INTRINSIC PRIORS FOR TWO-SAMPLE TESTS IN NORMAL POPULATIONS

ABSTRACT

There have been considerable amounts of work regarding the development of various default Bayes factors in model selection and hypothesis testing. Two commonly used criteria, the intrinsic Bayes factor and the fractional Bayes factor are compared to test two independent normal means and variances. We also derive several intrinsic priors whose Bayes factors are asymptotically equivalent to the respective Bayes factors. We demonstrate our results in simulated datasets.     

ON ASYMPTOTIC OPTIMALITY OF SOME TESTS FOR EXPONENTIAL DISTRIBUTIONS

The probability density function (pdf) of a two parameter exponential distribution is given by f(x; p, s?) =s?^-1 exp {-(x - ρ)/s?} for x≥ρ and 0 elsewhere, where 0 < ρ < ∞ and 0 < s?∞. Suppose we have k independent random samples where the ith sample is drawn from the ith population having the pdf f(x; ρ_i, s?_i), 0 < ρ_i < ∞, 0 < s?_i < s?_i < and f(x; ρ, s?) is as given above. Let X_i1 < X_i2 <… < X_iri denote the first r_i order statistics in a random sample of size n_i, drawn from the ith population with pdf f(x; ρ_i, s?_i), i = 1, 2,…, k. In this paper we show that the well known tests of hypotheses about the parameters ρ_i, s?_i, i = 1, 2,…, k based on the above observations are asymptotically optimal in the sense of Bahadur efficiency. Our results are similar to those for normal distributions.     

BAHADUR SLOPES FOR COMPARISONS OF TESTS BASED ON RESAMPLING

Let X₁,…, X_n be random variables symmetric about θ from a common unknown distribution F_θ(x) =F(x–θ). To test the null hypothesis H₀:θ= 0 against the alternative H₁:θ > 0, permutation tests can be used at the cost of computational difficulties. This paper investigates alternative tests that are computationally simpler, notably some bootstrap tests which are compared with permutation tests. Of these the symmetrical bootstrap-f test competes very favourably with the permutation test in terms of Bahadur asymptotic efficiency, so it is a very attractive alternative.     

ON ENTROPY BASED TESTS FOR EXPONENTIALITY

ABSTRACT

Recent literature has proposed a test for exponentiality based on sample entropy. We consider transformations of the observations which turn the test of exponentiality into one of uniformity and use a corresponding test based on entropy. The test based on the transformed variables performs better in many cases of interest.     

A NOTE ON OPTIMUM STRATIFICATION OF POPULATIONS FOR ESTIMATING THE POPULATION MEANS

The note gives the results of a study carried out to find the optimum points of stratification (OPS) for estimating the population means of some standard distributions. The distributions considered here are the normal and the set of chi-square distributions. The OPS for the various gamma distributions can be easily derived from the known OPS of the corresponding chi-square distributions. The OPS depend on the type of allocation envisaged. In this note attention has been confined to the proportional, equal, and optimum allocations.
Tables of OPS are given for the distributions and allocations mentioned above. Some other interesting results follow:
(i) Equalization of stratum totals as suggested by Ransen, Hurwitz and Madow (1953) does not lead to OPS for any of the populations considered.
(ii) Equalization of {f(x)}^½ dx gives an excellent approximabioii to the OPS for both equal and optimum allocations.
(iii) The OPS for equal and optimum allocations almost coincide. To put in other words, if strata are defined by OPS optimum allocation differs only slightly from equal allocation.
New rules are suggested for the family of distributions considered in this paper for all the three types of allocations.     

GOODNESS-OF-FIT TESTS FOR FISHER'S DISTRIBUTION ON THE SPHERE

A set of three goodness-of-fit procedures is proposed to investigate the adequacy of fit of Fisher's distribution on the sphere as a model for a given sample of spherical data. The procedures are all based on standard tests using the empirical distribution function.     

