Multiple comparisons of several populations with more than one control with respectto scale parameter

In this paper, one-sided and two-sided test procedures for comparing several treatments with more than one control with respect to scale parameter are proposed. The proposed test procedures are inverted to obtain the associated simultaneous confidence intervals. The multiple comparisons of test treatments with the best control are also developed. The computation of the critical points, required to implement the proposed procedures, is discussed by taking the normal probability model. Applications of the proposed test procedures to two-parameter exponential probability model are also demonstrated.     

Comparing several treatments with a control

Consider the partially balanced one-way layout for comparing k   treatments _μi,${\mu }_i,$1?i?k,$1?i?k,$ with a control _μ0${\mu }_0$. We propose a new test which is similar to the test statistics of Marcus [1976. The powers of some tests of the equality of normal means against an ordered alternative. Biometrika 63, 177–183]. By simulation we find that the proposed test has a good power performance when compared with other tests. Moreover, it can produce confidence intervals for _μi-_μ0,1?i?k.${\mu }_i-{\mu }_0,1?i?k.$     

A closed testing procedure for comparing successive exponential populations with respect to location parameter

Consider k independent random samples such that i^th sample is drawn from a two-parameter exponential population with location parameter μ_i and scale parameter θ_i,?i = 1, …, k. For simultaneously testing differences between location parameters of successive exponential populations, closed testing procedures are proposed separately for the following cases (i) when scale parameters are unknown and equal and (ii) when scale parameters are unknown and unequal. Critical constants required for the proposed procedures are obtained numerically and the selected values of the critical constants are tabulated. Simulation study revealed that the proposed procedures has better ability to detect the significant differences and has more power in comparison to exiting procedures. The illustration of the proposed procedures is given using real data.     

A combination multiple comparisons and subset selection procedure to identify treatments that are strictly inferior to the best

This paper considers the problem of identifying which treatments are strictly worse than the best treatment or treatments in a one-way layout, which has many important applications in screening trials for new product development. A procedure is proposed that selects a subset of the treatments containing only treatments that are known to be strictly worse than the best treatment or treatments. In addition, simultaneous confidence intervals are obtained which provide upper bounds on how inferior the treatments are compared with these best treatments. In this way, the new procedure shares the characteristics of both subset selection procedures and multiple comparison procedures. Some tables of critical points are provided for implementing the new procedure, and some examples of its use are given.     

Evaluating the Performance of Simultaneous Stepwise Confidence Intervals for the Difference between two Poisson Rates

We consider the problem of simultaneously estimating Poisson rate differences via applications of the Hsu and Berger stepwise confidence interval method (termed HBM), where comparisons to a common reference group are performed. We discuss continuity-corrected confidence intervals (CIs) and investigate the HBM performance with a moment-based CI, and uncorrected and corrected for continuity Wald and Pooled confidence intervals (CIs). Using simulations, we compare nine individual CIs in terms of coverage probability and the HBM with nine intervals in terms of family-wise error rate (FWER) and overall and local power. The simulations show that these statistical properties depend highly on parameter settings.     

Two-sided Confidence Bands with the Inferential Sensitivity of One-Sided Bands

In this article, we consider investigating whether any of k treatments are better than a control under the assumption of each treatment mean being no less than the control mean. A classic problem is to find the simultaneous confidence bounds for the difference between each treatment and the control. Compared with hypothesis testing, confidence bounds have the attractive advantage of telling more information about the effective treatment. Generally, the one-sided lower bounds are provided as it's enough for detecting effective treatment and the one-sided lower bounds has sharper lower bands than two-sided ones. However, a two-sided procedure provides both upper and lower bounds on the differences. In this article, we develop a new procedure which combines the good aspects of both the one-sided and the two-sided procedures. This new procedure has the same inferential sensitivity of the one-sided procedure proposed by Zhao (2007 Zhao , H. B. ( 2007 ). Comparing several treatments with a control . J. Statist. Plann. Infer. 137 : 2996 – 3006 .[Crossref], [Web of Science ®] , [Google Scholar]) while also providing simultaneous two-sided bounds for the differences between treatments and the control. By our computation results, we find the new procedure is better than Hayter, Miwa and Liu's procedure (Hayter et al., 2000 Hayter , A. J. , Miwa , T. , Liu , W. ( 2000 ). Combining the advantages of one-sided and two-sided procedures for comparing several treatments with a control . J. Statist. Plann. Infer. 86 : 81 – 99 .[Crossref], [Web of Science ®] , [Google Scholar]), when the sample size is balanced. We also illustrate the new procedure by an example.     

