On the Critical Value Behaviour of Multiple Decision Procedures

In this paper we investigate the asymptotic critical value behaviour of certain multiple decision procedures as e.g. simultaneous confidence intervals and simultaneous as well as stepwise multiple test procedures. Supposing that n hypotheses or parameters of interest are under consideration we investigate the critical value behaviour when n increases. More specifically, we answer e.g. the question by which amount the lengths of confidence intervals increase when an additional parameter is added to the statistical analysis. Furthermore, critical values of different multiple decision procedures as for instance step-down and step-up procedures will be compared. Some general theoretic results are derived and applied for various distributions.     

TWO-STAGE PROCEDURES FOR TESTING HOMOGENEITY OF MEANS AGAINST ORDERED ALTERNATIVES IN ONE-WAY ANOVA WITH COMMON UNKNOWN VARIANCE

In this paper we present two-stage procedures for testing the equality of normal means against ordered alternatives in one-way ANOVA with common and unknown variance. The procedures proposed are analogous to Williams's and Bartholomew's single-stage methods. A table of percentiles needed for implementation is provided. Some Monte Carlo results for estimating the power of both procedures are given.     

Fitting E(max) models to clinical trial dose-response data

We consider fitting Emax models to the primary endpoint for a parallel group dose–response clinical trial. Such models can be difficult to fit using Maximum Likelihood if the data give little information about the maximum possible response. Consequently, we consider alternative models that can be derived as limiting cases, which can usually be fitted. Furthermore we propose two model selection procedures for choosing between the different models. These model selection procedures are compared with two model selection procedures which have previously been used. In a simulation study we find that the model selection procedure that performs best depends on the underlying true situation. One of the new model selection procedures gives what may be regarded as the most robust of the procedures.     

Tests for a simple tree order restriction with application to dose–response studies

Summary.  We propose 'Dunnett-type' test procedures to test for simple tree order restrictions on the means of p independent normal populations. The new tests are based on the estimation procedures that were introduced by Hwang and Peddada and later by Dunbar, Conaway and Peddada. The procedures proposed are also extended to test for 'two-sided' simple tree order restrictions. For non-normal data, nonparametric versions based on ranked data are also suggested. Using computer simulations, we compare the proposed test procedures with some existing test procedures in terms of size and power. Our simulation study suggests that the procedures compete well with the existing procedures for both one-sided and two-sided simple tree alternatives. In some instances, especially in the case of two-sided alternatives or for non-normally distributed data, the gains in power due to the procedures proposed can be substantial.     

Selecting the best normal population better than a standard under unequal variances

In this article we consider a problem of selecting the best normal population that is better than a standard when the variances are unequal. Single-stage selection procedures are proposed when the variances are known. Wilcox (1984) and Taneja and Dudewicz (1992) proposed two-stage selection procedures when the variances are unknown. In addition to these procedures, we propose a two-stage selection procedure based on the method of Lam (1988). Comparisons are made between these selection procedures in terms of the sample sizes.     

Consistent variable selection via the optimal discovery procedure in multiple testing

In this paper, we translate variable selection for linear regression into multiple testing, and select significant variables according to testing result. New variable selection procedures are proposed based on the optimal discovery procedure (ODP) in multiple testing. Due to ODP’s optimality, if we guarantee the number of significant variables included, it will include less non significant variables than marginal p-value based methods. Consistency of our procedures is obtained in theory and simulation. Simulation results suggest that procedures based on multiple testing have improvement over procedures based on selection criteria, and our new procedures have better performance than marginal p-value based procedures.     

Asymptotics of Selective Inference

In this paper, we seek to establish asymptotic results for selective inference procedures removing the assumption of Gaussianity. The class of selection procedures we consider are determined by affine inequalities, which we refer to as affine selection procedures. Examples of affine selection procedures include selective inference along the solution path of the least absolute shrinkage and selection operator (LASSO), as well as selective inference after fitting the least absolute shrinkage and selection operator at a fixed value of the regularization parameter. We also consider some tests in penalized generalized linear models. Our result proves asymptotic convergence in the high‐dimensional setting where n<p, and n can be of a logarithmic factor of the dimension p for some procedures.     

