AN EXACT CONDITIONAL TEST FOR COVARIANCE SELECTION MODELS

An exact conditional test is developed for testing the absence of an edge in a graphical covariance selection model and is shown to be equivalent to a test based on the partial correlation coefficient. An example is given.     

Hypothesis testing for quantiles of two-parameter binary models

A procedure is developed to test the equality of the quantiles from k populations, assuming the responses follow a two-parameter binary model.The method utilizes the asymptotic distribution of the maximum likelihood estimators.The exact distribution of the test statistic is discussed in general.This exact distribution is generated for the logit model in order to investgate the convergence properties of the asymptotic procedure.     

Tables of the exact quantiles of the top-down correlation coefficient for n - 3(1)14

Iman and Connver (1985, 1987) have suggested the top-down correlation coefficient as a measure of association when n objects are ranked by two or more independent sources and interest centers primarily on agreement in the top rankings, with disagreements on items at the bottom of the rankings being of little or no importance. The top-down correlation coefficient results from computing the ordinary Pearson correlation coefficient on Savage scores. Quantiles of the exact distribution of the top-down correlation coefficient based on the assumption of independent rankings are provided for n = 3(1)14.     

A p-value for testing the equivalence of the variances of a bivariate normal distribution

A p-value is developed for testing the equivalence of the variances of a bivariate normal distribution. The unknown correlation coefficient is a nuisance parameter in the problem. If the correlation is known, the proposed p-value provides an exact test. For large samples, the p-value can be computed by replacing the unknown correlation by the sample correlation, and the resulting test is quite satisfactory. For small samples, it is proposed to compute the p-value by replacing the unknown correlation by a scalar multiple of the sample correlation. However, a single scalar is not satisfactory, and it is proposed to use different scalars depending on the magnitude of the sample correlation coefficient. In order to implement this approach, tables are obtained providing sub-intervals for the sample correlation coefficient, and the scalars to be used if the sample correlation coefficient belongs to a particular sub-interval. Once such tables are available, the proposed p-value is quite easy to compute since it has an explicit analytic expression. Numerical results on the type I error probability and power are reported on the performance of such a test, and the proposed p-value test is also compared to another test based on a rejection region. The results are illustrated with two examples: an example dealing with the comparability of two measuring devices, and an example dealing with the assessment of bioequivalence.     

A robust test for testing the correlation coefficient

A test based on Tiku's MML (modified maximum likelihood) estimators is developed for testing that the population correlation coefficient is zero. The test is compared with various other tests and shown to have good Type I error robustness and power for numerous symmetric and skew bivariate populations.     

Rank correlation plots for use with correlated input variables

A method for inducing a desired rank correlation matrix on multivariate input vectors for simulation studies has recently been developed by Iman and Conover (1982). The primary intention of this procedure is to produce correlated input variables for use with computer models. Since this procedure is distribution free and allows the exact marginal distributions to remain intact it can be used with any marginal distributions for which it is reasonable to think in terms of correlation. In this paper we present a series of rank correlation plots based on this procedure when the marginal distributions are normal, lognormal, uniform and loguniform. These plots provide a convenient tool both for aiding the modeler in determining the degree of dependence among input variables (rather than guessing) and for communicating with the modeler the effect of different correlation assumptions. In addition this procedure can be used with sample multivariate data by sampling directly from the respective marginal empirical distribution functions.     

Hermite polynomials and truncated trivariate normal distributions

The moments of a trivariate and in general of a multivariate normal distribution, which is truncated with respect to a single variable, are obtained by using properties of Hermite polynomials. An expression for the truncated correlation coefficient is derived in terms of the true population correlation coefficient and the truncation point. The values of this truncated correlation coefficient are tabulated for given values of the true correlation coefficient and a few selected values of the truncation point. A listing of the computer program for this purpose is also given.     

The Complete Exact Null Distribution of Spearman's Rho for n = 12(1)18

Spearman's rank correlation coefficient, Rho, is a widely used nonparametric measure of association. Complete, exact tables of the null distribution are calculated and presented for n = 12 to 18.     

Multivariate outlier tests with structured co variance matrices

Outlier tests are developed for multivariate data where there is a structure to the covariance or correlation matrix. Particular structures considered are the block diagonal structure where there are reasons to assume that one set of variables is independent of another, and the equicorrelation structure where it may be assumed that all pairs of variables have the same correlation. Likelihood ratio tests for an outlier are derived for these situations and critical values, under the null hypothesis of no outliers present, are determined for selected sample sizes and dimensions, using Bonferroni bounds or simulation. The powers of the tests are compared with those of the Wilks′ statistic for a variety of situations. It is shown that the test procedures which incorporate knowledge of the correlation structure have considerably greater power than the usual tests particularly in relatively small samples with several dimensions.     

Permutation Tests for Linear Models

Several approximate permutation tests have been proposed for tests of partial regression coefficients in a linear model based on sample partial correlations. This paper begins with an explanation and notation for an exact test. It then compares the distributions of the test statistics under the various permutation methods proposed, and shows that the partial correlations under permutation are asymptotically jointly normal with means 0 and variances 1. The method of Freedman & Lane (1983) is found to have asymptotic correlation 1 with the exact test, and the other methods are found to have smaller correlations with this test. Under local alternatives the critical values of all the approximate permutation tests converge to the same constant, so they all have the same asymptotic power. Simulations demonstrate these theoretical results.     

