Detection of the numbers of outliers present in a data set using an information theoretic criterion

We use an information theoretic criterion proposed by Zhao, Krishnaiah and Bai (1986) to detect the number of outliers in a data set. We consider univariable mean-slippage and dispersion-slippage outlier structure of the observations. Multivariate generalizations and the consistency of the estimates are also considered. Numerical examples are presented in tables.     

Detection of outliers in growth curve models

The growth curve model introduced by Potthoff and Roy (1964) is a general statistical model which includes as special cases regression models and both univariate and multivariate analysis of variance models. In this paper, we discuss procedures for detection of outliers in growth curve models for mean-slippage and dispersion-slippage outlier model. The distributions of the test statistics are discussed and the values of significant probabilities are given using Bonferronl's bounds. Some simulation results are also presented.     

Profile Likelihood Based Confidence Intervals for Common Intraclass Correlation Coefficient

In this article, we propose an approach for estimating the confidence interval of the common intraclass correlation coefficient based on the profile likelihood. Comparisons are made with a procedure using the concept of generalized pivots. The method presented is less computationally demanding than the method using generalized pivots. The approach also provides better coverage, and shorter lengths of confidence intervals for the case when the value of the common intraclass correlation coefficient is low. The lengths of confidence intervals given by both methods are quite comparable for high but less realistic values of the common intraclass correlation coefficient.     

ROBUST M-ESTIMATION OF THE INTRACLASS CORRELATION COEFFICIENT

Robust M-estimators of intraclass correlation coefficient, location and scale parameters are defined for familial data. It is shown that these estimators are strongly consistent. Also the asymptotic distributions of these estimators are derived when the underlying distribution is elliptically and permutationally symmetric.     

Bayes Estimation of Intraclass Correlation Coefficients Under Unequal Family Sizes

Bayesian inference for the intraclass correlation ρ is considered under unequal family sizes. We obtain the posterior distribution of ρ and then compare the performance of the Bayes estimator (posterior mean of ρ) with that of Srivastava's (1984) estimator through simulation. Simulation study shows that the Bayes estimator performs better than the Srivastava's estimator in terms of lower mean square error. We also obtain large sample posteriors of ρ based on the asymptotic posterior distribution and based on the Laplace approximation.     

Test for Generalized Variance in Factor Analysis Model

We derive a likelihood ratio test for generalized variance a in factor analysis model. The asymptotic distribution of the test statistic follows chi-square distribution with one degree of freedom from a general theory of likelihood ratio test.     

Detection of outliers in familial data

Procedures for detection of outliers in familial data is given for mean-slippage and dispersion-slippage model of outliers for equal and unequal family sizes. The distributions of the test statistics are derived and Bonferroni's bounds for the values of significant probabilities are given.     

A Small Sample Test for the Equality of Intraclass Correlation Coefficients Under Unequal Family Sizes for Several Populations

The set of all distinct blocks of a BIBD(v,b,r,k,λ) is referred to as the support of the design. In this paper, the family of BIB designs with v=9 and k=3 is studied from the view of possible support sizes, b*'s. A table is constructed of designs with support sizes belonging to {12,18,20,21,…,84}, for minimum possible b in each case and for any larger admissible b. In constructing this table the methods of trade-off and composition of designs are utilized