem to be significantly biased when the underlying Hawkes model is near criticality. This asks for a more rigorous analysis of the Hawkes likelihood and its optimization.     

A Note on Estimation of Parameters for Branching Processes with Immigration

It is proved that under certain conditions the conditional least-squares estimator of the offspring mean for a sequence of nearly critical Ji?ina processes with immigration is consistent but not asymptotically normal and the conditional least-squares estimator of the immigration mean is not consistent. These results differ from the existing results in the case where the initial values are zero (see Ispány et al., 2005 Ispány , M. , Pap , G. , Zuijlen , M. V. ( 2005 ). Fluctuation limit of branching processes with immigration and estimation of the means . Adv. Appl. Probab. 37 : 523 – 538 . [Google Scholar]).     

Efficient sequential estimation in a markov branching process with immigration

Sequential estimation of parameters In a continuous time Markov branching process with Immigration with split rate λ₁ Immigration rate λ₂, offspring distribution {p₁j≥O) and Immigration distribution {p₂j≥l} is considered. A sequential version of the Cramér-Rao type information inequality is derived which gives a lower bound on the variances of unbiased estimators for any function of these parameters. Attaining the lower bounds depends on whether the sampling plan or stopping rule S, the estimator f, and the parametric function g = E(f) are efficient. All efficient triples (S,f,g) are characterized; It Is shown that for i = 1,2, only linear combinations of λ_ip_{ij j}'s or their ratios are efficiently estimable. Applications to a Yule process, a linear birth and death process with immigration and an M/M/∞ queue are also considered     

On semi-markov compartmental models with branching particles under the influence of disasters

A semi-Markov multi-compart mental system in which particles reproduce similar particles as a Markov branching process and being subjected to disasters is studied. Expressions for the mean number of particles alive at time t in each compartment are obtained. The results concerning irreversible, mammillarian and catenary compartmental systems have been discussed.     

Quasi- and pseudo-maximum likelihood estimators for discretely observed continuous-time Markov branching processes

This article deals with quasi- and pseudo-likelihood estimation for a class of continuous-time multi-type Markov branching processes observed at discrete points in time. “Conventional” and conditional estimation are discussed for both approaches. We compare their properties and identify situations where they lead to asymptotically equivalent estimators. Both approaches possess robustness properties, and coincide with maximum likelihood estimation in some cases. Quasi-likelihood functions involving only linear combinations of the data may be unable to estimate all model parameters. Remedial measures exist, including the resort either to non-linear functions of the data or to conditioning the moments on appropriate sigma-algebras. The method of pseudo-likelihood may also resolve this issue. We investigate the properties of these approaches in three examples: the pure birth process, the linear birth-and-death process, and a two-type process that generalizes the previous two examples. Simulations studies are conducted to evaluate performance in finite samples.     

On the genealogy and coalescence times of Bienaymé–Galton–Watson branching processes

Coalescence processes have received a lot of attention in the context of conditional branching processes with fixed population size and non-overlapping generations. Here, we focus on similar problems in the context of the standard unconditional Bienaymé–Galton–Watson branching processes, either (sub)-critical or supercritical. Using an analytical tool, we derive the structure of some counting aspects of the ancestral genealogy of such processes, including: the transition matrix of the ancestral count process and an integral representation of various coalescence times distributions, such as the time to most recent common ancestor of a random sample of arbitrary size, including full size. We illustrate our results on two important examples of branching mechanisms displaying either finite or infinite reproduction mean, their main interest being to offer a closed form expression for their probability generating functions at all times. Large time behaviors are investigated.     

Bayesian prediction of waiting times in stochastic models

The authors show how saddlepoint techniques lead to highly accurate approximations for Bayesian predictive densities and cumulative distribution functions in stochastic model settings where the prior is tractable, but not necessarily the likelihood or the predictand distribution. They consider more specifically models involving predictions associated with waiting times for semi‐Markov processes whose distributions are indexed by an unknown parameter θ. Bayesian prediction for such processes when they are not stationary is also addressed and the inverse‐Gaussian based saddlepoint approximation of Wood, Booth & Butler (1993) is shown to accurately deal with the nonstationarity whereas the normal‐based Lugannani & Rice (1980) approximation cannot, Their methods are illustrated by predicting various waiting times associated with M/M/q and M/G/1 queues. They also discuss modifications to the matrix renewal theory needed for computing the moment generating functions that are used in the saddlepoint methods.     

Modelling Variability in the Branching Structure of Strawberry Inflorescences

The branching structure of inflorescences of the cultivated strawberry ( Fragaria × ananassa Duch.) is very variable. This paper demonstrates that some aspects of this variability are well described by a simple stochastic model of branching that has two adjustable parameters. The model is shown to provide a good fit to data from a set of almost 700 inflorescences of the cultivar Elsanta, collected over two successive years. For one parameter the maximum likelihood estimator is a moment estimator which is fully efficient even if the detailed branching structure of the inflorescences is not recorded. This parameter provides a convenient summary of branching vigour. The maximum likelihood estimator of the second parameter must be determined iteratively and can be quite inefficient unless the full branching structure is recorded. The model demonstrates that branching structure is affected by the order in which inflorescences emerge on the plant.     

