GMANOVA Model Research Articles

Ridge-type regularization method for questionnaire data analysis

In questionnaire studies for evaluating objects such as manufacturing products, evaluators are required to respond to several evaluation items for the objects. When the number of objects is large, a part of the objects is often assigned randomly to each evaluator, and the response becomes a matrix with missing components. To handle this kind of data, we consider a model by using a dummy matrix representing the existence of the missing components, which can be interpreted as an extension of the GMANOVA model. In addition, to cope with the case where the numbers of the object and evaluation items are large, we consider a ridge-type estimator peculiar to our model to avoid instability in estimation. Moreover, we derive a C p criterion in order to select the tuning parameters included in our estimator. Finally, we check the validity of the proposed method through simulation studies and real data analysis.

Estimation of parameters in a generalized GMANOVA model based on an outer product analogy and least squares

This paper investigates estimation of parameters in a combination of the multivariate linear model and growth curve model, called a generalized GMANOVA model. Making analogy between the outer product of data vectors and covariance yields an approach to directly do least squares to covariance. An outer product least squares estimator of covariance (COPLS estimator) is obtained and its distribution is presented if a normal assumption is imposed on the error matrix. Based on the COPLS estimator, two-stage generalized least squares estimators of the regression coefficients are derived. In addition, asymptotic normalities of these estimators are investigated. Simulation studies have shown that the COPLS estimator and two-stage GLS estimators are alternative competitors with more efficiency in the sense of sample mean, standard deviations and mean of the variance estimates to the existing ML estimator in finite samples. An example of application is also illustrated.

A novel trace test for the mean parameters in a multivariate growth curve model

A trace test for the mean parameters of the growth curve model is proposed. It is constructed using the restricted maximum likelihood followed by an estimated likelihood ratio approach. The statistic reduces to the Lawley–Hotelling trace test for the Multivariate Analysis of Variance (MANOVA) models. Our test statistic is, therefore, a natural extension of the classical trace test to GMANOVA models. We show that the distribution of the test under the null hypothesis does not depend on the unknown covariance matrix Σ . We also show that the distributions under the null and alternative hypotheses can be represented as sums of weighted central and non-central chi-square random variables, respectively. Under the null hypothesis, the Satterthwaite approximation is used to get an approximate critical point. A novel Satterthwaite type approximation is proposed to obtain an approximate power. A simulation study is performed to evaluate the performance of our proposed test and numerical examples are provided as illustrations.

Third-order power comparisons for a class of tests for multivariate linear hypothesis under general distributions

The purpose of this paper is, in multivariate linear regression model (Part I) and GMANOVA model (Part II), to investigate the effect of nonnormality upon the nonnull distributions of some multivariate test statistics under normality. It is shown that whatever the underlying distributions, the difference of local powers up to order N − 1 after either Bartlett’s type adjustment or Cornish–Fisher’s type size adjustment under nonnormality coincides with that in Anderson [An Introduction to Multivariate Statistical Analysis, 2nd ed. and 3rd ed., Wiley, New York, 1984, 2003] under normality. The derivation of asymptotic expansions is based on the differential operator associated with the multivariate linear regression model under general distributions. The performance of higher-order results in finite samples, including monotone Bartlett’s type adjustment and monotone Cornish–Fisher’s type size adjustment, is examined using simulation studies.

On Comparisons of Exact Powers of Bivariate GMANOVA Tests

Exact powers of four classical tests in a GMANOVA model are compared numerically when the order of the error sum of square matrix is 2. The four tests are likelihood ratio (=LR), Pillai's V, Hotelling's T 2, and Roy's largest root tests. It turns out that for small sizes, there are a few cases in which Rothenberg's condition for the relative magnitude of asymptotic powers of three standard tests does not hold.

