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向量参数的高精度方向推断AI 解读

We consider statistical inference for a vector-valued parameter of interest in a regular asymptotic model with a finite-dimensional nuisance parameter. We use highly accurate likelihood theory to derive a directional test, in which the |$p$|-value is obtained by one-dimensional numerical integration. This extends the results of Davison et al. (2014) for linear exponential families to nonlinear parameters of interest and to more general models. Examples and simulations…

线性指数族中向量参数的精确方向性推断AI 解读

We consider inference on a vector-valued parameter of interest in a linear exponential family, in the presence of a finite-dimensional nuisance parameter. Based on higher-order asymptotic theory for likelihood, we propose a directional test whose p-value is computed using one-dimensional integration. The work simplifies and develops earlier research on directional tests for continuous models and on higher-order inference for discrete models, and the examples include…

用于贝叶斯与频率学派推断的默认先验AI 解读

Summary We investigate the choice of default priors for use with likelihood for Bayesian and frequentist inference. Such a prior is a density or relative density that weights an observed likelihood function, leading to the elimination of parameters that are not of interest and then a density-type assessment for a parameter of interest. For independent responses from a continuous model, we develop a prior for the full parameter that is closely linked to the original Bayes…

平均对数似然与高阶近似AI 解读

Higher-order approximations to p-values can be obtained from the loglikelihood function and a reparameterization that can be viewed as a canonical parameter in an exponential family approximation to the model. This approach clarifies the connection between Skovgaard (1996) and Fraser et al. (1999a), and shows that the Skovgaard approximation can be obtained directly using the mean loglikelihood function.

离散数据的改进似然推断AI 解读

Summary Discrete data, particularly count and contingency table data, are typically analysed by using methods that are accurate to first order, such as normal approximations for maximum likelihood estimators. By contrast continuous data can quite generally be analysed by using third-order procedures, with major improvements in accuracy and with intrinsic separation of information concerning parameter components. The paper extends these higher order results to discrete data,…

分量参数的似然AI 解读

For a statistical model with data, likelihood for the scalar or vector full parameter θ, of dimension p say, is typically well defined and easily computed. In this paper, we investigate likelihood for a component parameter ψ(θ) of dimension d < p and make use of the recent likelihood theory that has been successful in producing highly accurate third‐order p‐values for scalar parameters of continuous models. The theory leads under moderate regularity to a definitive…

非线性或非正态回归分析：基于似然的简便精确p值近似AI 解读

We develop simple approximations for the p values to use with regression models having linear or nonlinear parameter structure and normal or nonnormal error distribution; computer iteration then gives confidence intervals. Both frequentist and Bayesian versions are given. The approximations are derived from recent developments in likelihood analysis and have third-order accuracy. Also, for very small and medium-sized samples, the accuracy can typically be high. The…

非线性或非正态回归：基于似然分析的简便精确p值AI 解读

We develop simple approximations for the p values to use with regression models having linear or nonlinear parameter structure and normal or nonnormal error distribution; computer iteration then gives confidence intervals. Both frequentist and Bayesian versions are given. The approximations are derived from recent developments in likelihood analysis and have third-order accuracy. Also, for very small and medium-sized samples, the accuracy can typically be high. The…

指数模型的多参数检验：基于似然的三阶近似AI 解读

For composite testing of several canonical components in an exponential model, a conical or directional test provides a one-dimensional measure of departure. We approximate the distribution of this test statistic to third order by numerical inversion of a profile likelihood. The method extends the Lugannani & Rice type formula to kernels other than the normal.

指数模型的多参数检验：基于似然的三阶近似AI 解读

For composite testing of several canonical components in an exponential model, a conical or directional test provides a one-dimensional measure of departure. We approximate the distribution of this test statistic to third order by numerical inversion of a profile likelihood. The method extends the Lugannani & Rice type formula to kernels other than the normal.

