Inferences on a linear combination of K multivariate normal mean vectors

In this paper, the hypothesis testing and confidence region construction for a linear combination of mean vectors for K independent multivariate normal populations are considered. A new generalized pivotal quantity and a new generalized test variable are derived based on the concepts of generalized p-values and generalized confidence regions. When only two populations are considered, our results are equivalent to those proposed by Gamage et al. [Generalized p-values and confidence regions for the multivariate Behrens–Fisher problem and MANOVA, J. Multivariate Aanal. 88 (2004), pp. 117–189] in the bivariate case, which is also known as the bivariate Behrens–Fisher problem. However, in some higher dimension cases, these two results are quite different. The generalized confidence region is illustrated with two numerical examples and the merits of the proposed method are numerically compared with those of the existing methods with respect to their expected areas, coverage probabilities under different scenarios.     

Modified method on the means for several log-normal distributions

Among statistical inferences, one of the main interests is drawing the inferences about the log-normal means since the log-normal distribution is a well-known candidate model for analyzing positive and right-skewed data. In the past, the researchers only focused on one or two log-normal populations or used the large sample theory or quadratic procedure to deal with several log-normal distributions. In this article, we focus on making inferences on several log-normal means based on the modification of the quadratic method, in which the researchers often used the vector of the generalized variables to deal with the means of the symmetric distributions. Simulation studies show that the quadratic method performs well only for symmetric distributions. However, the modified procedure fits both symmetric and skew distribution. The numerical results show that the proposed modified procedure can provide the confidence interval with coverage probabilities close to the nominal level and the hypothesis testing performed with satisfactory results.     

Discussion on the common threshold of several exponential distributions with different rates

The integration of results of independent studies in order to make inferences about a common threshold is an important problem with many practical applications. In this article, we apply the generalized variable method to make inferences on the common threshold of several exponential distributions when the scale (or rate) parameters are unknown and unequal. The merits of the proposed method are computed numerically and compared with other existing methods. Numerical results of both simulation studies and real data analyses show that the proposed method is applicable and its performance is better than other methods even when sample sizes are small.     

Inferences on the common mean of several heterogeneous log-normal distributions

We consider the problem of making inferences on the common mean of several heterogeneous log-normal populations. We apply the parametric bootstrap (PB) approach and the method of variance estimate recovery (MOVER) to construct confidence intervals for the log-normal common mean. We then compare the performances of the proposed confidence intervals with the existing confidence intervals via an extensive simulation study. Simulation results show that our proposed MOVER and PB confidence intervals can be recommended generally for different sample sizes and number of populations.     

Inferences on correlation coefficients of bivariate log-normal distributions

This article considers inference on correlation coefficients of bivariate log-normal distributions. We developed generalized confidence intervals and hypothesis tests for the correlation coefficients, and extended the results to compare two independent correlations. Simulation studies show that the suggested methods work well. Two practical examples are used to illustrate the application of the proposed methods.     

Generalized inferential procedures for generalized Lorenz curves under the Pareto distribution

This paper considers problems of interval estimation and hypotheses testing for the generalized Lorenz curve under the Pareto distribution. Our approach is based on the concepts of generalized test variables and generalized pivotal quantities. The merits of the proposed procedures are numerically carried out and compared with asymptotic and bootstrap methods. Empirical evidence shows that the coverage accuracy of the proposed confidence intervals and the type I error control of the proposed exact tests are satisfactory. For illustration purposes, a real data set on median income of the 20 occupations in the United States Census of Population is analysed.     

Generalized inferences on the common mean vector of several multivariate normal populations

The hypothesis testing and confidence region are considered for the common mean vector of several multivariate normal populations when the covariance matrices are unknown and possibly unequal. A generalized confidence region is derived using the concepts of generalized method based on the generalized p$p$-value. The generalized confidence region is illustrated with two numerical examples. The merits of the proposed method are numerically compared with those of existing methods with respect to their expected area or expected d-dimensional volumes and coverage probabilities under different scenarios.     

Properties of two tests for outliers in multivariate data

Mardia's multivariate kurtosis and the generalized distance have desirable properties as multivariate outlier tests. However, extensive critical values have not been published heretofore. A published approximation formula for critical values of the kurtosis is shown to inadequately control the type I error rate, with observed error rates often differing from their intended values by a factor of two or more. Critical values derived from simulations for both tests for up to 25 dimensions and 500 observations are presented. The power curves of both tests are discussed. The generalized distance is the more powerful test when exactly one outlier is present and the contaminant is substantially mean-shifted. However, as the number of outliers increases, the kurtosis becomes the more powerful test. The two tests are compared with respect to power and vulnerability to masking. Recommendations for the use of these tests and interpretation of results are given.     

Generalized confidence interval estimation for the mean of delta-lognormal distribution: an application to New Zealand trawl survey data

Highly skewed and non-negative data can often be modeled by the delta-lognormal distribution in fisheries research. However, the coverage probabilities of extant interval estimation procedures are less satisfactory in small sample sizes and highly skewed data. We propose a heuristic method of estimating confidence intervals for the mean of the delta-lognormal distribution. This heuristic method is an estimation based on asymptotic generalized pivotal quantity to construct generalized confidence interval for the mean of the delta-lognormal distribution. Simulation results show that the proposed interval estimation procedure yields satisfactory coverage probabilities, expected interval lengths and reasonable relative biases. Finally, the proposed method is employed in red cod densities data for a demonstration.     

