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簇内重抽样AI 解读

Within‐cluster resampling is proposed as a new method for analysing clustered data. Although the focus of this paper is clustered binary data, the within‐cluster resampling asymptotic theory is general for many types of clustered data. Within‐cluster resampling is a simple but computationally intensive estimation method. Its main advantage over other marginal analysis methods, such as generalised estimating equations (Liang & Zeger, 1986; Zeger & Liang, 1986) is that it…

次序统计量与推断：估计方法AI 解读

List of Tables. List of Figures. Introduction. Basic Theory. Moments and Other Expected Values. Linear Estimation Based on Order Statistics. Maximum Likelihood Estimation. Approximate Maximum Likelihood Estimation. Optimal Linear Estimation Based on Selected Order Statistics. Cohen -Whitten Estimators: Using Order Statistics.Estimation in Regression Models. A Sample Completion Technique for Censored Samples. Bibliography. Index.

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删失数据局部最强秩检验的统一推导方法AI 解读

Summary In this note a unified way of deriving locally most powerful rank tests for censored data is given. A lemma is proven. This lemma is then used to derive LMP rank tests.

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估计理论中若干界的比较AI 解读

Conditions are given for the attainment of the Hammersley-Chapman-Robbins bound for the variance of an unbiased estimator, in both regular and nonregular cases. Comparisons are made between this bound and the Bhattacharyya system of bounds for a wide class of distributions and parametric functions. Sufficient conditions are provided to determine when one bound is sharper than the other one.

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多元线性回归子假设的渐近分布无关检验札记AI 解读

The purpose of this note is to show that asymptotically distribution-free (ADF) rank tests proposed by Koul [Ann. Math. Statist. 41 (1970), 1273-1281] for testing subhypotheses in multiple linear regression when based on symmetric scores are asymptotically totally inefficient and can even be inconsistent for symmetric parent distributions. Also, the condition given in his Theorem 2.4 for a test to be ADF is not necessary.

某些样本函数极值的极限行为AI 解读

For a sequence of random variables forming an $m$-dependent stochastic process (not necessarily stationary), asymptotic distribution and other convergence properties of the extremum of certain functions of the empirical distribution are studied. In this context, it is shown that the asymptotic probability of the classical Kolmogorov-Smirnov statistic exceeding any positive real number provides an upper bound for the corresponding probability when the underlying random…

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样本分位数与中程统计量的渐近方差性质AI 解读

SUMMARY In this paper, some properties of the asymptotic variance of the sample quantiles and the derived mid-ranges are studied and the role of the sample median is critically discussed. It is shown that among the class of sample quantiles, the sample median will have asymptotically the smallest variance only under somewhat restrictive regularity conditions; while among the class of sample mid-ranges, the sample median (being the central mid-range) has asymptotically the…