Showing papers on "Heteroscedasticity published in 1978"

Using Residuals Robustly I: Tests for Heteroscedasticity, Nonlinearity

Abstract: We study the asymptotic power functions of tests for heteroscedasticity and nonlinearity in the linear model which were proposed by Anscombe and introduce and study some competitors robust against gross errors.

Exact Analysis of Variance with Unequal Variances: Test Procedures and Tables

Abstract: The level and power of the usual F-test for the r-way layout in analysis of variance (ANOVA) are vitiated when the assumption of equality of error variances is violated. We present procedures, with tables and approximations needed for implementation, which give exact tests with power and level completley independent of the unknown variances. Thus. the assumption of equal variances may be dropped.

Heteroscedasticity and Stratification in Error Components Models

Abstract: Classical error components models can be gene ralized by assuming that individual and temporal variances may vary between subsets of individuals and periods respectively. In this case, the variance covariance matrix of the disturbances can still be studied thoroughly, though not as completely as in the classical case. Its spectral decomposition, however, suggests an estimation procedure which is asymptotically efficient and computationnally tractable. Moreover, when specific non random individual and temporal effects are added, gene ralized least squares estimation amounts to per forming ordinary least squares on suitably trans formed data. * Unit? de Recherche, INSEE.

A Class of Parametric Tests for Heteroscedasticity in Linear Econometric Models

Abstract: A class of parametric tests for heteroscedasticity in linear models is discussed. For models with nonstochastic regressors, new exact tests within this class are suggested which utilize existing tables of the distribution of the von Neumann ratio and of the Durbin-Watson bounding ratios. "Bound tests" for heteroscedasticity in least squares regression are proposed. A rigorous treatment of tests within this class for heteroscedasticity in the errors of structural relations in dynamic simultaneous equations models is provided.

An Empirical Investigation of the Effect of Heteroscedasticity and Heterogeneity of Variance on the Analysis of Covariance and the Johnson-Neyman Technique.

Abstract: : The robustness of the Johnson-Neyman technique and analysis of covariance (ANCOVA) to violations of assumptions of homoscedasticity and homogeneity of variance was tested through the use of Monte Carlo computer procedures The study simulated a one-way, fixed-effects analysis with two treatment groups, one criterion, and one covariate Five fixed values of the covariate were selected with zero mean and unit variance, while the values of Y were varied randomly with a constant regression coefficient of 75 Four combinations of group sizes (10,10;10,20;20,10;20,20), five combinations of group variances (1,1;1,2;2,1;1,5;5,1), and five forms of heteroscedasticity (combined in 18 different pairs), were studied These conditions were combined to produce 186 different simulated experimental conditions For each simulated condition 3000 pseudo-random samples were generated and sampling distributions relevant to the Johnson-Neyman technique and ANCOVA were compiled

On quadratic estimation of heteroscedastic variances

Abstract: Beginning with a brief introduction to the general theory the concept of Bayes invariant quadratic unbiased estimators (BAIQUEs) founded by Kleffe and Pingus(1974)is applied to combined samples with a common mean and different variances.Explicite formulas for Baique under these special assumptions are derived.Finally,some numerical comparisons of the variance function of Baiques under different prior distributions are given.