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基于重抽样的假设检验的蒙特卡洛评价
Monte Carlo estimation of the power of tests that require resampling can be very computationally intensive. It is possible to reduce the size of the inner resampling loop as long as the resulting estimator of power can be corrected for bias. A simple linear extrapolation method is shown to perform well in correcting for bias and thus reduces computation time in Monte Carlo power studies.
协方差矩阵同质性检验的自助法临界值
Bartlett's modified likelihood ratio statistic A is often suggested in multivariate analysis for testing equality of covariance matrices. Unfortunately, the χ 2-approximation to the null distribution of −2 log Λ is useful only when the data is normally distributed. This article presents a pooled bootstrap procedure that replaces the χ 2-approximation and makes Bartlett's statistic a useful tool for data analysis. This procedure also applies to most quadratic form test…
协方差矩阵同质性检验的 Bootstrap 临界值
Bartlett's modified likelihood ratio statistic A is often suggested in multivariate analysis for testing equality of covariance matrices. Unfortunately, the χ 2-approximation to the null distribution of −2 log Λ is useful only when the data is normally distributed. This article presents a pooled bootstrap procedure that replaces the χ 2-approximation and makes Bartlett's statistic a useful tool for data analysis. This procedure also applies to most quadratic form test…
检验单个协方差矩阵结构的稳健方法
Patterns for a single covariance matrix may be studied using likelihood ratio or quadratic form test statistics. Typically these tests have an asymptotic chi-squared distribution when the data are normally distributed, but the asymptotic distribution is sensitive to nonnor-mality. This paper suggests two ways to robustify inference for patterned matrices: (i) estimate the critical values of normal theory test statistics using a new bootstrap procedure which takes advantage…