ON LEVENE'S TESTS OF VARIANCE HOMOGENEITY

Levene's tests of homogeneity of treatment variances in completely randomised and randomised complete block experiments are examined. These tests are essentially standard analysis of variance F-tests performed on functions of the absolute values of residuals. It is found that in order to achieve (i) equality of component mean squares under the null hypothesis, and (ii) nominal significance levels, the various standard degrees of freedom need to be modified.     

A COMPARISON OF TESTS OF UNIFORMITY FOR SPHERICAL DATA

A comparison is made af three tests of uniformity for spherical vector data, and of the corresponding tests of uniformity for spherical axial data.     

ON TESTS FOR INTERACTION IN A NONREPLICATED TWO-WAY LAYOUT

The paper proposes tests for interaction in a two-way table with one observation per cell. The power of these tests is independent of the additive main effects in the linear model. This is an advantage compared to a test suggested earlier, which has low power if main effects are very variable.     

BAYESIAN TESTS FOR SOME PRECISE HYPOTHESES ON MULTI-NORMAL MEANS

Bayesian counterparts of some standard tests concerning the means of multi-normal distribution are discussed. In particular the hypothesis that the multi-normal mean is equal to a specified value, and the hypothesis that the means are equal. Lower bounds on the Bayes factor in favour of the null hypothesis are obtained over the class of conjugate priors. The P-value, or observed significance level of the standard sampling-theoretic test procedure are compared with the posterior probability. The results correspond closely with those of Good (1967), Berger & Sellke (1987), Pepple (1988) and others and illustrate the conflict between posterior probabilities and P-values as measures of evidence.     

A NEW TEST FOR EQUALITY OF VARIANCES FOR k NORMAL POPULATIONS

ABSTRACT

A simple test based on Gini's mean difference is proposed to test the hypothesis of equality of population variances. Using 2000 replicated samples and empirical distributions, we show that the test compares favourably with Bartlett's and Levene's test for the normal population. Also, it is more powerful than Bartlett's and Levene's tests for some alternative hypotheses for some non-normal distributions and more robust than the other two tests for large sample sizes under some alternative hypotheses. We also give an approximate distribution to the test statistic to enable one to calculate the nominal levels and P-values.     

A NONPARAMETRIC TEST OF SYMMETRY VERSUS ASYMMETRY FOR RANKED-SET SAMPLES

This paper introduces a nonparametric test of symmetry for ranked-set samples to test the asymmetry of the underlying distribution. The test statistic is constructed from the Cramér-von Mises distance function which measures the distance between two probability models. The null distribution of the test statistic is established by constructing symmetric bootstrap samples from a given ranked-set sample. It is shown that the type I error probabilities are stable across all practical symmetric distributions and the test has high power for asymmetric distributions.     

A NEW FAMILY OF DIVERGENCE MEASURES FOR TESTS OF FIT

The aim of this work is to investigate a new family of divergence measures based on the recently introduced Basu, Harris, Hjort and Jones (BHHJ) measure of divergence (Biometrika 85 , 549–559). The new family is investigated in connection with hypothesis testing problems, and new test statistics are proposed. Simulations are performed to check the appropriateness of the proposed test statistics.     

ON THE NULL DISTRIBUTIONS OF THE ENTROPY TESTS FOR THE GAUSSIAN AND INVERSE GAUSSIAN MODELS

Vasicek [1] Vasicek, O. 1976. A test for normality based on sample entropy. J. R. Statist. Soc. B, 38: 54–59.  [Google Scholar]used the “convolution of twelve uniforms” for a Monte Carlo tabulation of the 5% critical values for his entropy test for normality. We employ a superior normal generator to construct a corrected and extended tabulation for his test. Interestingly, it is shown that, the same tables can be used for implementing Mudholkar and Tian's [2] Mudholkar, G. S. and Tian, L. 1999. “An entropy characterization of the inverse Gaussian distribution and related goodness-of-fit test”. In Tech. Rep., University of Rochester Rochester, NY Submitted for publication [Google Scholar]entropy test for the composite inverse Gaussian hypothesis. The finding extends the known Gaussian, inverse Gaussian analogies.     

