Unbalanced One-way Random Model Research Articles

Approximate test for testing a null variance ratio in the Unbalanced One-Way Random Model

The approximate test for testing the signicance of the random effect is presented in the unbalanced one-way random model in which both random effects and errors are from non-normal universes. The test is based on the asymptotic distribution of the F-statistic. Under the condition that the number of groups tends to infinity while the average of powers of the group sizes is bounded, the asymptotic dis tribution of the F-statistic is obtained. Robustness of the proposed test is given.

Inferences on the reliability in balanced and unbalanced one-way random models

In this article, the hypothesis testing and interval estimation for the reliability parameter are considered in balanced and unbalanced one-way random models. The tests and confidence intervals for the reliability parameter are developed using the concepts of generalized p-value and generalized confidence interval. Furthermore, some simulation results are presented to compare the performances between the proposed approach and the existing approach. For balanced models, the simulation results indicate that the proposed approach can provide satisfactory coverage probabilities and performs better than the existing approaches across the wide array of scenarios, especially for small sample sizes. For unbalanced models, the simulation results show that the two proposed approaches perform more satisfactorily than the existing approach in most cases. Finally, the proposed approaches are illustrated using two real examples.

Interval estimation of variance ratio in non-normal unbalanced one-way random models

Confidence intervals based on the harmonic mean method are proposed for estimating the ratio of variance components and/or the intraclass correlation coefficient (ρ) in unbalanced one-way random models without the normality assumptions. Since the proposed procedure is heavily dependent on the estimation of the kurtosis of the underlying distributions, bias-corrected estimators of kurtosis are proposed as well. Several asymptotic results concerning the proposed procedure are given along with simulation results to assess its performance in finite sample size situations. The proposed intervals are also compared with the corresponding confidence intervals based on the arithmetic mean method and were found to effectively maintain the nominal probability of coverage, except for leptokurtic distributions with fairly large kurtosis where the intervals tend to be liberal. According to the simulation results, the proposed harmonic mean intervals are recommended for use in practice. However, while computing the harmonic mean intervals, the bias-corrected estimators of kurtosis should be used when it is anticipated that ρ is small, but for large ρ the empirically corrected estimators of kurtosis should be adopted. The procedure is illustrated using a real data set.

Approximate interval for the between-group variance under heteroscedasticity

The paper presents an approximate confidence interval for the between-group variance in a one-way heteroscedastic random-effects model. Derivation of the interval is inspired by the ideas in Williams [A confidence intervals for variance components, Biometrika 49 (1962), pp. 278–281] for the homoscedastic case. Simulations suggest that the proposed interval performs better than or comparably to the other available procedures as studied in Wimmer and Witkovský [Between group variance component interval estimation for the unbalanced heteroscedastic one-way random effects model, J. Stat. Comput. Simul. 73 (2003), pp. 333–346], Hartung and Knapp [On confidence intervals for the among-group variance in the one-way random effects model with unequal error variances, J. Statist. Plann. Inference 127 (2005), pp. 157–177], Li [Comparison of confidence intervals on between group variance in unbalanced heteroscedastic one-way random models, Comm. Statist. Simulation Comput. 36 (2007), pp. 381–390]. Moreover, it is relatively easy to compute.

A note on fiducial generalized pivots for in one-way heteroscedastic ANOVA with random effects

The paper deals with generalized confidence intervals for the between-group variance in one-way heteroscedastic (unbalanced) ANOVA with random effects. The approach used mimics the standard one applied in mixed linear models with two variance components, where interval estimators are based on a minimal sufficient statistic derived after an initial reduction by the principle of invariance. A minimal sufficient statistic under heteroscedasticity is found to resemble its homoscedastic counterpart and further analogies between heteroscedastic and homoscedastic cases lead us to two classes of fiducial generalized pivots for the between-group variance. The procedures suggested formerly by Wimmer and Witkovský [Between group variance component interval estimation for the unbalanced heteroscedastic one-way random effects model, J. Stat. Comput. Simul. 73 (2003), pp. 333–346] and Li [Comparison of confidence intervals on between group variance in unbalanced heteroscedastic one-way random models, Comm. Statist. Simulation Comput. 36 (2007), pp. 381–390] are found to belong to these two classes. We comment briefly on some of their properties that were not mentioned in the original papers. In addition, properties of another particular generalized pivot are considered.

