The Explicit Computations of the Bergman Kernels on Generalized Hua Domains

The Bergman kernel function plays an important role in several complex variables. Thereexists the Bergman kernel function on any bounded domain in Cn. But we can get the Bergmankernel functions in explicit formulas for a few types of domains only, for instance, the boundedhomogeneous domains and the egg domains in some cases.Yin Weiping defined four types of generalized Hua domainsGHE1(N1,… Nr; m, n,p1…,pr, k)where wj = (wi1,…, wjNj ),j = 1, 2,…, r. RI(m, n), RⅡ(p), RⅢ(q), and RⅣ…     

Different Behaviour for the Solutions of Some Chemotaxis Equations

In biology, it is very important to investigate the movement of some cells ororganisms in some given biological system (cf. ［1, 2］). In order to understand howthe movement rules are affected by the effect of the chemo-attractant, Othmer and Stevens introduced in ［1］ several general classes of partial differential equations. In one of their models, they considered a master equation, i.e. barrier and nearestneighbor lattice model. Following a limiting process the model is described by the following system of partial differential equations:     

Singularly Perturbed Switching Diffusions: Rapid Switchings and Fast Diffusions

Motivated by many problems in optimization and control, this paper is concerned with singularly perturbed systems involving both diffusions and pure jump processes. Two models are treated. In the first model, the jump process changes very rapidly by comparison with the diffusion processes. In the second model, the diffusions change rapidly in comparison with the jump process. Asymptotic expansions are developed for the transition density vectors via a constructive method; justification of the asymptotic expansions and analysis of the remainders are provided.     

Stochastic Generalized Method of Moments

The generalized method of moments (GMM) is a very popular estimation and inference procedure based on moment conditions. When likelihood-based methods are difficult to implement, one can often derive various moment conditions and construct the GMM objective function. However, minimization of the objective function in the GMM may be challenging, especially over a large parameter space. Due to the special structure of the GMM, we propose a new sampling-based algorithm, the stochastic GMM sampler, which replaces the multivariate minimization problem by a series of conditional sampling procedures. We develop the theoretical properties of the proposed iterative Monte Carlo method, and demonstrate its superior performance over other GMM estimation procedures in simulation studies. As an illustration, we apply the stochastic GMM sampler to a Medfly life longevity study. Supplemental materials for the article are available online.