A general scheme for sequential treatment allocation vith balancing for prognostic factors in clinical trials

A general family of dynamic treatment allocations is defined, and it is shown that the permuted block procedure (Zelen 1974) and Begg and Iglewicz method (1980) are extreme choices in this family. A compromise method is suggested. The framework of this general family allows the relationships between these methods to be examined. By means of a simulation study these three methods plus the complete randomization method are compared in terms of efficiency and balance. The compromise method is shown to have good overall properties. In addition, an illustrative example is given     

Approximate maximum likelihood estimation in the presence of extra-poisson variation

A two-stage hierarchical model for analysis of discrete data with extra-Poisson variation is examined. The model consists of a Poisson distribution with a mixing lognormal distribution for the mean. A method of approximate maximum likelihood estimation of the parameters is proposed. The method uses the EM algorithm and approximations to facilitate its implementation are derived. Approximate standard errors of the estimates are provided and a numerical example is used to illustrate the method.     

Algorithms for frequency distributions: efficiency and generality comparisons

Five methods for forming empirical frequency distributions are outlined. A specific implementation of each is described, and theoretical comparison of their speed and storage is supplemented by simulation data to give a series of recommendations about the appropriateness of each for different situations. The index method is the fastest of those considered, but often uses excessive space. A method based on height-balanced trees is economical of space, and still has good speed. A method based on Quicksort is faster than the tree method, but uses more space.     

Estimation in two-parameter exponential distributions

Exponential distributions are used extensively in the field of life-testing. Estimation of parameters is revisited in two-parameter exponential distributions. A comparison study between the maximum likelihood method, the unbiased estimates which are linear functions of the maximum likelihood method, the method of product spacings, and the method of quantile estimates are presented. Finally, a simulation study is given to demonstrate the small sample properties     

A bayesian life test acceptance sampling plan by variables with cost considerations

This paper presents an economic life test acceptance sampling plan using item-censored data in a Bayesian situation. It is assumed that failures in a life test are replaced immediately by new ones. A prior distribution is assigned to the mean lifetime θ for the calculation of the expected total cost. Then the optimum plan is chosen to be the one which minimizes the expected total cost. A direct search method and a dual programming method are introduced, with emphasis on the latter. A numerical example is presented to illustrate the procedure. A sensitivity study is included on the effect of a wrong choice of the prior distribution.     

A Partial Likelihood Estimator of Vaccine Efficacy

A partial likelihood method is proposed for estimating vaccine efficacy for a general epidemic model. In contrast to the maximum likelihood estimator (MLE) which requires complete observation of the epidemic, the suggested method only requires information on the sequence in which individuals are infected and not the exact infection times. A simulation study shows that the method performs almost as well as the MLE. The method is applied to data on the infectious disease mumps.     

Efficient generation of random variates via the ratio-of-uniforms method

Improvements to the conventional ratio-of-uniforms method for random variate generation are proposed. A generalized radio-of-uniforms method is introduced, and it is demonstrated that relocation of the required density via the mode can greatly improve the computational efficiency of the method. We describe a multivariate version of the basic method and summarize a general strategy for efficient ratio-of-uniforms generation. Illustrative examples are given.     

Small sample efficiencies of tests based on the method of n rankings

A method is proposed for calculating the small sample powers of rank tests which are based on the method of n rankings. A class of normal shift alternative hypotheses is considered, and Hodges–Lehmann efficiencies are calculated for the Friedman test.     

Bayesian semiparametric inference for the accelerated failure-time model

Bayesian semiparametric inference is considered for a loglinear model. This model consists of a parametric component for the regression coefficients and a nonparametric component for the unknown error distribution. Bayesian analysis is studied for the case of a parametric prior on the regression coefficients and a mixture-of-Dirichlet-processes prior on the unknown error distribution. A Markov-chain Monte Carlo (MCMC) method is developed to compute the features of the posterior distribution. A model selection method for obtaining a more parsimonious set of predictors is studied. The method adds indicator variables to the regression equation. The set of indicator variables represents all the possible subsets to be considered. A MCMC method is developed to search stochastically for the best subset. These procedures are applied to two examples, one with censored data.     

Generalized elliptical distributions

A family of distributions generated by an operator acting on generalized normal density is introduced. This family contains as particular cases many known distributions, including the generalized normal, generalized t, and generalized gamma distributions. Several mathematical properties of the family (including expansions, characteristic function, moments, cumulants, and order statistics properties) are derived. Estimation procedures are derived too by the method of moments, method of maximum likelihood, and the method of empirical characteristic function. A real data application is presented. Finally, extensions to the multivariate case are outlined.     

Bayesian analysis of outlier problems using divergence measures

A Bayesian approach is presented for detecting influential observations using general divergence measures on the posterior distributions. A sampling-based approach using a Gibbs or Metropolis-within-Gibbs method is used to compute the posterior divergence measures. Four specific measures are proposed, which convey the effects of a single observation or covariate on the posterior. The technique is applied to a generalized linear model with binary response data, an overdispersed model and a nonlinear model. An asymptotic approximation using Laplace method to obtain the posterior divergence is also briefly discussed.     

