基于联合估计方法的广义随机系数自回归模型的统计推断

本文利用联合估计函数方法(CEF)对广义随机系数自回归(GRCA)模型进行统计研究.应用联合估计函数方法得到广义随机系数自回归模型参数估计量,证明了提出的参数估计量的相合性和渐近正态性,利用数值模拟对提出的参数统计量进行对比分析,数值模拟结果表明,联合估计方法的参数估计量优于基于估计函数方法、伪极大似然方法、最小二乘方法的参数估计量,实证研究也说明CEF方法具有较好的效果.     

局部平稳短期利率扩散模型的半参数估计

主要研究局部平稳扩散模型的半参数估计．首先,基于局部常数拟合,利用局部加权最小二乘法得到了漂移参数函数的估计量．同时,通过Kolmogorov向前方程,得到了扩散函数的估计量．然后,分别讨论了所得估计量的相合性和渐近正态性．最后,通过模拟研究说明了估计量的有效性．     

变系数EV模型基于核估计的误差方差估计

在利用核函数法和广义最小二乘法讨论变系数EV模型系数参数估计的基础上给出了其误差方差σ2的一种估计量(σ)2n,证明了所定义的估计量(σ)2n有很好的大样本性质.     

部分线性自回归模型中回归函数的半参数估计（英文）

本文主要研究具有一阶自回归误差的三阶部分线性自回归模型中回归函数的半参数估计问题.假定回归函数来自某个参数分布族,利用条件最小二乘法得到参数估计量,再结合非参数核函数进行调整,给出回归函数的半参数估计量.并在一定条件下,证明了估计量具有相合性.最后,通过模拟研究验证了此方法的有效性.     

协变量缺失情形下的逆概率加权众数回归估计

数据缺失在实际应用中普遍存在,数据缺失会降低研究效率,导致参数估计有偏.在协变量随机缺失(MAR)的假定下,本文基于众数回归和逆概率加权估计方法对线性模型进行参数估计.该方法结合参数Logistic回归和非参数Nadaraya-Watson估计两种倾向得分估计方法,分别构建IPWM-L估计量和IPWM-NW估计量.模拟研究和实例分析表明,众数回归模型比均值回归模型更具稳健性,逆概率加权众数(IPWM)估计方法在缺失数据下表现出了更好的拟合效果,与IPWM-L估计量相比, IPWM-NW估计量更稳健.     

扩散函数非参数估计的一种新方法

考虑了一类基于样本插值的时齐扩散方程扩散函数的非参数估计程序.在一定的正则条件下,给出的扩散函数估计量是依概率收敛的,并且渐近地符合一个正态分布.通过分析,发现与传统的基于已实现波动率的估计量相比,估计量在精度上有所提高.     

测量误差模型的自适应LASSO变量选择方法研究

本文研究测量误差模型的自适应LASSO(least absolute shrinkage and selection operator)变量选择和系数估计问题.首先分别给出协变量有测量误差时的线性模型和部分线性模型自适应LASSO参数估计量,在一些正则条件下研究估计量的渐近性质,并且证明选择合适的调整参数,自适应LASSO参数估计量具有oracle性质.其次讨论估计的实现算法及惩罚参数和光滑参数的选择问题.最后通过模拟和一个实际数据分析研究了自适应LASSO变量选择方法的表现,结果表明,变量选择和参数估计效果良好.     

多类型复发间隔时间下广义半参数风险模型

本文在多类型复发间隔时间数据下,研究了一类广义半参数风险回归模型的参数估计问题,给出了该模型中未知参数和非参数函数的一种估计方法,并证明了估计量的相合性和渐近正态性.最后利用数值模拟来评估估计量在有限样本下的表现.     

扩散系数小波估计的强相合性

本文研究了一维扩散方程中扩散系数的非参数估计,给出了有界Lipschitz扩散系数的线性小波估计,证明了所得估计量的强相合性.     

扩散系数小波估计的强相合性

本文研究了一维扩散方程中扩散系数的非参数估计,给出了有界Lipschitz扩散系数的线性小波估计,证明了所得估计量的强相合性.     

Wavelet-based estimator for the hurst parameters of fractional brownian sheet

It is proposed a class of statistical estimators H =(H_1,…,H_d) for the Hurst parameters H =(H_1,…,H_d) of fractional Brownian field via multi-dimensional wavelet analysis and least squares,which are asymptotically normal.These estimators can be used to detect self-similarity and long-range dependence in multi-dimensional signals,which is important in texture classification and improvement of diffusion tensor imaging(DTI) of nuclear magnetic resonance(NMR).Some fractional Brownian sheets will be simulated and the simulated data are used to validate these estimators.We find that when H_i ≥ 1/2,the estimators are accurate,and when H_i 1/2,there are some bias.     

Parameter estimation for fractional Ornstein–Uhlenbeck processes at discrete observation

This paper deals with the problem of estimating the parameters for fractional Ornstein–Uhlenbeck processes from discrete observations when the Hurst parameter H is known. Both the drift and the diffusion coefficient estimators of discrete form are obtained based on approximating integrals via Riemann sums with Hurst parameter H ∈ (1/2, 3/4). By adapting the stochastic integral representation to the fractional Brownian motion, these two estimators can be efficiently computed by the use of computer software. Numerical examples are presented to examine the performance of our method. An application to real data is also presented to show how to apply this method in practice.     

