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正电子发射断层成像中极大似然估计的渐近一致性及应用AI 解读

This paper indicates that a minor modification of the maximum likelihood estimate of Vardi, Shepp and Kaufman can be regarded as a step in the standard nonparametric MLE by the method of sieves and establishes the asymptotic consistency for it. This method of sieves suggests that the number of pixels needs to be in line with the number of detectors in order to avoid poor image reconstructions. A simulation study is also presented to support this suggestion.

平稳区间值概率模型的有限维分布与尾部行为AI 解读

We consider the relationship between the finite-dimensional distributions of a stationary time series model and its asymptotic behavior in the framework of interval-valued probability (IVP), a simple generalization of additive probability measures. By Caratheodory's theorem, the specification of a countably additive probability measure on the algebra of cylinders $\mathscr{C}$ uniquely defines its behavior on $\sigma(\mathscr{C})$ (containing the tail events). If the measure…

尺度模型的渐近贝叶斯风险下界及二阶极小极大估计AI 解读

The problem of Bayes estimation of the scale parameter is considered. Lower bounds for the asymptotic Bayes risk are given as the restricted parameter space increases to the positive half-line. The results are next applied to establish the second-order minimax estimator of the scale parameter. Surprisingly, the least favorable distribution coincides with that for the corresponding location parameter problem.

双参数指数族后验分布的可积展开AI 解读

Asymptotic expansions of posterior distributions are derived for a two-dimensional exponential family, which includes normal, gamma, inverse gamma and inverse Gaussian distributions. Reparameterization allows us to use a data-dependent transformation, convert the likelihood function to the two-dimensional standard normal density and apply a version of Stein's identity to assess the posterior distributions. Applications are given to characterize optimal noninformative priors…

通过逐次替代抽样进行不完整数据的贝叶斯非参数估计AI 解读

In the problem of estimating an unknown distribution function $F$ in the presence of censoring, one can use a nonparametric estimator such as the Kaplan-Meier estimator, or one can consider parametric modeling. There are many situations where physical reasons indicate that a certain parametric model holds approximately. In these cases a nonparametric estimator may be very inefficient relative to a parametric estimator. On the other hand, if the parametric model is only a…

未知连接函数下的自适应估计AI 解读

We consider the fitting of generalized linear models in which the link function is assumed to be unknown, and propose the following computational method: First, estimate regression coefficients using the canonical link. Then, estimate the link via a kernel smoother, treating the direction in the predictor space determined by the regression coefficients as known. Then reestimate the direction using the estimated link and alternate between these two steps. We show that under…

用最小加性主成分分析加性依赖与曲线共线性AI 解读

Additive principal components are a nonlinear generalization of linear principal components. Their distinguishing feature is that linear forms $\sum_ia_i X_i$ are replaced with additive functions $\sum_i\phi_i(X_i)$. A considerable amount of flexibility for fitting data is gained when linear methods are replaced with additive ones. Our interest is in the smallest principal components, which is somewhat uncommon. Smallest additive principal components amount to data…

协方差函数的非参数估计AI 解读

We describe kernel methods for estimating the covariance function of a stationary stochastic process, and show how to ensure that the estimator has the positive semidefiniteness property. From a practical viewpoint, our method is significant because it does not demand a parametric model for covariance. From a technical angle, our results exhibit a striking departure from those in more familiar cases of kernel estimation. For example, in the context of covariance estimation,…

最小假设下基于子样本的大样本置信区域AI 解读

In this article, the construction of confidence regions by approximating the sampling distribution of some statistic is studied. The true sampling distribution is estimated by an appropriate normalization of the values of the statistic computed over subsamples of the data. In the i.i.d. context, the method has been studied by Wu in regular situations where the statistic is asymptotically normal. The goal of the present work is to prove the method yields asymptotically valid…

受约束 M 估计的渐近理论AI 解读

Limit theorems for an $M$-estimate constrained to lie in a closed subset of $\mathbb{R}^d$ are given under two different sets of regularity conditions. A consistent sequence of global optimizers converges under Chernoff regularity of the parameter set. A $\sqrt n$-consistent sequence of local optimizers converges under Clarke regularity of the parameter set. In either case the asymptotic distribution is a projection of a normal random vector on the tangent cone of the…

探索后验分布的马尔可夫链方法AI 解读

Several Markov chain methods are available for sampling from a posterior distribution. Two important examples are the Gibbs sampler and the Metropolis algorithm. In addition, several strategies are available for constructing hybrid algorithms. This paper outlines some of the basic methods and strategies and discusses some related theoretical and practical issues. On the theoretical side, results from the theory of general state space Markov chains can be used to obtain…

允许插入与删除的序列比较：泊松近似AI 解读

We construct a statistical test for a sequence alignment problem which enables us to decide whether two given sequences are related. Such a test can be used in DNA and protein sequence comparisons. It is based on a comparison of two long sequences of i.i.d. letters taken from a finite alphabet. The test statistic typically employed is the length of the longest matching region between the two sequences in which a certain number of insertions and deletions but no mismatches…

以给定一般指数族为共轭先验族的抽样模型AI 解读

Let $\mathscr{K} = \{K_\lambda: \lambda \in \Lambda\}$ be a family of sampling distributions for the data $x$ on a sample space $\mathscr{X}$ which is indexed by a parameter $\lambda \in \Lambda,$ and let $\mathscr{F}$ be a family of priors on $\Lambda$. Then $\mathscr{F}$ is said to be conjugate for $\mathscr{K}$ if it is closed under sampling, that is, if the posterior distributions of $\lambda$ given the data $x$ belong to $\mathscr{F}$ for almost all $x$. In this paper,…