Estimation of probability of extinction of a multitype branchihg process

Maximoa likelihood estimation of the probability of ultimata extinction of a possibly age dependentaultitype branching process is studied when independent random samples from off spring distributions are available, In multitype daIton-fatson branching process     

Non parametric confidence intervals for the extinction probability of a Galton–Watson branching process

It is demonstrated that the confidence intervals (CIs) for the probability of eventual extinction and other parameters of a Galton–Watson branching process based upon the maximum likelihood estimators can often have substantially lower coverage when compared to the desired nominal confidence coefficient, especially in small, more realistic sample sizes. The same conclusion holds for the traditional bootstrap CIs. We propose several adjustments to these CIs, which greatly improves coverage in most cases. We also make a correction in an asymptotic variance formula given in Stigler (1971 Stigler, S.M. (1971). The estimation of the probability of extinction and other parameters associated with branching processes. Biometrika 58(3):499–508.[Crossref], [Web of Science ®] , [Google Scholar]). The focus here is on implementation of the CIs which have good coverage, in a wide variety of cases. We also consider expected CI lengths. Some recommendations are made.     

Non-ergodic martingale estimating functions and related asymptotics

Well-known estimation methods such as conditional least squares, quasilikelihood and maximum likelihood (ML) can be unified via a single framework of martingale estimating functions (MEFs). Asymptotic distributions of estimates for ergodic processes use constant norm (e.g. square root of the sample size) for asymptotic normality. For certain non-ergodic-type applications, however, such as explosive autoregression and super-critical branching processes, one needs a random norm in order to get normal limit distributions. In this paper, we are concerned with non-ergodic processes and investigate limit distributions for a broad class of MEFs. Asymptotic optimality (within a certain class of non-ergodic MEFs) of the ML estimate is deduced via establishing a convolution theorem using a random norm. Applications to non-ergodic autoregressive processes, generalized autoregressive conditional heteroscedastic-type processes, and super-critical branching processes are discussed. Asymptotic optimality in terms of the maximum random limiting power regarding large sample tests is briefly discussed.     

Probabilistic Approach to Solution of the Neumann Problem for Some Nonlinear Equation

In this article we will consider the Neumann boundary-value problem for the nonlinear Helmholtz equation ? Δ?u + a?u = gexp?(u) + f₀. We will assume that there exists the solution to our problem and this permits us to construct an unbiased estimator on the trajectories of certain branching processes. To do so, we apply Green’s formula and an elliptic mean value theorem. This allows us to derive a special integral equation that gives the value of the function u(x) at the point x, with its integral over the domain D and on boundary of the domain ?D = G. The solution of the problem in the form of a mathematical expectation of some random variable is also obtained. In accordance with the probabilistic representation, a branching process is constructed and an unbiased estimator of the solution of the problem is built on its trajectories. The derived unbiased estimator has finite variance. The proposed branching process has a finite average number of branches, and easily simulated. We provide numerical results based on numerical experiments carried out with these algorithms.     

CHARACTERIZATION OF EFFICIENT SEQUENTIAL SCHEMES FOR POPULATION GROWTH MODELS1

For population growth models, which include standard processes like Poisson, birth, birth-death, immigration-death, simple epidemic and M/M/1 queue as particular cases, we obtain efficient sequential schemes and characterize all efficiently estimable functions of the parameters by first deriving a sequential version of the information inequality.     

Some aspects of modern population mathematics

The purpose of this paper is to survey a number of the technical tools and models that have found use in the study of human and other populations, and to indicate some problems of current interest. These tools and models are varied: integral equations, nonlinear oscillations, differential geometry, dynamical systems, nonlinear operations, bifurcation theory, semigroup theory, martingale theory, Markov processes, diffusion processes, branching processes, ergodic theory, prediction theory and state-space models. A fairly extensive bibliography is provided. Also an Appendix has been added describing the analysis of a classical entomological data set.     

Non-parametric Bayesian inference through MCMC method for Y-linked two-sex branching processes with blind choice

ABSTRACT

Dirichlet-process-based non-parametric Bayesian inference is developed for a Y-linked two-sex branching process with blind choice. This stochastic model is suitable for analysing the evolution of the number of carriers of two alleles of a Y-linked gene in a two-sex monogamous population where each female chooses her partner from among the male population without caring about his type (i.e. the allele he carries). The only data assumed to be available are the total number of females and males (regardless of their types) up to some generation and the numbers of each type of male in the last generation. A simulation method which is based on a Dirichlet process and a Gibbs sampler is developed to estimate the posterior distributions of the model's main parameters. Finally, the computational efficiency of the algorithm is illustrated with example simulations and an application to real data.     

Estimation for Continuous Branching Processes

The maximum-likelihood estimator for the curved exponential family given by continuous branching processes with immigration is investigated. These processes originated from population biology but also model the dynamics of interest rates and development of the state of technology in economics. It is proved that in contrast to branching processes with discrete space and/or time the MLE gives a unified approach to the inference. In order to include singular subdomains of the parameter space we modify the MLE slightly. Consistency and asymptotic normality for the MLE are considered. Concerning the asymptotic theory of the experiments, all three properties LAQ, LAN, and LAMN occur for different submodels     

APPROXIMATION OF EXCEEDANCE PROCESSES IN LARGE POPULATIONS

We consider a population of n individuals. Each of these individuals generates a discrete time branching stochastic process. We study the number of ancestors S(n,t) whose offspring at time t exceeds level θ(t), where θ(t) is some positive valued function. It is proved that S(n,t) may be approximated as t → ∞ and n → ∞ by some stochastic processes with independent increments.

     

Efficient sequential estimation in multiplicative counting processes

The problem of efficient sequential estimation is counting processes with multiplicative intensity processes is considered. A sequential version of Cramér-Rao type information inequality is obtained and all the 'efficient' triples (S, f, g) are characterized: the variance of an unbiased estimator f for g attains the lower bound under a sampling plan S. Applications to Poisson processes, Markov processes, birth and death processes and Markov branching processes with immigration are also considered.