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We study multi-period inventory control systems in which managers face seasonal demands with unknown distributions and make inventory decisions based on past demand data. It can be shown that a data-d...
Classical statistical theory ignores model selection in assessing estimation accuracy. Here we consider bootstrap methods for computing standard errors and con dence intervals that take model selecti...
Bayesian Inference and the Parametric Bootstrap
Liouville theory is characterized by the exponential interaction μe2bφ and a coupling to worldsheet curvature QRφ. A typical irrational CFT (c = 1 + 6Q2). Difficult theory solved in late 90’s using th...
We establish two moderate deviation principles (MDP) in the bootstrap setting. We prove MDP for the joint distribution of the empirical measure and the empirical bootstrap measure (empirical measure o...
Abstract: In $\HH$-bootstrap percolation, a set $A \subset [n]$ of initially `infected' vertices spreads by infecting vertices which are the only uninfected vertex in an edge of the hypergraph $\HH \s...
Abstract: Graph bootstrap percolation is a deterministic cellular automaton which was introduced by Bollob\'as in 1968, and is defined as follows. Given a graph $H$, and a set $G \subset E(K_n)$ of in...
One of most important problems concerning the ARFIMA time series model is the estimation of fractional parameter d. Various methods have been used to solve this problem, such as the log-periodogram r...
We introduce the heterogeneous-k-core, which generalizes the k-core, and contrast it with bootstrap percolation.
介绍了AR模型线性的非参数核检验统计量,并对检验统计量进行了检验水平和检验势的Wild Bootstrap模拟.模拟结果显示,非参数核检验统计量的Wild Bootstrap检验稳健性和可靠性都比渐近检验高,TAR与双线性AR模型比其他3种AR的检验势高.
Bootstrap percolation on the random graph Gn,p is a process of spread of “activation” on a given realization of the graph with a given number of initially active nodes. At each step those vertices wh...
讨论了基于分位数过程能力指数的Bootstrap 置信区间, 并以Taguchi指数p mC ′ 为例, 在过程服从Weib ull分布情况下给出了指数p mC ′ 的Bootstrap 置信区间的求法, 通过模拟研究验证了该方法的可行性.
本文研究回归函数的kn-近邻估计的渐近性质,得到了回归函数的kn-近邻估计的渐近正态性和它的Bootstrap统计量的相合性. 在高阶矩存在的条件下,我们证明了回归函数的kn-近邻估计的Bootstrap逼近比正态逼近更精确.
本文研究随机删失概率密度估计的光滑bootstrap逼近.给出了光滑bootstrap逼近成立的充分条件,并证明了概率密度的光滑bootstrap估计方差几乎处处收敛到概率密度核估计的渐近方差.

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