Non parametric estimation of the diffusion coefficient of a diffusion process

This paper deals with the nonparametric estimation of the diffusion coefficient of a diffusion process in the case when it depends only on time. the estimator is based on the regularization of the quadratic mean through multiscale analysis. We then define estimators of the second derivative of the diffusion coefficient, in order to obtain optimal bandwith and to perform a test of linearity. The results we obtain are quite similar to those of the estimation of the density function of an i.i.d. sequence     

Uniform concentration inequality for ergodic diffusion processes observed at discrete times

In this paper a concentration inequality is proved for the deviation in the ergodic theorem for diffusion processes in the case of discrete time observations. The proof is based on geometric ergodicity of diffusion processes. We consider as an application the nonparametric pointwise estimation problem of the drift coefficient when the process is observed at discrete times.     

扩散模型中扩散系数的非参数组合估计

为了提高扩散系数估计的准确度, 我们利用动态组合时间域与状态域信息提出一个新的组合估计量. 我们发现所提组合估计量能有效估计扩散模型的扩散系数, 正如在本文中模拟所示. 在一定的条件下, 建立了估计量的渐进正态性, 并证明了时间域估计量与状态域估计量是渐进独立的. 大量的模拟展示了所提组合估计量优于单域估计量, 也优于本文所提估计量.     

无穷方差厚尾过程中非参数函数的小波估计

研究无穷方差厚尾过程中含有变点的非参数函数的估计问题。通过小波方法给出变点位置的估计值并得到其收敛速度。在已知变点估计值的基础上,将截尾方法与小波压缩方法相结合得到非参数函数的估计值。模拟研究结果说明对于无穷方差厚尾过程中的函数估计问题小波方法是有效的。     

Covariate-Adjusted Nonparametric Regression for Time Series

The covariate-adjusted regression model was initially proposed for the situations where both the predictors and the response variables are not directly observed, but are distorted by some common observable covariates. In this paper, we investigate a covariate-adjusted nonparametric regression (CANR) model and consider the proposed model on time series setting. We develop a two-step estimation procedure to estimate the regression function. The asymptotic property of the proposed estimation is investigated under the -mixing conditions. Both the real data and simulated examples are provided for illustration.     

Solution of counter diffusion problem with position dependent diffusion coefficent by using variational methods

Unsteady state counter diffusion problem with position dependent diffusion coefficient can be modeled using Fick’s second law. A mathematical model was constructed and solved to quantitatively describe the dynamic behavior of solute diffusion through non-homogeneous materials where diffusion coefficient is a function of position. The eigenfunction expansion approach was utilized to solve the model. The eigenvalues and eigenfunction of the system were obtained using a variational method. It has been shown that position dependency of the material can be neglected if the thickness of the material is relatively small. Mathematical models were solved for different thicknesses and different diffusion coefficient functions.     

Goodness of fit test for ergodic diffusions by tick time sample scheme

We consider a nonparametric goodness of fit test problem for the drift coefficient of one-dimensional ergodic diffusions, where the diffusion coefficient is a nuisance function which is estimated in some sense. Using a theory for the continuous observation case, we construct a test based on the data observed discretely in space, that is, the so-called tick time sampled data. It is proved that the asymptotic distribution of our test under the null hypothesis is the supremum of the standard Brownian motion, and thus our test is asymptotically distribution free. It is also shown that the test is consistent under any fixed alternative.     

Solutions for a generalized fractional anomalous diffusion equation

In this paper, we investigate the solutions for a generalized fractional diffusion equation that extends some known diffusion equations by taking a spatial time-dependent diffusion coefficient and an external force into account, which subjects to the natural boundaries and the generic initial condition. We obtain explicit analytical expressions for the probability distribution and study the relation between our solutions and those obtained within the maximum entropy principle by using the Tsallis entropy.     