A note on successive comparisons between several ordered variances

ABSTRACT

In this article, a procedure for comparisons between k (k ? 3) successive populations with respect to the variance is proposed when it is reasonable to assume that variances satisfy simple ordering. Critical constants required for the implementation of the proposed procedure are computed numerically and selected values of the computed critical constants are tabulated. The proposed procedure for normal distribution is extended for making comparisons between successive exponential populations with respect to scale parameter. A comparison between the proposed procedure and its existing competitor procedures is carried out, using Monte Carlo simulation. Finally, a numerical example is given to illustrate the proposed procedure.     

On Tests Based on Sample Quasi Ranges for Ordered Alternative (Scale Case of Exponential Distribution)

The analysis of categorical response data through the multinomial model is very frequent in many statistical, econometric, and biometric applications. However, one of the main problems is the precise estimation of the model parameters when the number of observations is very low. We propose a new Bayesian estimation approach where the prior distribution is constructed through the transformation of the multivariate beta of Olkin and Liu (2003 Olkin , I. , Liu , R. ( 2003 ). A bivariate beta distribution . Stat. Probab. Lett. 62 : 407 – 412 .[Crossref], [Web of Science ®] , [Google Scholar]). Moreover, the application of the zero-variance principle allows us to estimate moments in Monte Carlo simulations with a dramatic reduction of their variances. We show the advantages of our approach through applications to some toy examples, where we get efficient parameter estimates.     

Bayesian inference for a common scale parameter of several Pareto populations

This article deals with the problem of Bayesian inference concerning the common scale parameter of several Pareto distributions. Bayesian hypothesis testing of, and Bayesian interval estimation for, the common scale parameter is given. Numerical studies including a comparison study, a simulation study, and a practical application study are given in order to illustrate our procedures and to demonstrate the performance, advantages, and merits of the Bayesian procedures over the classical and generalized variable procedures.     

Max-min multiple comparison procedure for comparing several dose levels with a zero dose control

The comparison of increasing doses of a compound to a zero dose control is of interest in medical and toxicological studies. Assume that the mean dose effects are non-decreasing among the non-zero doses of the compound. A simple procedure that modifies Dunnett's procedure is proposed to construct simultaneous confidence intervals for pairwise comparisons of each dose group with the zero dose control by utilizing the ordering of the means. The simultaneous lower bounds and upper bounds by the new procedure are monotone, which is not the case with Dunnett's procedure. This is useful to categorize dose levels. The expected gains of the new procedure over Dunnett's procedure are studied. The procedure is shown by real data to compare well with its predecessor.     

Multiple comparisons of exponential location parameters with a control based on doubly censored sample under heteroscedasticity

In this article, a one-sample procedure for multiple comparisons of exponential location parameters with a control under heteroscedasticity is proposed. The observations are obtained by doubly censored samples. A one-sided and two-sided confidence intervals are used to perform such multiple comparisons. Statistical tables of critical values and an example of comparing four drugs in treating leukemia are provided.     

A modified multiple comparisons with a control for exponential location parameters based on doubly censored sample under heteroscedasticity

ABSTRACT

In this paper, a modified one-stage multiple comparison procedures with a control for exponential location parameters based on the doubly censored sample under heteroscedasticity is proposed. A simulation study is done and the results show that the proposed procedures have shorter confidence length with coverage probabilities closer to the nominal ones compared with the one proposed in Wu (2017 Wu, S. F. 2017. Multiple comparisons of exponential location parameters with a control based on doubly censored sample under heteroscedasticity. Communications in Statistics: Simulation and Computation 46 (3):1858–1870. doi: 10.1080/03610918.2015.1017582.[Taylor & Francis Online] , [Google Scholar]). At last, an example of comparing the duration of remission for four drugs as the treatment of leukemia is given to demonstrate the proposed procedures.     