Alternative estimators of the common regression matrix in two GMANOVA models under weighted quadratic losses

We consider the problem of estimating the common regression matrix of two GMANOVA models with different unknown covariance matrices under certain type of loss functions which include a weighted quadratic loss function as a special case. We consider a class of estimators, which contains the Graybill–Deal-type estimator proposed by Sugiura and Kubokawa (Ann. Inst. Statist. Math. 40 (1988) 119), and we give its risk representation via Kubokawa and Srivastava's (Ann. Statist. 27 (1999) 600; J. Multivariate Anal. 76 (2001) 138) identities when the error matrices follow the elliptically contoured distributions. Using the method similar to an approximate minimization of the unbiased risk estimate due to Stein (Studies in the Statistical Theory of Estimation, vol. 74, Nauka, Leningrad, 1977, p. 4), we obtain an alternative estimator to the Graybill–Deal-type estimator which was given under the normality assumption. However, it seems difficult to evaluate the risk of our proposed estimator analytically because of complex nature of its risk function. Instead, we conduct a Monte-Carlo simulation to evaluate the performance of our proposed estimator. The results indicate that our proposed estimator compares favorably with the Graybill–Deal-type estimator.

Form and selection of covariance adjusted estimators in repeated measures models

The GMANOVA model is considered when one characteristic is measured at q time points. The covariance adjusted estimator is the OLS estimator adjusted using analysis of covariance. The covariates are obtained from the space vertical to that derived from the design matrix. Its form is obtained as a GLS estimator using a weight matrix of reduced rank unless all available covariates are used. The choice of the appropriate covariate combination is made by introducing a method based on cross validation (CV). A comparison is made with two other methods that appeared in statistical literature using simulation.

ON MALLOWS'Cp FOR THE GMANOVA MODEL UNDER DOUBLE LINEAR RESTRICTIONS ON THE REGRESSION PARAMETER MATRIX

This paper generalizes Mallows'Cp to the GMANOVA model under double linear restrictions on the regreesion parameter matrix. This generalization follows from the MANOVA model generalization of Mallows'Cp derived by Hasegawa(1986).

Construdtion of shrinkage estimators for the regression coefficient matrix in the gmanova model

This paper extensively investigates the theory of estimating the regression coefficient matrix in the normal GM.4KOVA model. We explicitly construct estimators which improve upon the maximum likelihood estimator under an invariant scalar loss function. These include the double shrinkage estimatois and those shrinking the maximum likelihood estimators directly. The underlying method is the decomposition of the problem into the conditional subproblems due to Kariya, Konno, and Strawderman(l996) and application of integration-by-parts technique to derive an unbiased estimate of the risk for certain class of invariant estimators.

Double Shrinkage Estimators in the GMANOVA Model

In the GMANOVA model or equivalent growth curve model, shrinkage effects on the MLE (maximum likelihood estimator) are considered under an invariant risk matrix. We first study the fundamental structure of the problem through which we decompose the estimation problem into some conditional problems and then demonstrate some classes of double shrinkage minimax estimators which uniformly dominate the MLE in the matrix risk.

A note on the manova model with double linear restrictions

In this note we show how one may construct goodness-of-fit tests to test hypotheses for the restricted MANOVA and GMANOVA models using the multivariate seemingly unrelated regression (MSUR) model.

Estimation of a Normal Covariance Matrix with Incomplete Data under Stein′s Loss

Suppose that we have (n − a) independent observations from Np(0, Σ) and that, in addition, we have a independent observations available on the last (p − c) coordinates. Assuming that both observations are independent, we consider the problem of estimating Σ under the Stein′s loss function, and show that some estimators invariant under the permutation of the last (p − c) coordinates as well as under those of the first c coordinates are better than the minimax estimators of Eaten. The estimators considered outperform the maximum likelihood estimator (MLE) under the Stein′s loss function as well. The method involved here is computation of an unbiased estimate of the risk of an invariant estimator considered in this article. In addition we discuss its application to the problem of estimating a covariance matrix in a GMANOVA model since the estimation problem of the covariance matrix with extra data can be regarded as its canonical form.

