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Probability and Statistics: Essays in Honor of Franklin A. Graybill.

概率与统计:纪念 Franklin A. Graybill 的论文集

Admissibility in Linear Models with Polyhedral Covariance Structure (A.-M. Azzam, D. Birkes, J. Seely). Simulated Power Comparisons of the Asymptotic and Nonasymptotic Goodman and Kruskal Tau Tests for Sparse R and C Tables (K.J. Berry, P.W. Mielke, Jr.). Schemes Exhibiting Hurst Behavior (D.C. Boes). The Extension of Grubb's Estimators for Classes of Incomplete Block Designs (R.A. Bradley, D.A. Rollier). Applications of Innovation Representations in Time Series Analysis…

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A Monte Carlo Comparison of Four Estimators of the Dispersion Matrix of a Bivariate Normal Population, Using Incomplete Data

不完整数据下二元正态总体离散矩阵四种估计量的蒙特卡洛比较

Consider a random vector (X 1, X 2) distributed as a bivariate normal with mean vector zero, and dispersion matrix Σ = [(σ ij )]. Suppose we are given samples of sizes n 1 and n 2, respectively, from the marginals of X 1, X 2, and a sample of size n 3 from the bivariate population of (X 1, X 2). The problem is to obtain a good estimator of Σ based on this (incomplete) sample. In this article, four estimators of Σ are compared using Monte Carlo methods, and it is found that a…

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Optimal Fractional Factorial Plans for Main Effects Orthogonal to Two-Factor Interactions: 2 m Series

主效应与二因子交互作用正交的最优部分因子设计

This article develops some theory of balanced 2m fractional factorial designs which permit estimation of main effects orthogonally to two-factor interactions and the general mean, whose size N is desirably small and which are optimal with respect to the three standard criteria namely, the trace, determinant and the largest root of the variance-covariance matrix of the estimates. The equivalence of these criteria is established for certain situations. For various practical…

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On the Monotonicity Property of the Three Main Tests for Multivariate Analysis of Variance

多元方差分析三种主要检验的单调性

Summary This paper presents a unified and relatively simple proof of the monotonicity property of the three well-known tests for the multivariate analysis of variance (MANOVA), viz., (i) Hotelling-Lawley trace criterion, (ii) Wilks's likelihood-ratio criterion, and (iii) Roy's largest root criterion. The last one has already been considered by Roy and Mikhail (1961). Their proof, however, has a gap at one place, which has been bridged in this paper.

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