The global attractor of a competitor–competitor–mutualist reaction–diffusion system with time delays

The aim of this paper is to investigate the asymptotic behavior of time-dependent solutions of a three-species reaction–diffusion system in a bounded domain under a Neumann boundary condition. The system governs the population densities of a competitor, a competitor–mutualist and a mutualist, and time delays may appear in the reaction mechanism. It is shown, under a very simple condition on the reaction rates, that the reaction–diffusion system has a unique constant positive steady-state solution, and for any nontrivial nonnegative initial function the corresponding time-dependent solution converges to the positive steady-state solution. An immediate consequence of this global attraction property is that the trivial solution and all forms of semitrivial solutions are unstable. Moreover, the state–state problem has no nonuniform positive solution despite possible spatial dependence of the reaction and diffusion. All the conclusions for the time-delayed system are directly applicable to the system without time delays and to the corresponding ordinary differential system with or without time delays.     

Optimal control of the multidimensional diffusion equation

The existence and numerical estimation of a boundary control for then-dimensional linear diffusion equation is considered. The problem is modified into one consisting of the minimization of a linear functional over a set of Radon measures. The existence of an optimal measure corresponding to the above problem is shown, and the optimal measure is approximated by a finite convex combination of atomic measures. This construction gives rise to a finite-dimensional linear programming problem, whose solution can be used to construct the combination of atomic measures, and thus a piecewise-constant control function which approximates the action of the optimal measure, so that the final state corresponding to the above control function is close to the desired final state, and the value it assigns to the performance criterion is close to the corresponding infimum. A numerical procedure is developed for the estimation of these controls, entailing the solution of large, finite-dimensional linear programming problems. This procedure is illustrated by several examples.     

Stationary distribution function estimation for ergodic diffusion process

The problem of nonparametric stationary distribution function estimation by the observation of an ergodic diffusion process is considered. The local asymptotic minimax lower bound on the risk of all the estimators is found, and it is proved that the empirical distribution function is asymptotically efficient in the sense of this bound.     

Marginal quantile regression for varying coefficient models with longitudinal data

In this paper, we investigate the quantile varying coefficient model for longitudinal data, where the unknown nonparametric functions are approximated by polynomial splines and the estimators are obtained by minimizing the quadratic inference function. The theoretical properties of the resulting estimators are established, and they achieve the optimal convergence rate for the nonparametric functions. Since the objective function is non-smooth, an estimation procedure is proposed that uses induced smoothing and we prove that the smoothed estimator is asymptotically equivalent to the original estimator. Moreover, we propose a variable selection procedure based on the regularization method, which can simultaneously estimate and select important nonparametric components and has the asymptotic oracle property. Extensive simulations and a real data analysis show the usefulness of the proposed method.

     

Reweighted Nadaraya-Watson estimation of jump-diffusion models

In this paper,we study the nonparametric estimation of the second infinitesimal moment by using the reweighted Nadaraya-Watson (RNW) approach of the underlying jump diffusion model.We establish strong consistency and asymptotic normality for the estimate of the second infinitesimal moment of continuous time models using the reweighted Nadaraya-Watson estimator to the true function.     

Nonparametric multi-step prediction in nonlinear state space dynamic systems

Filtering and smoothing of stochastic state space dynamic systems have benefited from several generations of estimation approaches since the seminal works of Kalman in the sixties. A set of global analytical or numerical methods are now available, such as the well-known sequential Monte Carlo particle methods which offer some theoretical convergence results for both types of problems. However except in the case of linear Gaussian systems, objectives of the third kind i.e. prediction objectives, which aim at estimating k time steps ahead the anticipated probability density function of the system state variables, conditional on past and present system output observations, still raise theoretical and practical difficulties. The aim of this paper is to propose a nonparametric particle multi-step prediction method able to consistently estimate such anticipated conditional pdf of the state variables as well as their expectations.     

Minimax mean-square thresholding risk in models with non-Gaussian noise distribution

The problem of nonparametric estimation of a signal function from noisy observations by thresholding its wavelet coefficients is considered. The orders of mean-square risk and asymptotically optimal thresholds under general assumptions on the noise distribution are calculated.     