Properties of a fourier bootstrap method for time series

A method for bootstrapping stationary Gaussian sequences is studied. The FFT is applied to the original data, randomized in the frequency domain, and the inverse FFT is applied. The result is a sequence whose second order properties are similar to those of the original sequence. Conditions under which the method is valid are given.     

Design-based confidence limits for a distribution function

A method for constructing confidence limits for a distribution function is proposed. This method is a simple modification of the common method based on a normal approximation to the distribution of the estimated distribution function. The methods differ in how the estimated standard errors are used. The coverage properties of the two methods are compared in a simulation study. Coverage probabilities for the proposed method are found to be much closer to the nominal levels, particularly in the tails of the population distribution.     

Multiple imputation of missing data with ante-dependence covariance structure

A controlled clinical trial was conducted to investigate the efficacy effect of a chemical compound in the treatment of Premenstrual Dysphoric Disorder (PMDD). The data from the trial showed a non-monotone pattern of missing data and an ante-dependence covariance structure. A new analytical method for imputing the missing data with the ante-dependence covariance is proposed. The PMDD data are analysed by the non-imputation method and two imputation methods: the proposed method and the MCMC method.     

Minimum-distance methods based on quadratic distances for transforms

A class of minimum-distance methods based on empirical transforms is considered. This class includes the minimum-chi-squared method, the K-L method for empirical characteristic functions, and the analogous method for empirical moment generating functions. Asymptotic properties of the minimum-distance estimators and goodness-of-fit test statistics are derived. A general analogue of the Rao-Robson statistic is formulated.     

Bayesian Estimation and Model Choice in Item Response Models

Item response models are essential tools for analyzing results from many educational and psychological tests. Such models are used to quantify the probability of correct response as a function of unobserved examinee ability and other parameters explaining the difficulty and the discriminatory power of the questions in the test. Some of these models also incorporate a threshold parameter for the probability of the correct response to account for the effect of guessing the correct answer in multiple choice type tests. In this article we consider fitting of such models using the Gibbs sampler. A data augmentation method to analyze a normal-ogive model incorporating a threshold guessing parameter is introduced and compared with a Metropolis-Hastings sampling method. The proposed method is an order of magnitude more efficient than the existing method. Another objective of this paper is to develop Bayesian model choice techniques for model discrimination. A predictive approach based on a variant of the Bayes factor is used and compared with another decision theoretic method which minimizes an expected loss function on the predictive space. A classical model choice technique based on a modified likelihood ratio test statistic is shown as one component of the second criterion. As a consequence the Bayesian methods proposed in this paper are contrasted with the classical approach based on the likelihood ratio test. Several examples are given to illustrate the methods.     

On the use of the scholz-sievers method for dlstribution-free inference about the difference in slopes of two regression lines

A simple adaptation of a distribution-free method due to Scholz (1978) and Sievers (1978) for inference in a single regression setting is proposed for inference about the difference in slopes of two regression lines. We assume that the data are obtained from a designed experiment with common regression constants. A comparison of the proposed method to its competitors-one due to Hollander and the other due to Rao and Gore-indicates superiority of the new method.     

Double samble tests— a distribution free procedure

A method for constructing two-stage (double samble) tests is presented which does not require the evaluation of complicated bivariate distribution function. The procedure results from a modification of Fisher's method for combining independent tests of significance and is distribution free in the way it combines the test results from the two sampies. However, the one sample test statistics for the two samples are assumed to have continuous distributions and may be parametric. A rule is also given or the selection of a particular test out of a family of possible two-stage tests which can be generated by this method. Specific examples are given and comparisons are made with two double sample tests which have previously been presented in the literature.     

Likelihood-based cross-validation score for selecting the smoothing parameter in maximum penalized likelihood estimation

Maximum penalized likelihood estimation is applied in non(semi)-para-metric regression problems, and enables us exploratory identification and diagnostics of nonlinear regression relationships. The smoothing parameter A controls trade-off between the smoothness and the goodness-of-fit of a function. The method of cross-validation is used for selecting A, but the generalized cross-validation, which is based on the squared error criterion, shows bad be¬havior in non-normal distribution and can not often select reasonable A. The purpose of this study is to propose a method which gives more suitable A and to evaluate the performance of it.

A method of simple calculation for the delete-one estimates in the likeli¬hood-based cross-validation (LCV) score is described. A score of similar form to the Akaike information criterion (AIC) is also derived. The proposed scores are compared with the ones of standard procedures by using data sets in liter¬atures. Simulations are performed to compare the patterns of selecting A and overall goodness-of-fit and to evaluate the effects of some factors. The LCV-scores by the simple calculation provide good approximation to the exact one if λ is not extremeiy smaii Furthermore the LCV scores by the simple size it possible to select X adaptively They have the effect, of reducing the bias of estimates and provide better performance in the sense of overall goodness-of fit. These scores are useful especially in the case of small sample size and in the case of binary logistic regression.     

An estimation in a linear model with response-related error

A maximum likelihood solution is presented for analyzing data which arise from a linear model whose error term is assumed to have variance proportional to some unknown power of the response. An efficient iterative method for solving the likelihood equations is obtained which incoporates use of a transfomation to orthogonalize the two variance paramaters. Assessments of the method are made through simulations study and the results are compared with those of the ordinary least squares. Examples from the literature are included to illustrate the method and also to compare the results with a weighted least squares estimates.