Global and multiple test procedures using ordered p-values—a review

This paper reviews global and multiple tests for the combination ofn hypotheses using the orderedp-values of then individual tests. In 1987, Röhmel and Streitberg presented a general method to construct global level α tests based on orderedp-values when there exists no prior knowledge regarding the joint distribution of the corresponding test statistics. In the case of independent test statistics, construction of global tests is available by means of recursive formulae presented by Bicher (1989), Kornatz (1994) and Finner and Roters (1994). Multiple test procedures can be developed by applying the closed test principle using these global tests as building blocks. Liu (1996) proposed representing closed tests by means of “critical matrices” which contain the critical values of the global tests. Within the framework of these theoretical concepts, well-known global tests and multiple test procedures are classified and the relationships between the different tests are characterised.     

Heteroskedasticity-consistent covariance matrix estimation:white's estimator and the bootstrap ∗

This paper considers the issue of estimating the covariance matrix of ordinary least squares estimates in a linear regression model when heteroskedasticity is suspected. We perform Monte Carlo simulation on the White estimator, which is commonly used in.

empirical research, and also on some alternatives based on different bootstrapping schemes. Our results reveal that the White estimator can be considerably biased when the sample size is not very large, that bias correction via bootstrap does not work well, and that the weighted bootstrap estimators tend to display smaller biases than the White estimator and its variants, under both homoskedasticity and heteroskedasticity. Our results also reveal that the presence of (potentially) influential observations in the design matrix plays an important role in the finite-sample performance of the heteroskedasticity-consistent estimators.     

A comparison of the Hosmer–Lemeshow,Pigeon–Heyse,and Tsiatis goodness-of-fit tests for binary logistic regression under two grouping methods

Algebraic relationships between Hosmer–Lemeshow (HL), Pigeon–Heyse (J²), and Tsiatis (T) goodness-of-fit statistics for binary logistic regression models with continuous covariates were investigated, and their distributional properties and performances studied using simulations. Groups were formed under deciles-of-risk (DOR) and partition-covariate-space (PCS) methods. Under DOR, HL and T followed reported null distributions, while J² did not. Under PCS, only T followed its reported null distribution, with HL and J² dependent on model covariate number and partitioning. Generally, all had similar power. Of the three, T performed best, maintaining Type-I error rates and having a distribution invariant to covariate characteristics, number, and partitioning.     

Three-stage and ‘accelerated’ sequential procedures for the mean of a normal population with known coefficient of variation

Three-stage and ‘accelerated’ sequential procedures are developed for estimating the mean of a normal population when the population coefficient of variation (CV) is known. In spite of the usual estimator, i.e. the sample mean, Searls' (1964 Searls, DT. (1964). The utilization of a known coefficient of variation in the estimation procedure. J. Amer. Statist. Assoc, 50: 1225–1226.  ) estimator is utilized for the estimation purpose. It is established that Searls' estimator dominates the sample mean under the two sampling schemes.     

No Satisfaction? But We Try,and We Try,and We Try!

ABSTRACT

When Old Dominion University Libraries conducted the LibQUAL+ survey in 2015, results indicated a lack of satisfaction in effectively discovering and using our electronic resources. This article is based on a presentation from the 2017 North Carolina Serials Conference describing how Old Dominion University Libraries used data from the LibQUAL+ survey results to help our users connect more effectively with information resources, resulting in an improved user experience and an increased level of satisfaction.     

Lévy Processes,Phase-Type Distributions,and Martingales

Lévy processes are defined as processes with stationary independent increments and have become increasingly popular as models in queueing, finance, etc.; apart from Brownian motion and compound Poisson processes, some popular examples are stable processes, variance gamma processes, CGMY Lévy processes (tempered stable processes), NIG (normal inverse Gaussian) Lévy processes, and hyperbolic Lévy processes. We consider here a dense class of Lévy processes, compound Poisson processes with phase-type jumps in both directions and an added Brownian component. Within this class, we survey how to explicitly compute a number of quantities that are traditionally studied in the area of Lévy processes, in particular two-sided exit probabilities and associated Laplace transforms, the closely related scale function, one-sided exit probabilities and associated Laplace transforms coming up in queueing problems, and similar quantities for a Lévy process with reflection in 0. The solutions are in terms of roots to polynomials, and the basic equations are derived by purely probabilistic arguments using martingale optional stopping; a particularly useful martingale is the so-called Kella-Whitt martingale. Also, the relation to fluid models with a Brownian component is discussed.     