线性空间与锥约束下正态均值似然比检验功效的单调性AI 解读

Anderson studied the monotonicity of the integral of a symmetric, unimodal density over translates of a symmetric convex set. Restricting attention to elliptically contoured, unimodal densities, Mukerjee, Robertson and Wright weakened the assumption of symmetry on the set and obtained monotonicity properties of power functions, including unbiasedness, for some likelihood ratio tests in order restricted inference for the variance-known case. For elliptically contoured,…

改进 James–Stein 正部估计量AI 解读

The purpose of this paper is to give an explicit estimator dominating the positive-part James-Stein rule. The James-Stein estimator improves on the "usual" estimator $X$ of a multivariate normal mean vector $\theta$ if the dimension $p$ of the problem is at least 3. It has been known since at least 1964 that the positive-part version of this estimator improves on the James-Stein estimator. Brown's 1971 results imply that the positive-part version is itself inadmissible…

双侧检验与单侧置信界AI 解读

Based on the duality between tests and confidence sets we introduce a new method to derive one-sided confidence bounds following the rejection of a null hypothesis with two-sided alternatives. This method imputes that the experimenter is only interested in confidence bounds if the null hypothesis is rejected. Furthermore, we suppose that he is only interested in the direction and a lower confidence bound concerning the distance of the true parameter value to the parameter…

重复中位数斜率估计量的渐近性质AI 解读

The influence function is determined for (twice) repeated median estimators with arbitrary kernel functions, and more generally in the case where the two medians are replaced by a general class of estimators. Asymptotic normality is then established for the repeated median estimator of the slope parameter in simple linear regression. In this case the influence function is bounded. For bivariate Gaussian data the efficiency becomes $4/\pi^2 \approx 40.5{\tt \%},$ which is the…

与 U 过程相关的估计量及其多元中位数应用：渐近正态性AI 解读

If a criterion function $g(x_1, \ldots, x_m; \theta)$ depends on $m > 1$ samples, then a natural estimator of $\arg \max P^mg(x_1, \ldots, x_m; \theta)$ is the $\arg \max$ of a $U$-process. It is observed that, under suitable conditions, these estimators are asymptotically normal. This is then applied to prove asymptotic normality of Liu's simplical median and of Oja's medians in $\mathbb{R}^d$.

二元总体的 Kolmogorov–Smirnov 型拟合优度与比较检验AI 解读

Statistical tests consistent under any alternative are provided for both goodness-of-fit and two-sample comparisons. They are based on (1) the derivation of estimates for the tails of the probability distribution of some particular functionals of a Wiener sheet and (2) the use of a mapping that carries normalized empirical processes (asymptotically distributed as Brownian bridge) onto new processes that converge in distribution to a Wiener sheet. The proposed test statistics…

GI/G/1 队列平稳等待时间分布函数的非参数估计AI 解读

The $GI/G/1$ queueing model is regarded as a functional that maps the service and interarrival time distribution functions onto the stationary waiting time distribution function. By considering the output of the functional when it is applied to nonparametric estimators of the input distribution functions, we obtain a nonparametric estimator of the stationary waiting time distribution function. Using appropriate continuity and differentiability properties of the functional,…

相似曲线数据中共同回归函数的非参数估计AI 解读

The paper is concerned with data from a collection of different, but related, regression curves $(m_j0_{j = 1}, \ldots, N, N \gg 1$. In statistical practice, analysis of such data is most frequently based on low-dimensional linear models. It is then assumed that each regression curve $m_j$ is a linear combination of a small number $L \ll N$ of common functions $g_1, \ldots, g_L$k. For example, if all $m_j$'s are straight lines, this holds with $L = 2, g_1 \equiv 1$ and $g_2(x) = x$…

线性回归与平滑的同时置信带AI 解读

Suppose we observe $Y-i = f(x_i) + \varepsilon_i, i = 1, \ldots, n$. We wish to find approximate $1 - \alpha$ simultaneous confidence regions for $\{f(x), x \in \mathscr{X}\}$. Our regions will be centered around linear estimates $\hat{f}(x)$ of nonparametric or nonparametric $f(x)$. There is a large amount of previous work on this subject. Substantial restrictions have been usually placed on some or all of the dimensionality of $x,$ the class of functions $f$ that can be…

随机删失下二元分布的最近邻估计AI 解读

We consider the problem of estimating the bivariate distribution of the random vector $(X, Y)$ when $Y$ may be subject to random censoring. The censoring variable $C$ is allowed to depend on $X$ but it is assumed that $Y$ and $C$ are conditionally independent given $X = x$. The estimate of the bivariate distribution is obtained by averaging estimates of the conditional distribution of $Y$ given $X = x$ over a range of values of $x$. The weak convergence of the centered…

若干计数过程广义比例风险模型的信息与渐近效率AI 解读

Proportional hazards models with stochastic baseline hazards and estimators of the relative risk coefficient in these models were proposed by Prentice, Williams and Peterson and by Chang and Hsiung in medical and industrial contexts. The form of the estimating functions recommended varies according to the form of the unknown stochastic baseline hazards. This paper examines the same estimation problem in the context of large-sample theory. It is shown that the proposed…