Power of a test for equality of k exponential location parameters

Kambo and Awad (1985) defined a test statistic based on doubly censored samples to test the equality of location parameters of K exponential distributions when their common scale parameter is unknown. The power function of the test is derived in this paper and some special cases are studied.     

Robust durbin's and portmanteau goodness-of-fit test statistics and their limit distributions

Robust analogue of Durbin's(1970)statistic is derived and its limiting distribution is obtained under both null and alternative hypotheses. Also, robust version of the portmanteau goodness-of-fit test statistic for AR(p)model is given and the asymptotic distribution is derived.     

Distances between normal populations when covariance matrices are unequal

The definition of distance between two populations of equal covariance matrices is extended to two and more than two populations with unequal covariance matrices and Rao’s U test for testing the conditional contribution of a subset of variables to the distance is extended to this situation, even when sample sizes are not necessarily the same.     

Some remarks on the chi-squared test with both margins fixed

Under the hypothesis of independence, the chi-squared test statistic for independence in a two-way contingency table follows an asymptotic chi-squared distribution under both a multinomial and a product-multinomial models. Alalouf(1987) showed the same result holds for the third case where both margins are fixed. In this paper an intuitively easier way of proof using the conditional limit theorems is suggested and some points are discussed.     

Goodness of Fit Statistics for the Exponential Distribution When the Data Are Grouped

Abstract

In many industrial and biological experiments, the recorded data consist of the number of observations falling in an interval. In this paper, we develop two test statistics to test whether the grouped observations come from an exponential distribution. Following the procedure of Damianou and Kemp (Damianou, C., Kemp, A. W. (1990 Damianou, C. and Kemp, A. W. 1990. New goodness of statistics for discrete and continuous data. American Journal of Mathematical and Management Sciences, 10: 275–307. [Taylor & Francis Online] , [Google Scholar]). New goodness of statistics for discrete and continuous data. American Journal of Mathematical and Management Sciences 10:275–307.), Kolmogrov–Smirnov type statistics are developed with the maximum likelihood estimator of the scale parameter substituted for the true unknown scale. The asymptotic theory for both the statistics is studied and power studies carried out via simulations.     

Durbin–Watson and Generalized Durbin–Watson Tests for Autocorrelations and Randomness

The Durbin–Watson (DW) test for lag 1 autocorrelation has been generalized (DWG) to test for autocorrelations at higher lags. This includes the Wallis test for lag 4 autocorrelation. These tests are also applicable to test for the important hypothesis of randomness. It is found that for small sample sizes a normal distribution or a scaled beta distribution by matching the first two moments approximates well the null distribution of the DW and DWG statistics. The approximations seem to be adequate even when the samples are from nonnormal distributions. These approximations require the first two moments of these statistics. The expressions of these moments are derived.     

Central Limit Theorems for Reduced U‐Statistics Under Dependence and Their Usefulness

A reduced ‐statistic is a ‐statistic with its summands drawn from a restricted but balanced set of pairs. In this article, central limit theorems are derived for reduced ‐statistics under ‐mixing, which significantly extends the work of Brown & Kildea in various aspects. It will be shown and illustrated that reduced ‐statistics are quite useful in deriving test statistics in various nonparametric testing problems.     

Identifying Variables Contributing to Outliers in Phase I

When a process is monitored with a T ² control chart in a Phase II setting, the MYT decomposition is a valuable diagnostic tool for interpreting signals in terms of the process variables. The decomposition splits a signaling T ² statistic into independent components that can be associated with either individual variables or groups of variables. Since these components are T ² statistics with known distributions, they can be used to determine which of the process variable(s) contribute to the signal. However, this procedure cannot be applied directly to Phase I since the distributions of the individual components are unknown. In this article, we develop the MYT decomposition procedure for a Phase I operation, when monitoring a random sample of individual observations and identifying outliers. We use a relationship between the T ² statistic in Phase I with the corresponding T ² statistic resulting when an observation is omitted from this sample to derive the distributions of these components and demonstrate the Phase I application of the MYT decomposition.