Bayesian Methods for Wavelet Series in Single-Index Models

Single-index models have found applications in econometrics and biometrics, where multidimensional regression models are often encountered. This article proposes a nonparametric estimation approach that combines wavelet methods for nonequispaced designs with Bayesian models. We consider a wavelet series expansion of the unknown regression function and set prior distributions for the wavelet coefficients and the other model parameters. To ensure model identifiability, the direction parameter is represented via its polar coordinates. We employ ad hoc hierarchical mixture priors that perform shrinkage on wavelet coefficients and use Markov chain Monte Carlo methods for a posteriori inference. We investigate an independence-type Metropolis-Hastings algorithm to produce samples for the direction parameter. Our method leads to simultaneous estimates of the link function and of the index parameters. We present results on both simulated and real data, where we look at comparisons with other methods.     

Censored multiple regression by the method of average derivatives

This paper proposes a technique [termed censored average derivative estimation (CADE)] for studying estimation of the unknown regression function in nonparametric censored regression models with randomly censored samples. The CADE procedure involves three stages: firstly-transform the censored data into synthetic data or pseudo-responses using the inverse probability censoring weighted (IPCW) technique, secondly estimate the average derivatives of the regression function, and finally approximate the unknown regression function by an estimator of univariate regression using techniques for one-dimensional nonparametric censored regression. The CADE provides an easily implemented methodology for modelling the association between the response and a set of predictor variables when data are randomly censored. It also provides a technique for “dimension reduction” in nonparametric censored regression models. The average derivative estimator is shown to be root-n consistent and asymptotically normal. The estimator of the unknown regression function is a local linear kernel regression estimator and is shown to converge at the optimal one-dimensional nonparametric rate. Monte Carlo experiments show that the proposed estimators work quite well.     

Additive risk model for current status data with a cured subgroup

Current status data arise when the exact timing of an event cannot be observed, and the only available information is whether or not the event has occurred at a random censoring time point. We consider current status data with a cured subgroup, where subjects in this subgroup are not susceptible to the event of interest. We model the cure probability using a generalized linear model with a known link function. For subjects susceptible to the event, we model their survival hazard using a partly linear additive risk model. We show that the penalized maximum likelihood estimate of the parametric regression coefficient is \({\sqrt{n}}\) consistent, asymptotically normal and efficient. The nonparametric cumulative baseline function and nonparametric covariate effect can be estimated with the n ^1/3 convergence rate. We propose inference using the weighted bootstrap. Simulations study is employed to assess finite sample performance of the proposed estimate. We analyze the Calcification study using the proposed approach.     

Modelling and forecasting vehicle stocks using the trends of stochastic Gompertz diffusion models: The case of Spain

In the present study, we treat the stochastic homogeneous Gompertz diffusion process (SHGDP) by the approach of the Kolmogorov equation. Firstly, using a transformation in diffusion processes, we show that the probability transition density function of this process has a lognormal time‐dependent distribution, from which the trend and conditional trend functions and the stationary distribution are obtained. Second, the maximum likelihood approach is adapted to the problem of parameters estimation in the drift and the diffusion coefficient using discrete sampling of the process, then the approximated asymptotic confidence intervals of the parameter are obtained. Later, we obtain the corresponding inference of the stochastic homogeneous lognormal diffusion process as limit from the inference of SHGDP when the deceleration factor tends to zero. A statistical methodology, based on the above results, is proposed for trend analysis. Such a methodology is applied to modelling and forecasting vehicle stocks. Finally, an application is given to illustrate the methodology presented using real data, concretely the total vehicle stocks in Spain. Copyright © 2008 John Wiley & Sons, Ltd.     

半变系数模型约束PLS估计的渐近正态性

变系数模型已获得了广泛的应用,半变系数模型是变系数模型的有效推广,本文给出半变系数模型在线性约束条件下的PLS估计,并证明了常系数和函数系数估计的渐近正态性.