Closed testing procedures for all pairwise comparisons in a randomized block design

We consider multiple comparison test procedures among treatment effects in a randomized block design. We propose closed testing procedures based on maximum values of some two-sample t test statistics and based on F test statistics. It is shown that the proposed procedures are more powerful than single-step procedures and the REGW (Ryan/Einot–Gabriel/Welsch)-type tests. Next, we consider the randomized block design under simple ordered restrictions of treatment effects. We propose closed testing procedures based on maximum values of two-sample one-sided t test statistics and based on Batholomew’s statistics for all pairwise comparisons of treatment effects. Although single-step multiple comparison procedures are utilized in general, the power of these procedures is low for a large number of groups. The closed testing procedures stated in the present article are more powerful than the single-step procedures. Simulation studies are performed under the null hypothesis and some alternative hypotheses. In this studies, the proposed procedures show a good performance.     

Controlling the familywise error rate with plug-in estimator for the proportion of true null hypotheses

Summary.  Estimation of the number or proportion of true null hypotheses in multiple-testing problems has become an interesting area of research. The first important work in this field was performed by Schweder and Spjøtvoll. Among others, they proposed to use plug-in estimates for the proportion of true null hypotheses in multiple-test procedures to improve the power. We investigate the problem of controlling the familywise error rate FWER when such estimators are used as plug-in estimators in single-step or step-down multiple-test procedures. First we investigate the case of independent p -values under the null hypotheses and show that a suitable choice of plug-in estimates leads to control of FWER in single-step procedures. We also investigate the power and study the asymptotic behaviour of the number of false rejections. Although step-down procedures are more difficult to handle we briefly consider a possible solution to this problem. Anyhow, plug-in step-down procedures are not recommended here. For dependent p -values we derive a condition for asymptotic control of FWER and provide some simulations with respect to FWER and power for various models and hypotheses.     

Multiple comparison procedures for finding non-maximum normal means

Abstract

In this study, we discuss multiple comparison procedures for finding normal means which are not maximum among several normal means. Specifically, we propose the single step procedure, the sequentially rejective step down procedure and the step up procedure. For the single step procedure we determine the critical value for a specified significance level. For the sequentially rejective step down procedure and the step up procedure we determine the critical value at each step of the test for a specified significance level. For three procedures we formulate the power of the test under a specified alternative hypothesis. We give some numerical examples regarding critical values and power of the test intended to compare three procedures.     

Selection of a better component in bivariate exponential models

Summary Selection procedures of the better component in bivariate exponential (BVE) models are proposed. In this paper, we consider onlyBVE models proposed by Freund (1961) Marshall-Olkin (1967) and Block-Basu (1974). The probabilities of correct selection for the proposed procedures are compared by using the normal approximations. A numerical study on the determination of asymptotic relative efficiency (ARE) of the proposed procedures are presented.     

Variable selection in linear measurement error models via penalized score functions

We propose variable selection procedures based on penalized score functions derived for linear measurement error models. To calibrate the selection procedures, we define new tuning parameter selectors based on the scores. Large-sample properties of these new tuning parameter selectors are established for the proposed procedures. These new methods are compared in simulations and a real-data application with competing methods where one ignores measurement error or uses the Bayesian information criterion to choose the tuning parameter.     

Robustness of Parameter Estimation Procedures in Multilevel Models When Random Effects are MEP Distributed

In this paper we examine maximum likelihood estimation procedures in multilevel models for two level nesting structures. Usually, for fixed effects and variance components estimation, level-one error terms and random effects are assumed to be normally distributed. Nevertheless, in some circumstances this assumption might not be realistic, especially as concerns random effects. Thus we assume for random effects the family of multivariate exponential power distributions (MEP); subsequently, by means of Monte Carlo simulation procedures, we study robustness of maximum likelihood estimators under normal assumption when, actually, random effects are MEP distributed.     

