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同时置信界的平均宽度最优性AI 解读

Simultaneous confidence bounds for multilinear regression functions over subregions $X$ of Euclidean space are defined to be $\mu$-optimal in a class of bounds $C$, if they minimize average width with respect to $\mu$ over $X$, among all bounds in $C$ with equal coverage probability. We show that for certain simultaneous confidence bounds we can find a measure $\mu$ relative to which the bounds are $\mu$-optimal in $C$, where $C$ is a large class of bounds. Such results are…

极值理论启发的分布上尾估计AI 解读

An estimate of the upper tail of a distribution function which is based on the upper $m$ order statistics from a sample of size $n(m \rightarrow \infty, m/n \rightarrow 0$ as $n \rightarrow \infty)$ is shown to be consistent for a wide class of distribution functions. The empirical mean residual life of the $\log$ transformed data and the sample $1 - m/n$ quantile play a key role in the estimate. The joint asymptotic behavior of the empirical mean residual life and sample $1 - m/n$…

线性回归预测误差的Bootstrap与交叉验证估计AI 解读

Different estimates of the mean squared error of prediction for linear regression models are derived by the bootstrap and cross-validation approaches. A comparison is made under normal error distributions, especially by the biases and the mean square errors. The results indicate that the bias corrected bootstrap estimator is best unbiased and should be the first choice, while its simulated variant has approximately the same behaviour. On the other hand, if only a comparison…

高斯过程贝叶斯估计量的稳定性AI 解读

We consider the Bayes estimator $\delta_0$ for a Gaussian signal process observed in the presence of additive Gaussian noise under contamination of the signal or noise by QN-laws, introduced by Gualtierotti (1979). Upper bounds on the increase in the mean square error of $\delta_0$ over the minimum possible mean square error under contaminated noise or contaminated signal are given. It is shown that the performance of $\delta_0$ is relatively close to optimal for small…

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与最佳者多重比较的约束同时置信区间AI 解读

For comparing $k$ treatment effects $\theta_1, \theta_2, \cdots \theta_k$, often the parameters of primary interest are $\theta_i - \max_{j\neq i}\theta_j, i = 1, \cdots, k$. In this article, we develop constrained $100P^\ast{\tt\%}$ two-sided simultaneous confidence intervals for $\theta_i - \max_{j \neq i} \theta_j$ which we refer to as (constrained) MCB intervals. It turns out that the lower bounds of the intervals imply Indifference Zone selection inference, and the…

关于样本中位数自助法的一则说明AI 解读

Efron (1979, 1982), in his treatment of the bootstrap, discusses its use for estimation of the asymptotic variance of the sample median, in the sampling situation of independent and identically distributed random variables with common distribution function $F$ having a positive derivative continuous in a neighborhood of the true median $\mu$. The natural conjecture that the bootstrap variance estimator converges almost surely to the asymptotic variance is shown by an example…

一类独立性非线性秩检验的渐近正态性AI 解读

Asymptotic normality of a class of nonlinear rank statistics to test the null hypothesis of total independence of an $m$-variate population is proved. The rank statistics are generated from $2m$-variate square integrable functions such that they are symmetric and nondegenerate. Some results under contiguous alternatives are also given.

序贯检验维纳过程漂移符号的贝叶斯风险界AI 解读

Let $x(t)$ be a Wiener process with drift $\mu$ and variance 1 per unit time. The following problem is treated; test $H:\mu \leq 0$ vs. $A:\mu > 0$ with the loss function $|\mu|$ if the wrong decision is made and 0 otherwise, and with $c =$ cost of observation per unit time, where $\mu$ has a prior distribution which is normal with mean 0 and variance $\sigma^2_0$. An idea of Bickel and Yahav is followed to obtain a lower bound for the Bayes risk which is strict as $\sigma_0 \rightarrow \infty$…

二项分布双重抽样估计的若干结果AI 解读

A complete class result is obtained for double sample estimation of a binomial parameter. The complete class leads to some inadmissibility results for some procedures that are likely to be used in practice. It is also shown that the sample proportion as a true double estimator is not unbiased.

凸集的估计AI 解读

Given independent observations $x_1, \cdots, x_n$ drawn uniformly from an unknown compact convex set $D$ in $\mathbb{R}^p$ ($p$ known) it is desired to estimate $D$ from the observations. This problem was first considered, for $p = 2$, by Ripley and Rasson (1977). We consider a decision-theoretic approach where the loss function is $L(D, \hat{D}) = m(D \Delta \hat{D})$. We prove the completeness of the Bayes estimation rules. A form for the nonrandomized Bayes estimation…

超球面上对称分布的信息矩阵AI 解读

A general definition of symmetry for a directional model is given. Then the information matrix for the parameters indexing a symmetric density is shown to be block-diagonal: one block for the location and one block for the shape. This result is used to construct an algorithm for the efficient estimation of the parameters. Examples are given; a new symmetric distribution, the $FB_6$ is introduced; it generalizes the classical distributions on the hypersphere.