Robust designs for experiments with blocks

ABSTRACT

For experiments running in field plots or over time, the observations are often correlated due to spatial or serial correlation, which leads to correlated errors in a linear model analyzing the treatment means. Without knowing the exact correlation matrix of the errors, it is not possible to compute the generalized least-squares estimator for the treatment means and use it to construct optimal designs for the experiments. In this paper, we propose to use neighborhoods to model the covariance matrix of the errors, and apply a modified generalized least-squares estimator to construct robust designs for experiments with blocks. A minimax design criterion is investigated, and a simulated annealing algorithm is developed to find robust designs. We have derived several theoretical results, and representative examples are presented.     

Estimating correlation from dichotomized normal variables

Given dichotomized data from a bivariate normal distribution, the tetrachoric correlation coefficient provides a reasonable estimate of Pearson's correlation between the underlying variables. Greer et al. [2003. A Monte Carlo evaluation of the tetrachoric correlation coefficient. Educ. Psychol. Meas. 63, 931–950] suggested that this may be the case also under suitable transformations of the margins. As a complement to their work, this paper considers the estimation of Pearson's correlation between variables that are normal, but not jointly. A small Monte Carlo study is used to assess whether various approximations of the tetrachoric correlation coefficient could be helpful in this context. The results are encouraging, in terms of both bias and mean-square error.     

Testing for heteroscedasticity of exponential correlation mixed-effects linear models based on M-estimation

Homogeneity of variance is a basic assumption in longitudinal data analysis. However, the assumption is not necessarily appropriate. In this paper, Fisher scoring method is applied to get M-estimator in the exponential correlation mixed-effects linear model. The score tests for heteroscedasticity and correlation coefficient based on M-estimator are then studied. Monte Carlo method is applied to investigate the properties of test statistics. At last, the methods and properties are illustrated by an actual data example.     

Test for the equality of interclass correlation cofefficients under unequal family sizes for several populations

Large sample ANOVA test and likelihood ratio test have been Proposed to test for the equaiity of sevcrai iniraciass correlation coefiicients unier unequai family sizes based on several independent multinormal samples, it has been found on the basis of simulation study that the likelihood ratio test consistenily and reliably produced results superior to those of the large sample ANOVA test in terms of power for various combinations of intraclass correlatxon coefficient values.     

Testing for Common Features

This article introduces a class of statistical tests for the hypothesis that some feature that is present in each of several variables is common to them. Features are data properties such as serial correlation, trends, seasonality, heteroscedasticity, autoregressive conditional hetero-scedasticity, and excess kurtosis. A feature is detected by a hypothesis test taking no feature as the null, and a common feature is detected by a test that finds linear combinations of variables with no feature. Often, an exact asymptotic critical value can be obtained that is simply a test of overidentifying restrictions in an instrumental variable regression. This article tests for a common international business cycle.     

A Test of Independence in Two-Way Contingency Tables Based on Maximal Correlation

Maximal correlation has several desirable properties as a measure of dependence, including the fact that it vanishes if and only if the variables are independent. Except for a few special cases, it is hard to evaluate maximal correlation explicitly. We focus on two-dimensional contingency tables and discuss a procedure for estimating maximal correlation, which we use for constructing a test of independence. We compare the maximal correlation test with other tests of independence by Monte Carlo simulations. When the underlying continuous variables are dependent but uncorrelated, we point out some cases for which the new test is more powerful.     

Estimation of the Intraclass Correlation Coefficient for the Analysis of Covariance Model

For the unbalanced analysis of covariance model with one covariate, a simple formula is given for the intraclass correlation coefficient estimator that results from Henderson's Method 3 estimation of variance components. Example calculations and the corresponding interpretations are given for a study of the correlation of iron content among brothers. The example illustrates the manner in which the estimator depends on the pattern of correlation between the covariate and the variable under investigation.     

A WEIGHTED RANK MEASURE OF CORRELATION

Spearman's rank correlation coefficient is not entirely suitable for measuring the correlation between two rankings in some applications because it treats all ranks equally. In 2000, Blest proposed an alternative measure of correlation that gives more importance to higher ranks but has some drawbacks. This paper proposes a weighted rank measure of correlation that weights the distance between two ranks using a linear function of those ranks, giving more importance to higher ranks than lower ones. It analyses its distribution and provides a table of critical values to test whether a given value of the coefficient is significantly different from zero. The paper also summarizes a number of applications for which the new measure is more suitable than Spearman's.     

Comparing Equal-Tail Probability and Unbiased Confidence Intervals for the Intraclass Correlation Coefficient

The conventional confidence interval for the intraclass correlation coefficient assumes equal-tail probabilities. In general, the equal-tail probability interval is biased and other interval procedures should be considered. Unbiased confidence intervals for the intraclass correlation coefficient are readily available. The equal-tail probability and unbiased intervals have exact coverage as they are constructed using the pivotal quantity method. In this article, confidence intervals for the intraclass correlation coefficient are built using balanced and unbalanced one-way random effects models. The expected length of confidence intervals serves as a tool to compare the two procedures. The unbiased confidence interval outperforms the equal-tail probability interval if the intraclass correlation coefficient is small and the equal-tail probability interval outperforms the unbiased interval if the intraclass correlation coefficient is large.