Goodness‐of‐fit tests for parametric models based on biased samples

The authors study the problem of checking the adequacy of a parametric model for a distribution using several possibly censored weight biased samples. They discuss identifiability problems related to the underlying distribution and the distributions of the biased samples. They propose a test statistic based on the supremum of the weighted aggregated martingale residual processes from a number of such samples. Both numerical and graphical procedures are discussed, which the authors apply to do model checking for oil exploration drilling data.     

How Co-operative is Co-operative Purchasing in Smaller Firms?: Evidence from UK Engineering SMEs

Most SMEs do not try to engage in co-operative purchasing arrangements, and even those that do don’t handle them very well. Examining a sample of SME engineering firms from the UK Midlands for signs of more advanced practice, the authors identify three types of behaviour: firms with deliberate strategies (where co-operative purchasing is a consciously-designed and long-term part of management policy); firms with close ties with their suppliers but which still operate fundamentally adversarial policies (where, despite some development of practice, defensive, short-term and ‘low-trust’ attitudes still predominate) and those whose strategies, maybe of long standing, ‘just happened’ and are described as still emergent. The article analyses these positions to yield lessons for managers, recognising that, as firms grow, emergent strategies will need replacing with something more codified.     

Bayesian model selection for join point regression with application to age-adjusted cancer rates

Summary.  The method of Bayesian model selection for join point regression models is developed. Given a set of K +1 join point models M ₀,  M ₁, …,  M _K with 0, 1, …,  K join points respec-tively, the posterior distributions of the parameters and competing models M _k are computed by Markov chain Monte Carlo simulations. The Bayes information criterion BIC is used to select the model M _k with the smallest value of BIC as the best model. Another approach based on the Bayes factor selects the model M _k with the largest posterior probability as the best model when the prior distribution of M _k is discrete uniform. Both methods are applied to analyse the observed US cancer incidence rates for some selected cancer sites. The graphs of the join point models fitted to the data are produced by using the methods proposed and compared with the method of Kim and co-workers that is based on a series of permutation tests. The analyses show that the Bayes factor is sensitive to the prior specification of the variance σ ², and that the model which is selected by BIC fits the data as well as the model that is selected by the permutation test and has the advantage of producing the posterior distribution for the join points. The Bayesian join point model and model selection method that are presented here will be integrated in the National Cancer Institute's join point software ( http://www.srab.cancer.gov/joinpoint/ ) and will be available to the public.     

Estimation of a bivariate distribution function from incomplete observations

Kaplan and Meier (1958) give a maximum likelihood estimator of the distribution function based on a univariate right censored sample-Here we investigate the extension of their results to the case of bivariate right censored samples. Following Efron (1967), we provide "self-consistent" estimators for the bivariate distribution function.     

A unified approach to estimating parameters in some generalized poission distributions

A family of distributions labelled as Poisson v Katz is formulated, which includes, as particular or limiting cases, the Negative Binomial, Neyman Type A, Poisson v Pascal, and Poisson v Binomial. Thus, while analyzing data, estimating the parameters in the Poisson v Katz family obviates the necessity of having to choose from among the particular or limiting cases. In this article minimum chi-square estimators are presented and their asymptotic relative efficiency obtained. An example is presented to illustrate the procedure     

Bayes estimators of the average failure rates

In the present paper we have proposed a Bayesian approach for making inferences from accelerated life tests which do not require distributional assumptions     

Inflated modified power series distributions with applications

In certain applications involving discrete data, it is sometimes found that X = 0 is observed with a frequency significantly higher than predicted by the assumed model. Zero inflated Poisson, binomial and negative binomial models have been employed in some clinical trials and in some regression analysis problems.

In this paper, we study the zero inflated modified power series distributions (IMPSD) which include among others the generalized Poisson and the generalized negative binomial distributions and hence the Poisson, binomial and negative binomial distributions. The structural properties along with the distribution of the sum of independent IMPSD variables are studied. The maximum likelihood estimation of the parameters of the model is examined and the variance-covariance matrix of the estimators is obtained. Finally, examples are presented for the generalized Poisson distribution to illustrate the results.     

Parameter estimation of nonlinear mixed-effects models using first-order conditional linearization and the EM algorithm

Nonlinear mixed-effects (NLME) models are flexible enough to handle repeated-measures data from various disciplines. In this article, we propose both maximum-likelihood and restricted maximum-likelihood estimations of NLME models using first-order conditional expansion (FOCE) and the expectation–maximization (EM) algorithm. The FOCE-EM algorithm implemented in the ForStat procedure SNLME is compared with the Lindstrom and Bates (LB) algorithm implemented in both the SAS macro NLINMIX and the S-Plus/R function nlme in terms of computational efficiency and statistical properties. Two realworld data sets an orange tree data set and a Chinese fir (Cunninghamia lanceolata) data set, and a simulated data set were used for evaluation. FOCE-EM converged for all mixed models derived from the base model in the two realworld cases, while LB did not, especially for the models in which random effects are simultaneously considered in several parameters to account for between-subject variation. However, both algorithms had identical estimated parameters and fit statistics for the converged models. We therefore recommend using FOCE-EM in NLME models, particularly when convergence is a concern in model selection.     

The role of human and social board capital in driving CSR reporting

The objective of this paper is to analyze the effect of the professional, technical and relational background (human and social capital) of outside directors on promoting firm CSR disclosure. Following the Hillman et al. (2000) taxonomy of board members, we classify outside directors as business experts, support specialists and community influential, and examine whether business and technical expertise or political ties in the boardroom affect CSR disclosure.This study confirms that not all outside directors are equally effective in improving CSR disclosure and that only certain kinds of outside directors, those classified as support specialists, help promote it. On the other hand, our findings also show that directors with previous experience as politicians affect CSR disclosure negatively, probably due to their interests in safeguarding their reputation within the company, in avoiding public scrutiny and in protecting their political connections. In addition, our set of analysis with interaction effects reveals that powerful CEOs have the incentive to promote CSR-related strategies and to convince business experts and support specialist directors to enhance profitable sustainability strategies and transparency in CSR disclosure. Nevertheless, the powerful CEO effect is not enough to compensate the negative role of political directors on CSR reporting. Therefore, this paper supports the theories in favor of analyzing the multiple configurations of corporate governance mechanisms by adopting a holistic approach, and the need to combine these configurations in order to analyze their impact on CSR behavior.     

Are college students health literate?

Abstract

Objective: To examine the health literacy of college students. Participants: A convenience sample of 245 graduate and undergraduate college students. Methods: During February–April of 2018 participants completed the Short Test of Functional Health Literacy which assessed literacy on two passages describing a thyroid scan, and basic healthcare insurance information. Results: Most college students displayed adequate health literacy (99.2%). The ANOVA analyses revealed college classification was the most significant predictor, followed by ethnicity, sex, and primary language. Age was significantly related to health literacy, when holding college classification constant. Interestingly, college major, healthcare work experience, or having health care credentials were not predictors of health literacy. Conclusion: This group of college students had adequate health literacy skills. However, the results of this study revealed demographic disparities that suggest further study.