正则变尾参数估计的最优可达收敛速率AI 解读

We derive lower bounds to rates of convergence for estimators of shape and scale parameters in distributions with regularly varying tails. We exhibit simple estimators which attain these rates.

子集选择渐近一致性的一般理论及应用AI 解读

The problem of selecting a random nonempty subset from $k$ populatinos, characterized by $\theta_1, \cdots, \theta_k$ with possible nuisance parameters $\sigma$, is considered using a decision-theoretic approach. The concept of asymptotic consistency is defined as the property that the risk of a procedure at $(\theta, \sigma)$ tends to the minimum loss at $(\theta, \sigma)$. Necessary and sufficient conditions for both pointwise and uniform (on compact sets) consistency for…

相关单位向量的对称分布AI 解读

This paper introduces several notions of symmetry for the joint distribution of two dependent unit vectors. Bivariate generalizations of $\mathscr{L}$-symmetry (Rivest, 1984) and rotational symmetry are introduced. If the joint distribution of two unit vectors is at least $\mathscr{L}$-symmetric the information matrix for the parameters indexing it is shown to have a simple shape.

贝叶斯两阶段抽样估计程序AI 解读

Bayes double sample procedures are given for estimating the mean of exponential family distributions. The distribution can be multiparameter and so the case of normal mean with unknown variance is included. Linear combination loss functions are usually assumed. Stein's double sample procedure, and other intuitive double sample procedures for estimating a binomial parameter, are studied. Recommendations are given, including guidelines for the size of the initial samples.

序贯临床试验中偏置硬币设计的性质AI 解读

Martingale methods and the martingale invariance principle are used to derive central limit theorems and related results for biased coin designs of the kind previously studied by Efron, Wei and many others. The results are applied to the study of selection bias. The method is developed for the simplest two-treatment case and then extended, first to the case of several treatments, and secondly to the case of two treatments with prognostic factors.

基于变换的近似似然与概率计算AI 解读

Probability distributions, especially in applications, are generally specified via density functions; alternative representations, including the characteristic function, moment generating function, and sequence of moments, are most commonly encountered in theoretical settings. These alternative means of specification do, however, give rise to the construction of certain approximations that can facilitate the implementation of likelihood methods or the calculation of…

最小判别信息调整AI 解读

Minimum discriminant information adjustment has primarily been used in the analysis of multinomial data; however, no such restriction is necessary. Let $P$ be a distribution on $R^a$, and let $\mathscr{C}$ be a convex set of distributions on $R^a$. Let $\mathbf{X}_i, 1 \leq i \leq n$, be independent and identically distributed observations with common distribution $P$. The minimum discriminant information adjustment (MDIA) of $P$ relative to $\mathscr{C}$ is the element $Q$ o…

位置参数后验分布的决策论推导AI 解读

In this paper we develop a decision theoretic formulation for the problem of deriving posterior distributions for a parameter $\theta$, when the prior information is vague. Let $\pi(d\theta)$ be the true but unknown prior, $Q_\pi(d\theta\mid X)$ the corresponding posterior and $\delta(d\theta\mid X)$ an estimate of the posterior based on an observation $X$. The loss function is specified as a measure of distance between $Q_\pi(\cdot\mid X)$ and $\delta(\cdot\mid X)$, and the…

受限参数空间中的最优固定大小置信程序AI 解读

Optimal fixed size confidence procedures are derived for the mean of a normal random variable with known variance, when the mean is restricted to a compact interval. These confidence procedures are, in turn, based on the solution of a related minimax decision problem which is characterized by a zero-one loss function and a compact interval parameter space. The minimax rules obtained are nonrandomized, admissible, Bayes procedures. The decision-theoretic results are extended…

控制问题中估计量不可容许性的尖锐充要条件AI 解读

Suppose $\mathbf{x} = (x_1, \cdots, x_m)^t$ is an $m$-variate normal random variable with mean vector $\mathbf{\theta} = (\theta_1, \cdots, \theta_m)^t$ and identity dispersion matrix. We consider the control problem which, in canonical form, is the problem of estimating $\mathbf{\theta}$ with respect to the loss $L(\theta, \delta) = (1 - \theta^t\delta)^2,$ where $\delta(x) = (\delta_1(x), \cdots, \delta_m(x))^t$. A necessary and sufficient condition for the admissibility…

最近邻回归函数估计的渐近正态性AI 解读

Let $(X, Y)$ be a random vector in the plane. We show that a smoothed N.N. estimate of the regression function $m(x) = \mathbb{E}(Y\mid X = x)$ is asymptotically normal under conditions much weaker than needed for the Nadaraya-Watson estimate. It also turns out that N.N. estimates are more efficient than kernel-type estimates if (in the mean) there are few observations in neighborhoods of $x$.

样条平滑：等价变带宽核方法AI 解读

The spline smoothing approach to nonparametric regression and curve estimation is considered. It is shown that, in a certain sense, spline smoothing corresponds approximately to smoothing by a kernel method with bandwidth depending on the local density of design points. Some exact calculations demonstrate that the approximation is extremely close in practice. Consideration of kernel smoothing methods demonstrates that the way in which the effective local bandwidth behaves in…