多元分析中处理效应与误差的条件推断AI 解读

Directional methods of conditional inference are applied to the multivariate analysis of variance. Exact and easily implemented tests are derived for treatment effect, error variance matrix, and treatment improvement. Each test provides a physical measure of the magnitude of the departure from expectation under the hypothesis and is conditional on the indicated direction of departure. For treatment effect the test provides a geometrically understandable alternative to tests…

统计推断：从似然到显著性AI 解读

The concepts of likelihood and significance were defined and initially developed by R. A. Fisher, but followed almost separate and distinct routes. We suggest that a central function of statistical inference is in fact the conversion of the first, likelihood, into the second, significance: a linking of the Fisher concepts. A first-order asymptotic route for this is incorporated into most statistical packages. It uses the standardized maximum likelihood estimate, the…

多元分析中处理效应与误差的条件推断AI 解读

Directional methods of conditional inference are applied to the multivariate analysis of variance. Exact and easily implemented tests are derived for treatment effect, error variance matrix, and treatment improvement. Each test provides a physical measure of the magnitude of the departure from expectation under the hypothesis and is conditional on the indicated direction of departure. For treatment effect the test provides a geometrically understandable alternative to tests…

非正态线性回归：高维显著性水平计算的一个例子AI 解读

Analysis of nonnormal linear models leads to an initial conditioning on the standardized residuals, giving an unnormed density on Rk, where k is the number of parameters. To obtain an observed level of significance for a single parameter it is then necessary to calculate a marginal probability, thus requiring integration in k dimensions. In this paper a conditional approach to evaluating the observed level of significance is developed, and an importance sampling technique is…

边际尾概率近似与标量参数推断AI 解读

In many situations, inference for a scalar parameter in the presence of nuisance parameters requires integration of either a joint density of pivotal quantities or a joint posterior density. For such inference, accurate approximations of marginal tail probabilities are useful to avoid high-dimensional integrals. Two tail probability approximations are developed in this paper. Numerical results given for conditional inference in location-scale and linear regression models…

摘要暂缺

非正态线性回归：高维显著性水平的一个例子AI 解读

Analysis of nonnormal linear models leads to an initial conditioning on the standardized residuals, giving an unnormed density on Rk, where k is the number of parameters. To obtain an observed level of significance for a single parameter it is then necessary to calculate a marginal probability, thus requiring integration in k dimensions. In this paper a conditional approach to evaluating the observed level of significance is developed, and an importance sampling technique is…

剖面似然的调整AI 解读

Conditional and marginal likelihoods constructed from parameter-dependent functions of the data have an additional parameter dependence that changes with the choice of supporting metric for the corresponding densities. We consider constructing marginal and conditional likelihoods by using densities expressed in terms of an instrinsic choice of support measure, based on either Euclidean metric or the constant information metric. Barndorff-Nielsen's (1983) approximation to the…

剖面似然的调整AI 解读

Conditional and marginal likelihoods constructed from parameter-dependent functions of the data have an additional parameter dependence that changes with the choice of supporting metric for the corresponding densities. We consider constructing marginal and conditional likelihoods by using densities expressed in terms of an instrinsic choice of support measure, based on either Euclidean metric or the constant information metric. Barndorff-Nielsen's (1983) approximation to the…

实参数的条件推断：样本空间上的微分方法AI 解读

A one-dimensional conditional procedure defines a partition of the sample space into curves which can be represented by means of a unit vector field. A formula is given for the conditional distribution in terms of local properties of the vector field. Conditions are developed for reducing the first-order effects of nuisance parameters and reproducing to higher order the likelihood change for the parameter of interest. The emphasis is on extending exponential family methods…

实参数的条件推断：样本空间上的微分方法AI 解读

A one-dimensional conditional procedure defines a partition of the sample space into curves which can be represented by means of a unit vector field. A formula is given for the conditional distribution in terms of local properties of the vector field. Conditions are developed for reducing the first-order effects of nuisance parameters and reproducing to higher order the likelihood change for the parameter of interest. The emphasis is on extending exponential family methods…

摘要暂缺

置信水平、后验概率与Buehler例子AI 解读

A confidence level is sometimes treated as a probability and accordingly given substantial "confidence." And with certain statistical models a confidence level can also be an objective posterior probability (Fraser and MacKay, 1975). An instance involving such a probability with a binary error was discussed at the Symposium on Foundations of Statistical Inference, University of Waterloo, 1970 (Godambe and Sprott, 1971, page 49). An example by Buehler (ibid., page 337) of a…

必然分析与自适应推断AI 解读

Statistical inference is examined for the location-scale model, using nonnormal distributions for the error. The development emphasizes necessary analysis that follows as a logical consequence from the data and model alone; only the terminal parts of the analysis use the conventional criteria of estimation and testing. Inferences are obtained concerning the shape of the distribution and concerning the location and scale parameters. Illustrations are included using real and…