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嵌套随机效应非平衡混合模型极大似然估计的快速评分算法AI 解读

A fast Fisher scoring algorithm for maximum likelihood estimation in unbalanced mixed models with nested random effects is described. The algorithm uses explicit formulae for the inverse and the determinant of the covariance matrix, given by LaMotte (1972), and avoids inversion of large matrices. Description of the algorithm concentrates on computational aspects for large sets of data.

球面数据的核密度估计AI 解读

We study two natural classes of kernel density estimators for use with spherical data. Members of both classes have already been used in practice. The classes have an element in common, but for the most part they are disjoint. However, all members of the first class are asymptotically equivalent to one another, and to a single element of the second class. In this sense the second class ‘contains’ the first. It includes some estimators which out-perform all those in the first…

自回归误差下重复测量的若干最优设计AI 解读

A number of individuals are observed at equally spaced time points; different individuals behave independently but the observations on the same individual have the autocorrelation structure of a first-order autoregressive process. We are concerned with the optimal allocation of treatments, assuming that at each time point a new selection must be made. Conditions for optimality are given for models without nuisance parameters, and also with covariates and with block effects.

广义线性测量误差模型的条件得分与最优得分AI 解读

This paper studies estimation in generalized linear models in canonical form when the explanatory vector is measured with independent normal error. For the functional case, i.e. when the explanatory vectors are fixed constants, unbiased score functions are obtained by conditioning on certain sufficient statistics. This work generalizes results obtained by the authors (Stefanski & Carroll, 1985) for logistic regression. In the case that the explanatory vectors are independent…

估计函数与近似条件似然AI 解读

The approximate conditional likelihood method proposed by Cox & Reid (1987) is applied to the estimation of a scalar parameter Θ, in the presence of nuisance parameters. The estimating function of Θ based on the approximate conditional likelihood is shown to be preferable to that based on the profile likelihood. A sufficient condition for both approaches to be equivalent is given. The role of parameter orthogonality is emphasized. Several examples including bivariate normal…

比较两个概率的双重抽样估计量精度AI 解读

It is assumed that both error-prone and error-free observations can be made on a binary response, the second type being more expensive. In double sampling error-prone observations are made on all sample units and for a random subsample error-free observations are made as well. Tenenbein (1970) studied allocation of units in double sampling for estimating a single probability. We consider double sampling for comparing two probabilities. For different cost ratios for…

《随机区组设计删失数据的对齐秩检验》的相关纠错说明AI 解读

The paper is incorrect in stating that permutation tests based on the modified statistics described in § 7 are valid when treatments are subject to unequal consoring. Wei (1980) and Wei & Pee (1985) have developed distribution-free methods for analysing right-censored paired data that can be applied when censoring is unequal.

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《自回归时间序列集合的估计与假设检验》的公式更正AI 解读

Formulae for ψ^(α) and σ^2(α) on p. 87 do not hold for all α, since they may not satisfy the constraint σ^2(α)≤ψ^(α) induced by the parameterization. Then the expressions do not give maximum likelihood estimates for given α. When the inequality fails, the maximum likelihood estimates are on the boundary of the parameter space, namely where σ2=ψ⁠. The maximum then occurs for σ^2(α)=ψ^(α) both equal to σ^2(α) given at p. 88, shortly after (7). Formula (6) still applies,…

针对正象限依赖备择的 Kendall τ 独立性检验替代方法AI 解读

A class of distribution-free tests has been proposed for testing independence against positive quadrant dependence. The Kendall-tau test belongs to this class and the newly proposed tests are seen to be quite efficient.

高斯超总体模型下 Royall 预测量的最优性AI 解读

This note shows that under the assumption of a Gaussian superpopulation model with a general symmetric and positive-definite covariance matrix, the model-based predictor of Royall (1976) is the unbiased minimum mean squared error predictor of the total of a given finite population. This somewhat anticipated result is a generalization of recent published work in which it was derived under more restrictive assumptions.

一阶自回归参数的渐消窗估计AI 解读

In the estimation of autoregressive parameters, biases from ordinary least-squares or maximum likelihood estimator are big even for moderately large sample sizes. A new tapering estimation technique is developed to reduce the bias. Monte Carlo experiments show that the tapering estimation in the time domain yields a substantial reduction in the bias when autoregressive parameters are positive and not very near unity, over a wide range of sample sizes.

