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自适应与交互式马尔可夫链蒙特卡洛算法的收敛AI 解读

Adaptive and interacting Markov chain Monte Carlo algorithms (MCMC) have been recently introduced in the literature. These novel simulation algorithms are designed to increase the simulation efficiency to sample complex distributions. Motivated by some recently introduced algorithms (such as the adaptive Metropolis algorithm and the interacting tempering algorithm), we develop a general methodological and theoretical framework to establish both the convergence of the…

长程依赖下 U 过程的渐近性质AI 解读

Let (Xi)i≥1 be a stationary mean-zero Gaussian process with covariances $\rho(k)=\mathbb {E}(X_{1}X_{k+1})$ satisfying ρ(0) = 1 and ρ(k) = k−DL(k), where D is in (0, 1), and L is slowly varying at infinity. Consider the U-process {Un(r), r ∈ I} defined as $$U_n(r) =\frac{1}{n(n−1)} \sum_{1≤i≠j≤n}1_{\{G(X_i, X_j)≤r\}},$$ where I is an interval included in ℝ, and G is a symmetric function. In this paper, we provide central and noncentral limit theorems for Un. They are used to…

非平稳高斯时间序列记忆参数的小波 Whittle 估计AI 解读

We consider a time series X={Xk, k∈ℤ} with memory parameter d0∈ℝ. This time series is either stationary or can be made stationary after differencing a finite number of times. We study the “local Whittle wavelet estimator” of the memory parameter d0. This is a wavelet-based semiparametric pseudo-likelihood maximum method estimator. The estimator may depend on a given finite range of scales or on a range which becomes infinite with the sample size. We show that the estimator…