ON THE OPTIMAL ASYMPTOTIC TESTS FOR THE EFFECTS OF CLOUD SEEDING ON RAINFALL (1) THE CASE OF FIXED EFFECTS

In this paper we consider a sequence of experimental units {u_n} which are t o be treated according to some random scheme. A general randomized design is suggested for the purpose. Asymptotic tests, optimal in some sense, are derived for testing the absence of the effects of the treatment. These tests are applicable in various situations, for example, when the treatment effects are additive or when they are multiplicative. Based on the asymptotic power of the tests obtained, optimality of various designs is discussed. The randomized designs discussed here have a wide range of applicability, e.g. in weather modification experiments and bio-assay.     

MODIFICATION OF KELLY AND RICE METHOD FOR SYNERGISM TESTS

ABSTRACT

In this paper, we present a modified Kelly and Rice method for testing synergism. This approach is consistent with Berenbaum's1-3 Berenbaum, M.C. 1977. Synergism, Additivism, and Antagonism in Immuno-Suppression: A Critical Review. Clinical and Experimental Immunology, 28: 1–18. Berenbaum, M.C. 1985. The Expected Effect of a Combination of Agents: The General Solution. Journal of Theoretical Biology, 114: 413–431. Berenbaum, M.C. 1989. What is Synergism? Pharmacological Reviews. 41: 93–141.   framework for additivity. The delta method[4] Bishop, Y.M., Fienberg, S.E. and Holland, P.W. 1975. Discrete Multivariate Analysis: Theory and Practice Massachusetts: MIT Press.  [Google Scholar] is applied to obtain the estimated variance for the predicted additivity proportion. A Monte Carlo simulation study for the evaluation of the method's performance, i.e., global overall tests for synergism, is also discussed. Kelly and Rice[5] Kelly, C. and Rice, J. 1990. Monotone Smoothing with Application to Dose-Response Curves and the Assessment of Synergism. Biometrics, 46: 1071–1085. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar] do not provide a correct test statistic because the variance is underestimated. Hence, the performance of the Kelly–Rice[5] Kelly, C. and Rice, J. 1990. Monotone Smoothing with Application to Dose-Response Curves and the Assessment of Synergism. Biometrics, 46: 1071–1085. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar] method is generally anti-conservative, based on the simulation findings. In addition, the overall test of synergism with χ²(r) from the modified Kelly and Rice method for larger sample sizes is better than that with χ²(1) from the modified Kelly and Rice method.     

CRITICAL VALUE APPROXIMATIONS FOR TESTS OF LINEAR REGRESSION DISTURBANCES

Two important classes of tests for non-spherical disturbances in the linear regression model involve test statistics whose null distributions and hence critical values depend on the regressors. This paper investigates the accuracy of the normal, two moment beta and four moment beta approximations to the critical values of such tests. An empirical experiment aimed at evaluating the accuracy of the approximations for a variety of tests against autocorrelation and heteroscedasticity is conducted. Overall the approximations are found to provide reasonably accurate critical values with skewness being a factor determining the degree of accuracy.     

TESTS FOR INDEPENDENCE IN THE PRESENCE OF MISSING VALUES

The locally most powerful rank test is derived for testing independence of two random variables with possible missing observations on both responses. The test statistic has a simple form and can be easily obtained. For the purpose of practical use, the asymptotic null distribution of the test statistic is also provided.     

MORE INFORMATIVE TESTING FOR BIVARIATE SYMMETRY

In testing for bivariate symmetry against arbitrary alternatives the well‐known test developed by Bowker in 1948 is shown to be a score test, and to have useful components. These components are asymptotically independent and asymptotically have the standard normal distribution. Moreover they assess particular pairs of cells for symmetry. These components can also be used in a data analytic manner to complement a test for bivariate symmetry against ordered alternatives.