论文检索

利用傅里叶级数检验含干扰参数的分布假设AI 解读

We develop a class of smoothed χ 2 tests of distributional hypotheses based on Fourier coefficients used in the density estimation procedure of Fellner. Starting with the simple test of Fx = G, where G is a completely specified hypothesized continuous distribution, we develop tests of the composite null hypothesis Fx − Gα for some vector of unknown parameters α. Variances used in computing the tests vary according to the choice of distributional family and depend on…

置换检验功效模拟中的计算资源分配AI 解读

Consider a randomization test in which an observed statistic is compared to a null distribution constructed by first drawing a random sample from the set of all possible permutations of the data and then calculating the statistic anew for each member of the random sample. If the power of such a test is investigated by simulation, the computer code must have two main loops. In the outer loop, which has O iterations, data are generated under a specific alternative hypothesis.…

位置参数的折中最大似然估计AI 解读

This article describes a new approach for constructing robust location estimators. These estimators are constructed to be nearly optimal in small sample sizes simultaneously for two or more possible underlying shapes of the density of the data. The estimators are weighted averages of the maximum likelihood estimators for the densities considered, where the weights depend on the sample through the likelihood functions. Because of this relationship to the usual MLE's, these…

样条平滑参数的交叉验证与最大似然估计表现AI 解读

An important aspect of nonparametric regression by spline smoothing is the estimation of the smoothing parameter. In this article we report on an extensive simulation study that investigates the finite-sample performance of generalized cross-validation, cross-validation, and marginal likelihood estimators of the smoothing parameter in splines of orders 2 and 3. The performance criterion for both the estimate of the function and its first derivative is measured by the square…

多元总体选择的贝叶斯与极小极大样本可容许程序AI 解读

Let P be a class of independent populations each having an underlying multivariate normal distribution with unknown mean vector. Independent vector samples of common size n are drawn from each population in P. We say that a subset G of P is δ*-Pareto-optimal (in P) if no other population in P has the property that each component of its mean vector is larger by at least δ* ≥ 0 than the corresponding component of the mean vector of any given population in G. This article…

具有相联系自相关随机效应的混合模型系统的经验贝叶斯分析AI 解读

Empirical Bayes concepts are implemented in a simultaneous analysis of a system of mixed linear models having linked and serially correlated random effects. Emphasis is placed on the estimation of the random effects and exploration of the relationships between them. Application is made to the investigation of several series of laboratory assay data that were observed during overlapping time intervals and were therefore subjected to common systematic errors, or “daily…

有限总体抽样区间估计的无信息贝叶斯方法AI 解读

A noninformative Bayesian approach to interval estimation in finite population sampling is discussed. Given the sample, this method introduces the Polya distribution as a pseudo posterior distribution over the unobserved members of the population. In many cases this distribution yields interval estimates similar to those of standard frequentist theory. In addition, it can be used in situations where the standard methods are difficult to apply, for example, in producing an…

贝叶斯分析者如何预期彼此的信念AI 解读

Our goal is to study general properties of one Bayesian's subjective beliefs about the behavior of another Bayesian's subjective beliefs. We consider two Bayesians, A and B, who have different subjective distributions for a parameter θ, and study Bayesian A's expectation of Bayesian B's posterior distribution for θ given some data Y. We show that when θ can take only two values, Bayesian A always expects Bayesian B's posterior distribution to lie between the prior…

聚焦贝叶斯方法：导言AI 解读

This special section of the Theory and Methods section of the Journal of the American Statistical Association honors Morris H. DeGroot, who died November 2, 1989. It contains his last paper, which was being written at the time of his death. Morrie DeGroot was the founding editor of Statistical Science, which published an issue (Vol. 6, No. 1) in 1991 in his honor. That issue contains an excellent biography of Morrie. Morrie was, of course, a Bayesian. This special section is…

摘要暂缺

数据库错误检测与预测AI 解读

We develop and analyze models for a class of problems involving inferences about uncertain numbers of errors in data bases. In particular, we study two error detection methods. In the duplicate performance method, all items in a data base are processed by two individuals (or machines), and the resulting records are compared to find disagreements, which are then resolved. In the known errors method, a data base is first extended to include additional items known to be in…

混沌系统的似然与贝叶斯预测AI 解读

There has recently been considerable interest in both applied disciplines and in mathematics, as well as in the popular science literature, in the areas of nonlinear dynamical systems and chaotic processes. By a nonlinear, deterministic dynamical system, we mean a time series in which, starting at some initial condition, the values of the series are some fixed, nonlinear function of the previous states. One of the more intriguing aspects of these models is their propensity…