空间高斯过程协方差函数似然估计的问题AI 解读

Maximum likelihood has frequently been suggested as a way to estimate covariance parameters in spatial Gaussian processes. The results of some simple examples based on simulated data are discussed. It is shown by an example with real data that the profile likelihood can be multimodal, and that the global maximum may not correspond to a sensible value of the parameters.

疾病病例聚集的检测检验AI 解读

A test is given to detect clustering in disease incidence or mortality data The test statistic is the mean distance between all pairs of disease cases. Its null mean and variance and its asymptotic normality are derived under assumptions that accommodate differences in population distribution among demographic subgroups at different disease risk. The test is illustrated on 63 cases of anal and rectal squamous cell carcinoma in San Francisco during 1973–1981.

构造两样本序贯置信区间的一种简单方法AI 解读

On presente une methode generale simple pour construire un intervalle de confiance de longueur bornee pour la difference des medianes de population. On obtient un intervalle a deux echantillons

关于多元岭回归AI 解读

A multivariate linear regression model with q responses as a linear function of p independent variables is considered with a p × q parameter matrix B. The least-squares or normal-theory maximum likelihood estimate of B is deficient in that it takes no account of the ‘across regression’ correlations, and ignores the Stein effect. A remedy was offered by Brown & Zidek (1980) in the form of a multivariate ridge estimator. A richer class of estimators is obtained here by casting…

采用新无信息先验的多响应参数估计AI 解读

A noninformative prior distribution for the covariance matrix of a normal distribution is derived from a data-relocated likelihood function. This new prior distribution also follows from Jeffreys's rule. Resulting modifications of current formulae for multiresponse parameter estimation are summarized.

二维格点上的内蕴自回归及相关模型AI 解读

Stationary autoregressions on a two-dimensional lattice are generalized to intrinsic models where only increments are assumed to be stationary. Prediction formulae and the asymptotic behaviour of the semivariogram are derived. For parameter estimation we propose an approximate maximum likelihood estimator, a generalization of Whittle's estimator; it is derived also for general intrinsic models.

利用平滑方法诊断二元 Logistic 回归AI 解读

The paper discusses some diagnostic tools for binary logistic regression which use smoothing techniques. Smoothed binary data are employed to devise a battery of diagnostic plots, from simple procedures which indicate only the adequacy or inadequacy of a maximum likelihood logistic fit, to partial residual plots and delta local deviance plots which reveal the type of model inadequacy, suggesting perhaps the addition of nonlinear terms or interactions. Properties of the…

二元数据 Logistic 回归中的解释变量变换AI 解读

Some results are presented on improving the fit of the logistic regression model for binary data by transforming the vector of explanatory variables. The methods are based on consideration of the distributions of these variables conditional on outcome group. The transformations required are the functions of the explanatory variables which appear in the log density ratio of the conditional distributions.

Bootstrap 与基于似然的置信区域AI 解读

We describe a method for constructing likelihood-based confidence regions for a vector parameter, using the bootstrap and nonparametric density estimation. The technique is illustrated by application to a numerical example, and its theoretical properties are elucidated. It is argued that likelihood-based regions should not be approximated by ellipses, if we are to have any hope of capturing first-order departures from normality. Bootstrap algorithms for constructing…

通过预枢轴化降低置信集的覆盖水平误差AI 解读

Approximate confidence sets for a parameter θ may be obtained by referring a function of θ and of the sample to an estimated quantile of that function's sampling distribution. We call this function the root of the confidence set. Either asymptotic theory or bootstrap methods can be used to estimate the desired quantile. When the root is not a pivot, in the sense of classical statistics, the actual level of the approximate confidence set may differ substantially from the…

关于《有限总体系统抽样与分层抽样的若干渐近结果》的评论AI 解读

This paper leaves a misleading impression. When estimating a population mean, Y¯N⁠, the estimator Y^G=Y¯S+G.(X¯S−X¯N)⁠, where Y¯S is the sample mean of the Yi and G is any bounded function of the sample, is design consistent in both the Cochran sense, as sample size n approaches population size N, and the Isaki-Fuller sense, as n approaches infinity with n/N fixed, under simple random sampling. The perversity of Cochran design consistency may be seen when ŶG is combined with…

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