Tolerance intervals in a two-way nested model with mixed or random effects

We address the problem of deriving a one-sided tolerance interval in a two-way nested model with mixed or random effects. The generalized confidence interval idea is used in the derivation of our tolerance limit, and the results are obtained by suitably modifying the approach in Krishnamoorthy and Mathew [Krishnamoorthy, K. and Mathew, T., 2004, Generalized confidence limits and one-sided tolerance limits in balanced and unbalanced one-way random models. Technometrics, 46, 44–52], for the one-way random model. Our proposed tolerance limit can be estimated by Monte Carlo simulation. We have also been able to develop closed form approximations in some cases. The performance of our tolerance interval is numerically investigated and found to be satisfactory. The results are illustrated with an example.

Comparison of One-Sided Tolerance Limits in Unbalanced One-Way Random Models

ABSTRACT The objective of this article is to provide and evaluate a new method for constructing tolerance limits in unbalanced one-way random models via generalized inference. A simulation study is conducted to compare the proposed method with that of Krishnamoorthy and Mathew (2004). The simulation results suggest that the proposed method performs well in all designs considered.

One- and Two-Sided Tolerance Intervals for General Balanced Mixed Models and Unbalanced One-Way Random Models

In this article we develop procedures for one- and two-sided tolerance intervals for normal general linear models in which there exists a set of independent scaled chi-squared random variables. The proposed procedures are based on the concept of generalized pivotal quantities and are applicable to general mixed models provided that balanced data are available. However, this study focuses on situations involving unbalanced data. Specific attention is given to the unbalanced one-way random model. It is shown that the use of generalized pivotal quantities allows the construction of the tolerance intervals of interest fairly straightforward. Some practical examples are given to illustrate the proposed procedures. Furthermore, detailed statistical simulation studies are conducted to evaluate their performance, showing that the proposed procedures can be recommended for practical use.

One-Sided Tolerance Limits in Balanced and Unbalanced One-Way Random Models Based on Generalized Confidence Intervals

We consider the problem of deriving one-sided tolerance intervals in the one-way random model with balanced as well as unbalanced data, under the usual normality assumptions. The problems investigated deal with the computation of such intervals for the observable random variable, as well as the unobservable random effect in the one-way random model. The tolerance limits are derived using the concept of a generalized confidence interval. Some approximations are derived for the tolerance limits, and their performance is investigated by simulation. The simulation results show that the proposed tolerance limits are quite satisfactory for practical use.

The Effect of Design Imbalance on the Power of the F‐test of a Variance Component in the One‐Way Random Model

AbstractThe ANOVA‐based F‐test used for testing the significance of the random effect variance component is a valid test for an unbalanced one‐way random model. However, it does not have an uniform optimum property. For example, this test is not uniformly most powerful invariant (UMPI). In fact, there is no UMPI test in the unbalanced case (see Khuri, Mathew, and Sinha, 1998).The power of the F‐test depends not only on the design used, but also on the true values of the variance components. As Khuri (1996) noted, we can gain a better insight into the effect of data imbalance on the power of the F‐test using a method for modelling the power in terms of the design parameters and the variance components. In this study, generalized linear modelling (GLM) techniques are used for this purpose. It is shown that GLM, in combination with a method of generating designs with a specified degree of imbalance, is an effective way of studying the behavior of the power of the F‐test in a one‐way random model.

Statistical Tests for Mixed Linear Models

Nature of Exact and Optimum Tests in Mixed Linear Models. Balanced Random and Mixed Models. Measures of Data Imbalance. Unbalanced One-Way and Two-Way Random Models. Random Models with Unequal Cell Frequencies in the Last Stage. Tests in Unbalanced Mixed Models. Recovery of Inter-Block Information. Split-Plot Designs Under Mixed and Random Models. Tests Using Generalized P-Values. Multivariate Mixed and Random Models. Appendix. General Bibliography. Indexes.

Choice of noninformative priors for the variance components of an unbalanced one-way random model

Several noninformative priors for the variance components of an unbalanced one-way model are proposed. A simulation study is performed to compare the posterior means of the variance components based on the different priors. These estimators are also compared to the restricted maximum likelihood estimator (REML). Under the squared error loss, this simulation study indicates that the invariant priors lead to the optimal estimators of the variance components.