Error estimators for advection–reaction–diffusion equations based on the solution of local problems

This paper deals with a posteriori error estimates for advection–reaction–diffusion equations. In particular, error estimators based on the solution of local problems are derived for a stabilized finite element method. These estimators are proved to be equivalent to the error, with equivalence constants eventually depending on the physical parameters. Numerical experiments illustrating the performance of this approach are reported.     

Estimation of cusp location of stochastic processes: a survey

We present a review of some recent results on estimation of location parameter for several models of observations with cusp-type singularity at the change point. We suppose that the cusp-type models fit better to the real phenomena described usually by change point models. The list of models includes Gaussian, inhomogeneous Poisson, ergodic diffusion processes, time series and the classical case of i.i.d. observations. We describe the properties of the maximum likelihood and Bayes estimators under some asymptotic assumptions. The asymptotic efficiency of estimators are discussed as well and the results of some numerical simulations are presented. We provide some heuristic arguments which demonstrate the convergence of log-likelihood ratios in the models under consideration to the fractional Brownian motion.     

Modeling non-Darcian flow and solute transport in porous media with the Caputo–Fabrizio derivative

In this study, the non-Darcian flow and solute transport in porous media are modeled with a revised Caputo derivative called the Caputo–Fabrizio fractional derivative. The fractional Swartzendruber model is proposed for the non-Darcian flow in porous media. Furthermore, the normal diffusion equation is converted into a fractional diffusion equation in order to describe the diffusive transport in porous media. The proposed Caputo–Fabrizio fractional derivative models are addressed analytically by applying the Laplace transform method. Sensitivity analyses were performed for the proposed models according to the fractional derivative order. The fractional Swartzendruber model was validated based on experimental data for water flows in soil–rock mixtures. In addition , the fractional diffusion model was illustrated by fitting experimental data obtained for fluid flows and chloride transport in porous media. Both of the proposed fractional derivative models were highly consistent with the experimental results.     

Estimation of parameters in the fractional compound Poisson process

This paper discusses method-of-moments estimators for parameters in the fractional compound Poisson process and establishes asymptotic normality of estimators. Simulation are presented to illustrate the properties of the estimators.     

On identification of the threshold diffusion processes

We consider the problems of parameter estimation for several models of threshold ergodic diffusion processes in the asymptotics of large samples. These models are the direct continuous time analogues of the well known in time series analysis threshold autoregressive models. In such models, the trend is switching when the observed process attaints some (unknown) values and the problem is to estimate it or to test some hypotheses concerning these values. The related statistical problems correspond to the singular estimation or testing, for example, the rate of convergence of estimators is T and not ?T{\sqrt{T}} as in regular estimation problems. We study the asymptotic behavior of the maximum likelihood and Bayesian estimators and discuss the possibility of the construction of the goodness-of-fit test for such models of observation.     

Approximate analytical solution of two-dimensional space-time fractional diffusion equation

This work presents an iterative scheme for the numerical solution of the space-time fractional two-dimensional advection–reaction–diffusion equation applying homotopy perturbation with Laplace transform using Caputo fractional-order derivatives. The solution obtained is beneficial and significant to analyze the modeling of superdiffusive systems and subdiffusive system, anomalous diffusion, transport process in porous media. This iterative technique presents the combination of homotopy perturbation technique, and Laplace transforms with He's polynomials, which can further be applied to numerous linear/nonlinear two-dimensional fractional models to computes the approximate analytical solution. In the present method, the nonlinearity can be tackle by He's polynomials. The salient features of the present scientific work are the pictorial presentations of the approximate numerical solution of the two-dimensional fractional advection–reaction–diffusion equation for different particular cases of fractional order and showcasing of the damping effect of reaction terms on the nature of probability density function of the considered two-dimensional nonlinear mathematical models for various situations.     

Tempered stable Lévy motion and transient super-diffusion

The space-fractional diffusion equation models anomalous super-diffusion. Its solutions are transition densities of a stable Lévy motion, representing the accumulation of power-law jumps. The tempered stable Lévy motion uses exponential tempering to cool these jumps. A tempered fractional diffusion equation governs the transition densities, which progress from super-diffusive early-time to diffusive late-time behavior. This article provides finite difference and particle tracking methods for solving the tempered fractional diffusion equation with drift. A temporal and spatial second-order Crank-Nicolson method is developed, based on a finite difference formula for tempered fractional derivatives. A new exponential rejection method for simulating tempered Lévy stables is presented to facilitate particle tracking codes.     

Estimating Functions for Nonlinear Time Series Models

This paper discusses the problem of estimation for two classes of nonlinear models, namely random coefficient autoregressive (RCA) and autoregressive conditional heteroskedasticity (ARCH) models. For the RCA model, first assuming that the nuisance parameters are known we construct an estimator for parameters of interest based on Godambe's asymptotically optimal estimating function. Then, using the conditional least squares (CLS) estimator given by Tjøstheim (1986, Stochastic Process. Appl., 21, 251–273) and classical moment estimators for the nuisance parameters, we propose an estimated version of this estimator. These results are extended to the case of vector parameter. Next, we turn to discuss the problem of estimating the ARCH model with unknown parameter vector. We construct an estimator for parameters of interest based on Godambe's optimal estimator allowing that a part of the estimator depends on unknown parameters. Then, substituting the CLS estimators for the unknown parameters, the estimated version is proposed. Comparisons between the CLS and estimated optimal estimator of the RCA model and between the CLS and estimated version of the ARCH model are given via simulation studies.