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局部中心极限定理、拒绝抽样的高阶相关性与逻辑似然渐近性质AI 解读

Let I1,…,In be independent but not necessarily identically distributed Bernoulli random variables, and let Xn=∑j=1nIj. For ν in a bounded region, a local central limit theorem expansion of $\mathbb {P}(X_{n}=\mathbb {E}X_{n}+\nu)$ is developed to any given degree. By conditioning, this expansion provides information on the high-order correlation structure of dependent, weighted sampling schemes of a population E (a special case of which is simple random sampling), where a…

半正态图最大值的渐近理论AI 解读

We study approximations to the distribution of the largest Walsh-Fourier coefficient obtained by transforming i.i.d. observations from a non-normal parent distribution. The study is inspired by the use of half-normal plots in screening studies and in the analysis of molecular sequence data. In the latter context, we indicate why continuity corrections are needed.

用于非参数曲线估计的一类新核函数AI 解读

We introduce a new class of variable kernels which depend on the smoothing parameter b through a simple scaling operation, and which have good integrated mean square error (IMSE) convergence properties. These kernels deform "automatically" near the boundary, eliminating boundary bias. Computational formulas are given for all orders of kernel in terms of exponentially damped sines and cosines. The kernel is a computationally convenient approximation to a certain Green's…

Cox 回归模型中嵌套病例对照抽样的渐近理论AI 解读

By providing a probabilistic model for nested case-control sampling in epidemiologic cohort studies, consistency and asymptotic normality of the maximum partial likelihood estimator of regression parameters in a Cox proportional hazards model can be derived using process and martingale theory as in Andersen and Gill. A general expression for the asymptotic variance is given and used to calculate asymptotic relative efficiencies relative to the full cohort variance in some…

非参数泛函估计的最优代入估计量AI 解读

Consider the problem of estimating the value of a functional $\Lambda(f)$ for $f$ an unknown density or regression function. The straightforward plug-in estimator $\Lambda(\hat f)$ with $\hat f$ a particular estimate of $f$ achieves the optimal rate of convergence in the sense of Stone over bounded subsets of a Sobolev space for a broad class of linear and nonlinear functionals. For many functionals the rate calculation depends on a Frechet-like derivative of the functional,…