市场模型中贝塔系数的时间序列性质考察AI 解读

Understanding the stochastic behavior of beta is fundamental to portfolio risk management. We evaluate the time series properties of beta for oil industry stocks, machinery industry stocks, and a random sample of 100 stocks. Daily returns for stocks in each of these industries and the random sample are obtained from the Center for Research in Security Prices tapes for the period July 1962 through December 1986 and are grouped into 98 quarter-year, and also 49 half-year,…

摘要暂缺

缓慢衰减相关下的正态性检验与干扰参数AI 解读

Slowly decaying serial correlations can cause goodness-of-fit tests for a distribution to reject the null hypothesis with probability tending to one with increasing sample size. When the null distribution is completely specified (simple null hypothesis) the problem is actually worse than for the case where there are nuisance parameters to be estimated (composite null hypothesis). In particular, we consider here testing normality. We discuss limit theorems and propose…

多终点研究中风险相依事件的统计推断AI 解读

Kaplan–Meier and cumulative incidence functions are not sufficient descriptive devices for studies that have multiple time-to-event endpoints. For example, in cancer treatment research the probability of tumor recurrence conditional on not having died from treatment-related toxicities and the prevalence of graft-versus-host disease among leukemia-free patients surviving a bone marrow transplant are of interest. These quantities can be estimated nonparametrically using simple…

相关矩阵特定主成分的假设检验AI 解读

In the application of principal components analysis it is common to replace an observed sample principal component vector by another vector closely resembling the sample vector but which is easier to use or interpret. A useful test of hypothesis in this case is one that specifies the true ith principal component. In this article we obtain an asymptotically chi-squared procedure suitable for testing such a hypothesis when the principal components analysis is performed on a…

基于一类参数估计量的 Cox 回归拟合优度分析AI 解读

In this article we propose a class of estimation functions for the vector of regression parameters in the Cox proportional hazards model with possibly time-dependent covariates by incorporating the weight functions commonly used in weighted log-rank tests into the partial likelihood score function. The resulting estimators behave much like the conventional maximum partial likelihood estimator in that they are consistent and asymptotically normal. When the Cox model is…

约束极大似然：以单调凸逻辑回归为例AI 解读

Maximum likelihood estimates in problems in which the likelihood is smooth and the parameter space is defined by linear or smooth nonlinear inequality constraints can be obtained using available nonlinear optimization packages, such as NPSOL. This class of models includes generalized linear models with order restrictions, convexity, or smoothness constraints on the parameters (smoothness constraints being in the form of bounds on finite differences of the regression…

缓慢衰减序列相关下高斯及相关过程的位置 M 估计AI 解读

We investigate the behavior of M estimators of the location parameter for stochastic processes with long-range dependence. The processes considered are Gaussian or one-dimensional transformations of Gaussian processes. It turns out that, up to a constant, all M estimators are asymptotically equivalent to the arithmetic mean. For Gaussian processes this constant is always equal to one, independently of the ψ function. In view of the case of iid observations, the results are…

回归模型整体与局部拟合诊断AI 解读

We concern ourselves with diagnostics for checking the overall and local goodness of fit of a model s(x) used in the regression of Y on x ∈ U = [0, 1] d . The model for s(x) is a functional form that depends on a finite number of unknown parameters. Two statistics, Λ and Λ w , are proposed that measure the level of agreement between the model fit to the data and the nonparametric kernel estimator on m preselected points in U. Conditions are given under which Λ and λ w , are…

消除核型非参数回归边界效应的几何方法AI 解读

We introduce a simple geometric method for removing edge effects from kernel-type nonparametric regression estimators. It involves reflecting the data set in two estimated points, thereby generating a new data set with three times the range of the original data. The usual kernel-type estimator may be applied to the new, enlarged data set, without any danger of edge effects. This technique is applicable generally to both regularly spaced and randomly spaced designs and admits…

非参数回归中平滑程度范围的选择AI 解读

Most nonparametric regression estimates smooth the observed data in the process of generating a suitable estimate. The resulting curve estimate is sensitive to the amount of smoothing introduced, and it is important to examine the estimate over a range of smoothing choices. An objective way to specify such a range is to estimate the optimal value of the smoothing parameter (bandwidth) with respect to some loss function and then report a confidence interval for this parameter…

灵活快速的自动平滑方法AI 解读

The choice of smoothing parameter or bandwidth is crucial when applying nonparametric regression estimators such as kernel estimators. The optimal choice depends on the data at hand. A data-driven bandwidth selection, close to the optimal one, would make these curve estimators objective, more reliable, and easier to use. Minimizing residual mean squared error criteria, such as cross-validation, have been frequently proposed to estimate the optimal smoothing parameter.…