On the comparison of the Fisher information of the log-normal and generalized Rayleigh distributions

Surles and Padgett recently considered two-parameter Burr Type X distribution by introducing a scale parameter and called it the generalized Rayleigh distribution. It is observed that the generalized Rayleigh and log-normal distributions have many common properties and both distributions can be used quite effectively to analyze skewed data set. In this paper, we mainly compare the Fisher information matrices of the two distributions for complete and censored observations. Although, both distributions may provide similar data fit and are quite similar in nature in many aspects, the corresponding Fisher information matrices can be quite different. We compute the total information measures of the two distributions for different parameter ranges and also compare the loss of information due to censoring. Real data analysis has been performed for illustrative purposes.     

Testing the equality means of several log-normal distributions

Researches propose various methods for comparing the means of two log-normal distributions. Some of these methods have been recently extended to test the equality means of several log-normal populations. Investigations show that none of the established methods is satisfactory. In this article, we provide three methods based on the computational approach test, which is a parametric bootstrap approach, for testing the means of several log-normal distributions. Further, we compare our methods with the existing methods through Monte Carlo simulation. The numerical results show that the Type I errors of these procedures are satisfactory regardless of the sample size, number of populations, and the true parameters. Finally, we explain the considered methods by real examples.     

Unbiased and upper critical values of mean trace of multivariate beta for testing difference of two covariance matrices or several mean vectors

For the invariant unbiased level-α test of equality of two co-variance matrices, the quantities b and B satisfying the equations P(b≤T≤B) = 1-α, E(T|b≤T≤B) = E(T), where T is the mean trace of a multivariate beta, are required. Five and one per cent values of B are tabulated for m = 2,3(2)11,16; b can be obtained from B. Upper five and one per cent values of T are also included, as these are required for the locally most powerful invariant test of nullity of any source of difference in several mean vectors and the locally most powerful invariant one-sided test of equality of two covariance matrices. Lower critical values may be obtained from upper critical values.     

A power study of goodness-of-fit tests for multivariate normality implemented in R

Multivariate statistical analysis procedures often require data to be multivariate normally distributed. Many tests have been developed to verify if a sample could indeed have come from a normally distributed population. These tests do not all share the same sensitivity for detecting departures from normality, and thus a choice of test is of central importance. This study investigates through simulated data the power of those tests for multivariate normality implemented in the statistic software R and pits them against the variant of testing each marginal distribution for normality. The results of testing two-dimensional data at a level of significance α=5% showed that almost one-third of those tests implemented in R do not have a type I error below this. Other tests outperformed the naive variant in terms of power even when the marginals were not normally distributed. Even though no test was consistently better than all alternatives with every alternative distribution, the energy-statistic test always showed relatively good power across all tested sample sizes.     

Group sequential x2 statistic for testing the difference of two mean vectors in clinical trials

In this study we discuss the group sequential procedures for comparing two treatments based on multivariate observations in clinical trials. Also we suppose that a response vector on each of two treatments has a multivariate normal distribution with unknown covariance matrix. Then we propose a group sequential x² statistic in order to carry out repeated significance test for hypothesis of no difference between two population mean vectors. In order to realize the group sequential test where average sample number is reduced, we propose another modified group sequential x² statistic by extension of Jennison and Turnbull ( 1991 ). After construction of repeated confidence boundaries for making the repeated significance test, we compare two group sequential procedures based on two statistics regarding the average sample number and the power of the test in the simulations.     

CUSUM charts for monitoring the mean of a multivariate Gaussian process

In this paper control charts for the mean of a multivariate Gaussian process are considered. Using the generalized likelihood ratio approach and the sequential probability ratio test under an additional constraint on the magnitude of the change various types of CUSUM control charts are derived. It is analyzed under which conditions these schemes are directionally invariant. These charts are compared with several other control schemes proposed in literature. The performance of the charts is studied based on the maximum average delay.     

Comparing the overlapping of two independent confidence intervals with a single confidence interval for two normal population parameters

Two overlapping confidence intervals have been used in the past to conduct statistical inferences about two population means and proportions. Several authors have examined the shortcomings of Overlap procedure and have determined that such a method distorts the significance level of testing the null hypothesis of two population means and reduces the statistical power of the test. Nearly all results for small samples in Overlap literature have been obtained either by simulation or by formulas that may need refinement for small sample sizes, but accurate large sample information exists. Nevertheless, there are aspects of Overlap that have not been presented and compared against the standard statistical procedure. This article will present exact formulas for the maximum % overlap of two independent confidence intervals below which the null hypothesis of equality of two normal population means or variances must still be rejected for any sample sizes. Further, the impact of Overlap on the power of testing the null hypothesis of equality of two normal variances will be assessed. Finally, the noncentral t-distribution is used to assess the Overlap impact on type II error probability when testing equality of means for sample sizes larger than 1.     

Percentage points of the statistics for testing hypotheses on mean vectors of multivariate normal distributions with missing observations

A problem of testing of hypotheses on the mean vector of a multivariate normal distribution with unknown and positive definite covariance matrix is considered when a sample with a special, though not unusual, pattern of missing observations from that population is available. The approximate percentage points of the test statistic are obtained and their accuracy has been checked by comparing them with some exact percentage points which are calculated for complete samples and some special incomplete samples. The approximate percentage points are in good agreement with exact percentage points. The above work is extended to the problem of testing the hypothesis of equality of two mean vectors of two multivariate normal distributions with the same, unknown covariance matrix     

A set of independent sequential residuals for the multivariate regression model

In this paper a set of residuals for the multivariate linear regression model is introduced. These residuals are shown to be independent with known distributions which do not depend on the parameters of the model. Transformations of the mentioned residuals may be used to construct exact α goodness-of-fit tests for the multivariate regression model.