Generalised Rank Regression Estimator with Standard Error Adjusted Lasso

One of the standard variable selection procedures in multiple linear regression is to use a penalisation technique in least‐squares (LS) analysis. In this setting, many different types of penalties have been introduced to achieve variable selection. It is well known that LS analysis is sensitive to outliers, and consequently outliers can present serious problems for the classical variable selection procedures. Since rank‐based procedures have desirable robustness properties compared to LS procedures, we propose a rank‐based adaptive lasso‐type penalised regression estimator and a corresponding variable selection procedure for linear regression models. The proposed estimator and variable selection procedure are robust against outliers in both response and predictor space. Furthermore, since rank regression can yield unstable estimators in the presence of multicollinearity, in order to provide inference that is robust against multicollinearity, we adjust the penalty term in the adaptive lasso function by incorporating the standard errors of the rank estimator. The theoretical properties of the proposed procedures are established and their performances are investigated by means of simulations. Finally, the estimator and variable selection procedure are applied to the Plasma Beta‐Carotene Level data set.     

CONDITIONAL INFERENCE PROCEDURES FOR THE PARETO AND POWER FUNCTION DISTRIBUTIONS BASED ON TYPE-II RIGHT CENSORED SAMPLES

In this paper we consider conditional inference procedures for the Pareto and power function distributions. We develop procedures for obtaining confidence intervals for the location and scale parameters as well as upper and lower n probability tolerance intervals for a proportion g, given a Type-II right censored sample from the corresponding distribution. The intervals are exact, and are obtained by conditioning on the observed values of the ancillary statistics. Since, for each distribution, the procedures assume that a shape parameter x is known, a sensitivity analysis is also carried out to see how the procedures are affected by changes in x.     

Asymptotic comparison of the critical values of step-down and step-up multiple comparison procedures

We consider the problem of comparing step-down and step-up multiple test procedures for testing n hypotheses when independent p-values or independent test statistics are available. The defining critical values of these procedures for independent test statistics are asymptotically equal, which yields a theoretical argument for the numerical observation that the step-up procedure is mostly more powerful than the step-down procedure. The main aim of this paper is to quantify the differences between the critical values more precisely. As a by-product we also obtain more information about the gain when we consider two subsequent steps of these procedures. Moreover, we investigate how liberal the step-up procedure becomes when the step-up critical values are replaced by their step-down counterparts or by more refined approximate values. The results for independent p-values are the basis for obtaining corresponding results when independent real-valued test statistics are at hand. It turns out that the differences of step-down and step-up critical values as well as the differences between subsequent steps tend to zero for many distributions, except for heavy-tailed distributions. The Cauchy distribution yields an example where the critical values of both procedures are nearly linearly increasing in n.     

Control charts for multivariate spatial autoregressive models

This paper deals with spatial detection of changes in model parameters of spatial autoregressive processes. The respective sequential testing problems are formulated. Moreover, we introduce characteristic quantities to monitor means or covariances of multivariate spatial autoregressive processes. Additionally, we also take into account the simultaneous surveillance of the mean vector and the covariance matrix. The aim is to apply control charts, important tools of sequential analysis, to these quantities. The considered control procedures are based on either cumulative sums or exponential smoothing. Further, we illustrate the methodology of statistical process control studying the spectrum of additive colors in a satellite photograph. Via simulation studies, the proposed control procedures are calibrated for a predefined average run length. In addition, we compare the performance of the control procedures considering the out-of-control situation. Eventually, the control charts are applied, and the signals of the different schemes are visualized. The final results are critically discussed.     

A note on information loss in analyzing a mixture model of count data

In this note we consider estimation of a mixture model of count data which is composed of two discrete random variables. Conditional and unconditional estimation procedures are given for estimating the unknown parameter(s) of interest using the likelihood function. Asymptotic relative efficiencies are given to examine the amount of information loss in using the two estimation procedures. Specifically, we study the change in asymptotic relative efficiency, if any, in different parameter settings.     

Efficient ways to impute incomplete panel data

We find that existing multiple imputation procedures that are currently implemented in major statistical packages and that are available to the wide majority of data analysts are limited with regard to handling incomplete panel data. We review various missing data methods that we deem useful for the analysis of incomplete panel data and discuss, how some of the shortcomings of existing procedures can be overcome. In a simulation study based on real panel data, we illustrate these procedures’ quality and outline fruitful avenues of future research.     

Bayesian approach to robust procedures based on censored samples

By adopting; the Bayesian method, we develop in this paper some robust procedures for the one and two-sample location problems based on symmetric Type-II censored samples and by assuming normality for the censored samples. The posterior distributions and the Highest Posterior Density (H.P.D.) intervals are? derived. Finally, we illustrate these procedures by applying the results to Darwin's data and to Brown lee's data