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Sensitivity Analysis of Seasonal Adjustments: Empirical Case Studies

季节调整的敏感性分析:实证案例研究

Three detailed case studies illustrating the seasonal analysis of economic time series are presented using component models for seasonal and nonseasonal behavior. Analyses are performed within a semi-Bayesian framework where inferences for target quantities of interest, such as seasonally adjusted values, are obtained as posterior distributions conditional on observed data and fitted parameter values. Such an approach is similar to previous model-based methods of seasonal…

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New Tools for Residual Analysis

残差分析的新工具

Techniques are presented for stepwise selection of observations in order of discrepancy from a linear model, and for assigning a $P$ value to each selected observation. A general class of criteria for discrepancy is defined, and leading members of the class are discussed. Two ways to define the sequence of $P$ values for a general selection criterion are discussed, and their relative advantages are compared. The techniques are illustrated on several data sets using only the…

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Estimation in Covariance Components Models

协方差分量模型中的估计

Estimation techniques for linear covariance components models are developed and illustrated with special emphasis on explaining computational processes. The estimation of fixed and random effects when the variances and covariances are known is presented in Bayesian terms, Point estimates of the unknown variances and covariances are computed using the EM algorithm for maximum likelihood estimation from incomplete data. The techniques are illustrated with data on law schools,…

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Maximum Likelihood from Incomplete Data Via the EM Algorithm

通过 EM 算法对不完整数据进行最大似然估计

Summary A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situations, applications to grouped, censored or truncated data, finite mixture models, variance component estimation, hyperparameter estimation, iteratively reweighted least…

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A Simulation Study of Alternatives to Ordinary Least Squares

普通最小二乘替代方法的模拟研究

Estimated regression coefficients and errors in these estimates are computed for 160 artificial data sets drawn from 160 normal linear models structured according to factorial designs. Ordinary multiple regression (OREG) is compared with 56 alternatives which pull some or all estimated regression coefficients some or all the way to zero. Substantial improvements over OREG are exhibited when collinearity effects are present, noncentrality in the original model is small, and…

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A Generalization of Bayesian Inference

贝叶斯推断的一种推广

Summary Procedures of statistical inference are described which generalize Bayesian inference in specific ways. Probability is used in such a way that in general only bounds may be placed on the probabilities of given events, and probability systems of this kind are suggested both for sample information and for prior information. These systems are then combined using a specified rule. Illustrations are given for inferences about trinomial probabilities, and for inferences…

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Upper and lower probability inferences based on a sample from a finite univariate population

有限单变量总体样本的上下概率推断

This paper provides detailed formulae for upper and lower probability inferences, based on a sample from a finite population with a discrete univariate observable characteristic, and directed towards the unknown parameters of the population or properties of a future sample. The model is set up in § 2 and the detailed formulae are given in § 3. Section 4 demonstrates a limited relationship with confidence statements and §5 explores some special cases.

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Logic of Statistical Inference.

统计推断的逻辑

Preface 1. Long run frequencies 2. The chance set-up 3. Support 4. The long run 5. The law of likelihood 6. Statistical tests 7. Theories of testing 8. Random sampling 9. The fiducial argument 10. Estimation 11. Point estimation 12. Bayes' theory 13. The subjective theory.

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On the Difficulties Inherent in Fisher's Fiducial Argument

费舍尔信任论证的内在困难

The terms predictive and postdictive are introduced for two basic and implicitly well-known modes of interpreting probabilities, and the roles of these two modes in statistical inference are discussed with emphasis on the confidence argument of Neyman and the fiducial argument of Fisher. The conflicts between the confidence approach and the fiducial approach are analyzed in a way which generally supports Fisher's position. Finally, difficulties internal to the fiducial…

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