Hypotheses tests for variance components in some multivariate mixed models

Abstract A multivariate mixed model involving exactly one random effect is considered and a locally best invariant (LBI) test is derived for testing the significance of the multivariate variance component corresponding to the random effect. For balanced models, the likelihood ratio test (LRT) is also derived for the same testing problem. In general, the LBI test is different from Pillai's trace test, except for balanced models. The standard multivariate tests based on Wilk's Λ, Pillai's trace and Roy's maximum root are also valid tests for this problem and simulated powers of these tests and the LBI test are reported for several bivariate unbalanced one-way random models. As expected, the LBI test has some advantage over the others for local alternatives. For several bivariate balanced one-way random models, simulation results are also given on the power of the LRT and the tests based on Wilk's Λ, Pillai's trace and Roy's maximum root in order to compare these tests.

Quadratic unbiased estimation without invariance and its application in the unbalanced one-way random model

Quadratic unbiased estimation in a general mixed model is considered. It was recently proved in Stępniak (1987, Ann. Inst. Statist. Math. 39, 563–573) that any admissible estimator in the problem is a limit of unique locally best quadratics in a wide sense. We focus on the usual locally best quadratic unbiased estimators (LBQUE's). It is shown, that any locally best invariant quadratic unbiased estimator (or MINQUE with invariance) is a limit of LBQUE's. Explicit formulae for LBQUE's and their variances are given. The unbalanced one-way random model is considered in detail.

Distribution of variance ratio in unbalanced random model under heterogeneity of error variances

The effects of heteroscedasticity have been studied on the mean and variance of F ratio and on the power of F-test in unbalanced one-way random model, numerically. The computed results reveal that the heteroscedasticity and unbalanoedness have combined effects. The mean and variance of F as well as the power of F-test increase with inequality of error variances under balanced and those unbalanced situations where more variable groups have larger size. The effects are of serious nature when more variable groups have smaller size.

On invariant quadratic unbiased estimation of variance components

The present study deals with three different invarint quadratic unbiased estimators (IQUE) for variance components namely quadratic least squares estimators (QLSE), weighted quadratic least squares estimators (WQLSE) and Mitra type estimators (MTE). The variance and covariances of these three different estimators are presented for unbalanced one-way random model. The relative performances of these estimators are assessed based on different optimality criteria like, D-optimality, T-optimality and M-optimality together with variances of these estimators. As a result, it has been shown that MTE has optimal properties.

On the distributions of components sums of squares in one-way unbalanced random model under heter06ene0us error variances

Exact sampling distributions of sums of squares in the unbalanced one-way random model are obtained under heterogeneous error variances. These distributions are used to investigate the effect of heteroscedasticity and unbalancedness on the probability of negative estimate of the group variance component. The computed results reveal that heteroscedasticity affects the probability of negative estimate in all situations of group sizes. Further, the probability decreases with heterogeneity of error variances for balanced situations and increases with variability among group size for equal error variances case.

A Bayesian Approach to the Estimation of Variance Components for the Unbalanced One-Way Random Model

The estimation of variance components in the one-way random model with unequal sample sizes is studied. A simulation study that indicates that modes of posterior distributions have good sampling properties compared with other estimators is presented. The posterior distributions are calculated using a noninformative prior distribution that is uniform on the intraclass correlation. A simulation study for the estimation of the ratio of the variance components is also presented, together with a study of the sampling properties of highest posterior density regions for this ratio, Bayesian estimators appear to be viable competitors to the many classical alternatives in a sampling framework.

A Bayesian Approach to the Estimation of Variance Components for the Unbalanced One-Way Random Model

A Bayesian Approach to the Estimation of Variance Components for the Unbalanced One-Way Random Model

On the Non-Null Distribution of Anova F-Ratio in One Way Unbalanced Random Model

Expressions for the non-null distribution of analysis of variance F-ratio and the power of F-test in the unbalanced one-way random model have been obtained. The exact values of power of the test have been worked out, numerically, and compared with those obtained by using ( i) the Laguerre polynomial and (ii) the two moments approximations. The computed results reveal that the two moments approximation, for the non-null distribution of F-ratio, is quite good and there is no need for the Laguerre polynomial approimation, which is computationally very tedious for practical applications.