Robustness and power of parametric, nonparametric, robustified and adaptive tests—The multi-sample location problem

This paper deals with a survey of different types of tests, parametric, nonparametric, robustified and adaptive ones, and with an application to the two-sided c-sample location problem. Some concepts of robustness are discussed, such as breakdown point, influence function, gross-error sensitivity and especially α- and β-robustness. A robustness study on level α in the case of heteroscedasticity and nonnormal distributions is carried out via Monte Carlo methods and also a power comparison of all the tests considered. It turns out that robustified versions of the F-test and Welch-test where the original observations are replaced by its ranks behave well over a broad class of distributions, symmetric ones with different tail weight and asymmetric ones, but, on the whole, an adaptive test is to prefer.     

Cultural Anthropology and Open Access—Interview With Dominic Boyer,Cymene Howe,and Marcel LaFlamme

Abstract

In this installment of “Conversations,” Dominic Boyer, Cymene Howe, and Marcel LaFlamme, of the Cultural Anthropology editorial team, discuss the ins and outs of publishing a gold open access journal in the field of anthropology. Highlights of the discussion include Cultural Anthropology’s move to open access, the ethics of open access, the growth of international readership, ongoing publishing challenges in open access, and the role libraries can play with open access publishing.     

Mixed models and unbalanced data: wherefrom,whereat and whereto?

A brief history of the early years (1820-1947) of random effects models and the estimation of variance components is followed by a personal evaluation of M.L, REML and MINQUE estimation. A method is suggested for combining ML estimator obtained from subsets of a large data set, and comments are made on the need for simulation studies to assess the degree of approximation in using asymptotic properties of ML-type estimators as if they were exact for finite-sized unbalanced data sets.     

Median split, k-group split, and optimality in continuous populations

Continuous populations are grouped in many social, economic, medical, or technical fields of research. However, by grouping them, a lot of information provided by the continuous population is lost. Especially the median split, which is still adopted by many researchers, and its generalization to an equiprobable k-group split lead to a high efficiency loss. Here, this loss of information is investigated by analytical and numerical analyses for some typical symmetric and skew population distributions often found in applications. Various distribution parameters, numbers of groups, and split methods are taken from theoretical considerations and real data sets. Losses sometimes in excess of 50% can be reduced by optimal grouping.     

An extended Birnbaum–Saunders distribution: Theory,estimation, and applications

Abstract

Birnbaum and Saunders (1969a Birnbaum, Z.W., Saunders, S.C. (1969a). A new family of life distributions. J. Appl. Probab. 6:319–327.[Crossref], [Web of Science ®] , [Google Scholar]) pioneered a lifetime model which is commonly used in reliability studies. Based on this distribution, a new model called the gamma Birnbaum–Saunders distribution is proposed for describing fatigue life data. Several properties of the new distribution including explicit expressions for the ordinary and incomplete moments, generating and quantile functions, mean deviations, density function of the order statistics, and their moments are derived. We discuss the method of maximum likelihood and a Bayesian approach to estimate the model parameters. The superiority of the new model is illustrated by means of three failure real data sets. We also propose a new extended regression model based on the logarithm of the new distribution. The last model can be very useful to the analysis of real data and provide more realistic fits than other special regression models.     

Observational studies,clinical trials,and the women’s health initiative

The complementary roles fulfilled by observational studies and randomized controlled trials in the population science research agenda is illustrated using results from the Women’s Health Initiative (WHI). Comparative and joint analyses of clinical trial and observational study data can enhance observational study design and analysis choices, and can augment randomized trial implications. These concepts are described in the context of findings from the WHI randomized trials of postmenopausal hormone therapy and of a low-fat dietary pattern, especially in relation to coronary heart disease, stroke, and breast cancer. The role of biomarkers of exposure and outcome, including high-dimensional genomic and proteomic biomarkers, in the elucidation of disease associations, will also be discussed in these same contexts.     

Analysis of clustered spatially correlated binary data using autologistic model and Bayesian method with an application to dental caries of 3–5-year-old children

The autologistic model, first introduced by Besag, is a popular tool for analyzing binary data in spatial lattices. However, no investigation was found to consider modeling of binary data clustered in uncorrelated lattices. Owing to spatial dependency of responses, the exact likelihood estimation of parameters is not possible. For circumventing this difficulty, many studies have been designed to approximate the likelihood and the related partition function of the model. So, the traditional and Bayesian estimation methods based on the likelihood function are often time-consuming and require heavy computations and recursive techniques. Some investigators have introduced and implemented data augmentation and latent variable model to reduce computational complications in parameter estimation. In this work, the spatially correlated binary data distributed in uncorrelated lattices were modeled using autologistic regression, a Bayesian inference was developed with contribution of data augmentation and the proposed models were applied to caries experiences of deciduous dents.