Consistent estimators of the variance-covariance matrix of the gmanova model with missing data

A common problem in multivariate general linear models is partially missing response data. The simplest method of analysis in the presence of missing data has been to delete all observations on any individual with any missing data(listwise deletion) and utilize a traditional complete data approach. However: this can result in a great loss of information: and perhaps inconsistencies in the estimation of the variance-covariance matrix. In the generalized multivariate analysis of variance(GMANOVA) model with missing data: Kleinbaum(1973) proposed an estimated generalized least squares approach. In order to apply this: however: a consistent estimate of the variance-covariance matrix is needed. Kleinbaum proposed an estimator which is unbiased and consistent: but it does not take advantage of the fact that the underlying model is GMANOVA and not MANOVA. Using the fact that the underlying model is GMANOVA we have constructed four other con¬sistent estimators. A Monte Carlo simulation experiment is conducted tto further examine how well these estimators compare to the estimator proposed by Kleinbaum.

Improved estimators for the GMANOVA problem with application to Monte Carlo simulation

The problem of finding classes of estimators which improve upon the usual (e.g., ML, LS) estimator of the parameter matrix in the GMANOVA model under (matrix) quadratic loss is considered. Classes of improved estimators are obtained via combining integration-by-parts methods for normal and Wishart distributions. Also considered is the application of control variates to achieve better efficiency in multipopulation multivariate simulation studies.

Equivariant Estimation in a Model with an Ancillary Statistic

This paper reformulates a result of Hora and Buehler on best equivariant estimators to treat a model admitting an ancillary statistic. The approach itself was established by Pitman, Girshick and Savage and Kiefer, and expanded by Zidek. The model considered in this paper is assumed to be generated as an orbit under a group acting on the parameter space. The general result obtained here is applied to a model in the Nile problem, a model with a known variation coefficient, a circle model and the GMANOVA model, and best equivariant estimators (BEE's) are derived. In the first two models, the BEE's dominate the MLE's uniformly.

A note on connectedness in fixed effects manova and gmanova models withmissing cells

Murray and Smith (1985) and Hocking (1985) give a generalized definition and test of connectedness in the case of missing cells using the univariate cell-means model with linear restrictions on the cell-means. The test of connectedness is here extended to multivariate fixed effects models, including the usual MANOVA model with linear restrictions, the MANOVA model with double linear restrictions, and the GMANOVA model.

Applications of a Vectorized MANOVA‐Model

AbstractIf the observation matrix of n independent p‐dimensional normal vectors with common covariance matrix is transformed into an np‐vector, linear models and hypotheses on the expectations can be formulated, that are not attainable in standard MANOVA or GMANOVA models. The distribution of an earlier proposed test procedure for this vectorized model is derived for p = 2, and its application is demonstrated for crossover and repeated measurement designs. Numerical results are compared in situations, in which both the standard and the vectorized model are applicable.

Error bounds for asymptotic expansions of the distribution of the MLE in a GMANOVA model

In this paper we obtain asymptotic expansions for the distribution function and the density function of a linear combination of the MLE in a GMANOVA model, and for the density function of the MLE itself. We also obtain certain error bounds for the asymptotic expansions.

On a gmanova model likelihood ratio test criterion

For the generalized MANOVA (GMANOVA) model of Potthoff and Roy (1964), X = BξA + E, Khatri (1966) derives the likelihood ratio test criterion for test-ing the composite double linear null hypothesis CξV = 0, C,V known. This criterion plays an important role in statistics, and several authors have recently studied its further properties. However, Khatri's (1966) de-reviation of the distribution of this criterion is involved. By noting that the GMANOVA model is re-stricted MANOVA model, this paper presents an alter-native simple derivation of the distribution of this criterion. The derivation is based on the generalized Sverdrup's lemma, Kabe (1965).

A mixture of the manova and gmanova models

A mixture of the MANOVA and GMANOVA models is presented. The expected value of the response matrix in this model is the sum of two matrix components. The first component represents the GMANOVA port...