MANOVA for Nested Designs with Unequal Cell Sizes and Unequal Cell Covariance Matrices
نویسندگان
چکیده
منابع مشابه
Two-Way MANOVA With Unequal Cell Sizes and Unequal Cell Covariance Matrices
In this article, we propose a parametric bootstrap (PB) test for testing main, simple and interaction effects in heteroscedastic two-way MANOVA models under multivariate normality. The PB test is shown to be invariant under permutation-transformations, and affinetransformations, respectively.Moreover, the PB test is independent of the choice ofweights used to define the parameters uniquely. The...
متن کاملTwo-way Anova with Unequal Cell Frequencies and Unequal Variances
In this article we consider the Two-Way ANOVA model with unequal cell frequencies without the assumption of equal error variances. By taking the generalized approach to finding p-values, classical F-tests for no interaction effects and equal main effects are extended under heteroscedasticity. The generalized Ftests developed in this article can be utilized in significance testing or in fixed le...
متن کاملCentralized Caching with Unequal Cache Sizes
We address centralized caching problem with unequal cache sizes. We consider a system with a server of files connected through a shared error-free link to a group of cacheenabled users where one subgroup has a larger cache size than the rest. We investigate caching schemes with uncoded cache placement which minimize the load of worst-case demands over the shared link. We propose a caching schem...
متن کاملA Two Sample Test for Mean Vectors with Unequal Covariance Matrices
In this paper, we consider testing the equality of two mean vectors with unequal covariance matrices. In the case of equal covariance matrices, we can use Hotelling’s T 2 statistic, which follows the F distribution under the null hypothesis. Meanwhile, in the case of unequal covariance matrices, the T 2 type test statistic does not follow the F distribution, and it is also difficult to derive t...
متن کاملA Linear Classifier for Gaussian Class Conditional Distributions with Unequal Covariance Matrices
In this paper we present a linear pattern classification algorithm, Principal Component Null Space Analysis (PCNSA) which uses only the first and second order statistics of data for classification and compare its performance with existing linear algorithms. PCNSA first projects data into the PCA space in order to maximize between class variance and then finds separate directions for each class ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Applied Mathematics
سال: 2014
ISSN: 1110-757X,1687-0042
DOI: 10.1155/2014/649202