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线性模型中的一致变量选择AI 解读

A method of estimating linear model dimension and variable selection is proposed. This new criterion, which generalizes the Cp criterion, the Akaike information criterion (AIC), the Bayes information criterion, and the phiv criterion and is consistent under certain conditions, is based on a new class of penalty functions and a procedure of sorting covariates based on t-statistics. In the course of introducing this method, we discuss the important role of the penalty function…

Box–Cox 变换后检验的渐近相对效率界AI 解读

Bounds on the asymptotic relative efficiency (ARE) of the Box-Cox transformed two-sample $t$-test to the ordinary $t$-test are obtained under local alternatives. It is shown that the ARE is at least 1 for location-shift models. In the case of scale-shift models, a similar bound applies provided the limiting value of the estimated power transformation is greater than 1. If instead the Box-Cox transformed $t$-test is compared against the ordinary $t$-test applied to the…

列联表卡方独立性检验的检验水平界AI 解读

Bounds are obtained on the limiting size of the level-$\alpha \chi^2$-test of independence in a contingency table, as the sample size increases. The situations considered include (a) sampling with one or both sets of marginal totals random, (b) performing the test with or without the continuity correction and (c) with or without conditioning on the event $\mathscr{E}_k$ that the minimum estimated expected cell count is greater than a given $k \geq 0$. Bounds for both the…

通过广义判别分析构建树形分类器AI 解读

The problem of constructing classification rules that can be represented as decision trees is considered. Each object to be classified has an associated x vector containing possibly incomplete covariate information. Tree construction is based on the information provided in a “learning sample” of objects with known class identities. The x vectors in the learning sample may have missing values as well. Procedures are proposed for each of the components of classifier…

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通过广义判别分析构造树形分类规则AI 解读

The problem of constructing classification rules that can be represented as decision trees is considered. Each object to be classified has an associated x vector containing possibly incomplete covariate information. Tree construction is based on the information provided in a “learning sample” of objects with known class identities. The x vectors in the learning sample may have missing values as well. Procedures are proposed for each of the components of classifier…

置信水平的校准AI 解读

Two approaches for dealing with the problem of poor coverage probabilities of certain standard confidence intervals are proposed. The first is a recommendation that the actual coverage be estimated directly from the data and its value reported in addition to the nominal level. This is achieved through a combination of computer simulation and density estimation. The asymptotic validity of the procedure is proved for a number of common situations. A classical example is the…

方差分量比的改进估计量AI 解读

The problem of estimating a ratio of variance components in the balanced one-way random effects model is considered. It is shown that in terms of mean squared error, the ML, REML (or truncated ANOVA), and Bayes modal estimators (using the noninformative prior) are inadmissible. An estimator that dominates all three is derived. Two other estimators that are adaptive in nature are also introduced. The new estimators are shown to possess much-improved mean squared error…

检验分离假设族的一种新方法AI 解读

Theoretical and empirical evidence are reported showing that the tests given in Cox (1961) and Williams (1970a, b) for the problem of testing separate families of hypotheses suffer from fundamental difficulties. Neither test is guaranteed to be asymptotically level a, even for very smooth problems. A new method proposed here as an alternative works adaptively by repeated application of the parametric bootstrap idea over slowly shrinking confidence regions. Theoretical…

利用重抽样方法估计分布端点AI 解读

The problem of estimating an endpoint of a distribution is revisited, using the bootstrap and random subsample methods. Contrary to an example in Bickel and Freedman (1981) suggesting that these methods do not work here, it is shown that one can in fact construct asymptotically valid confidence intervals in some situations.

尾部序所定义分布类的渐近相对效率界AI 解读

It is shown that large classes of asymptotic relative efficiencies (AREs) are isotonic with respect to various partial orderings on the heaviness of tails of symmetric distributions. The orderings include those of van Zwet (1964), Lawrence (1975), Barlow and Proschan (1975), and a new one that generalizes all three. Characterizations in terms of these orderings are given for many familiar families of distributions with restricted tail and central behavior. By restricting…