Generalized confidence intervals compatible with the Min test for simultaneous comparisons of one subpopulation to several other subpopulations

A problem where one subpopulation is compared with several other subpopulations in terms of means with the goal of estimating the smallest difference between the means commonly arises in biology, medicine, and many other scientific fields. A generalization of Strass-burger-Bretz-Hochberg approach for two comparisons is presented for cases with three and more comparisons. The method allows constructing an interval estimator for the smallest mean difference, which is compatible with the Min test. An application to a fluency-disorder study is illustrated. Simulations confirmed adequate probability coverage for normally distributed outcomes for a number of designs.     

Exact likelihood inference for two populations from two-parameter exponential distributions under joint Type-II censoring

In this article, we develop exact inference for two populations that have a two-parameter exponential distribution with the same location parameter and different scale parameters when Type-II censoring is implemented on the two samples in a combined manner. We obtain the conditional maximum likelihood estimators (MLEs) of the three parameters. We then derive the exact distributions of these MLEs along with their moment generating functions. Based on general entropy loss function, Bayesian study about the parameters is presented. Finally, some simulation results and an illustrative example are presented to illustrate the methods of inference developed here.     

The simultaneous confidence intervals for all distances from the extreme populations for two-parameter exponential populations based on the multiply type II censored samples

Among k independent two-parameter exponential distributions which have the common scale parameter, the lower extreme population (LEP) is the one with the smallest location parameter and the upper extreme population (UEP) is the one with the largest location parameter. Given a multiply type II censored sample from each of these k independent two-parameter exponential distributions, 14 estimators for the unknown location parameters and the common unknown scale parameter are considered. Fourteen simultaneous confidence intervals (SCIs) for all distances from the extreme populations (UEP and LEP) and from the UEP from these k independent exponential distributions under the multiply type II censoring are proposed. The critical values are obtained by the Monte Carlo method. The optimal SCIs among 14 methods are identified based on the criteria of minimum confidence length for various censoring schemes. The subset selection procedures of extreme populations are also proposed and two numerical examples are given for illustration.     

Some Multiple Decision Procedures for Comparing Several Population Proportions

In this article, comparison of several population proportions using multiple decision approach is studied. The probability of the order of the sample proportions matching with the order of the population proportions is being controlled. A related multiple comparison procedure with a control is also discussed. For ranking the proportions in multinomial distribution, the simultaneous confidence interval is constructed and used for the ranking. Some examples are used to illustrate the multiple decision procedures discussed in this paper.     

Multiple comparisons with a control under heteroscedasticity

This article investigates three procedures on the multiple comparisons with a control in the presence of unequal error variances. The advantages of the proposed methods are illustrated through two examples. The performance of the proposed methods and other alternative methods is compared by simulation studies. The results show that the typical methods assuming equal variance will have inflated error rate and may lead to erroneous inference when the equal variance assumption fails. In addition, the simulation study shows that the proposed approaches always control the family-wise error rate at a specified nominal level α, while some established methods are liberal and have inflated error rate in some scenarios.     

Comparing several experimental groups with a control in the multivariate case

This paper presents a multivariate extension of Dunnett's test for comparing simultaneously k treatment group means with a single control group mean. A test based on Hotelling T²statistics is presented and approximate critical values are evaluated for the case of equal numbers of observations in each group, for the .05 and .01 levels of significance, for 1 to 5 variates, for 1 to 10 treatment groups, and for varying degrees of freedom. The accuracy of the procedure for generating approximate critical values is assessed via simulation studies conducted for selected cases and an example is presented using real data.     

Exact inference for exponential distribution with multiply Type-I censored data

In this paper, we focus on exact inference for exponential distribution under multiple Type-I censoring, which is a general form of Type-I censoring and represents that the units are terminated at different times. The maximum likelihood estimate of mean parameter is calculated. Further, the distribution of maximum likelihood estimate is derived and it yields an exact lower confidence limit for the mean parameter. Based on a simulation study, we conclude that the exact limit outperforms the bootstrap limit in terms of the coverage probability and average limit. Finally, a real dataset is analyzed for illustration.